Kerrie Mengersen

Issues in Designing Hybrid Algorithms

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH24

A Python Package for Bayesian Estimation Using Markov Chain Monte Carlo

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH25

Quantification of particle number emission factors for motor vehicles from on-road measurements

Publisher: American Chemical Society (ACS)

Date: 03-11-2005

DOI: 10.1021/ES050069C

Abstract: The database on particle number emission factors has been very limited to date despite the increasing interest in the effects of human exposure to particles in the submicrometer range. There are also major questions on the comparability of emission factors derived through dynamometer versus on-road studies. Thus, the aims of this study were (1) to quantify vehicle number emission factors in the submicrometer (and also supermicrometer) range for stop-start and free-flowing traffic at about 100 km h(-1) driving conditions through extensive road measurements and (2) to compare the emission factors from the road measurements with those obtained previously from dynamometer studies conducted in Brisbane. For submicrometer particles the average emission factors for Tora Street were estimated at (1.89 +/- 3.40) x 10(13) particles km(-1) (mean +/- standard error n = 386) for petrol and (7.17 +/- 2.80) x 10(14) particles km(-1) (diesel n = 196) and for supermicrometer particles at 2.59 x 10(9) particles km(-1) and 1.53 x 10(12) particles km(-1), respectively. The average number emission factors for submicrometer particles estimated for Ipswich Road (stop-start traffic mode) were (2.18 +/- 0.57) x 10(13) particles km(-1) (petrol) and (2.04 +/- 0.24) x 10(14) particles km(-1) (diesel). One implication of the conclusion that emission factors of heavy duty diesel vehicles are over 1 order of magnitude higher than emission factors of petrol-fueled passenger cars is that future control and management strategies should in particular target heavy duty vehicles, as even a moderate decrease in emissions of these vehicles would have a significant impact on lowering atmospheric concentrations of particles. The finding that particle number emissions per vehicle-km are significantly larger for higher speed vehicle operation has an important implication on urban traffic planning and optimization of vehicle speed to lower their impact on airborne pollution. Additionally, statistical analysis showed that neither the measuring method (dynamometer or on-road), nor data origin (Brisbane or elsewhere in the world), is associated with a statistically significant difference between the average values of emission factors for diesel, petrol, and vehicle fleet mix. However, statistical analyses of the effect of fuel showed that the mean values of emission factors for petrol and diesel are different at a 5% significance level.

A proposed validation framework for expert elicited Bayesian Networks

Publisher: Elsevier BV

Date: 2013

DOI: 10.1016/J.ESWA.2012.07.026

Predicting the need for adaptive radiotherapy in head and neck cancer

Publisher: Elsevier BV

Date: 07-2015

DOI: 10.1016/J.RADONC.2015.06.025

Abstract: Adaptive radiotherapy (ART) can account for the dosimetric impact of anatomical change in head and neck cancer patients however it can be resource intensive. Consequently, it is imperative that patients likely to require ART are identified. The purpose of this study was to find predictive factors that identify oropharyngeal squamous cell carcinoma (OPC) and nasopharyngeal carcinoma (NPC) patients more likely to need ART. One hundred and ten patients with OPC or NPC were analysed. Patient demographics and tumour characteristics were compared between patients who were replanned and those that were not. Factors found to be significant were included in logistic regression models. Risk profiles were developed from these models. A dosimetric analysis was performed. Nodal disease stage, pre-treatment largest involved node size, diagnosis and initial weight (categorised in 2 groups) were identified as significant for inclusion in the model. Two models were found to be significant (p=0.001), correctly classifying 98.2% and 96.1% of patients respectively. Three ART risk profiles were developed. Predictive factors identifying OPC or NPC patients more likely to require ART were reported. A risk profile approach could facilitate the effective implementation of ART into radiotherapy departments through forward planning and appropriate resource allocation.

Big Bayes Stories—Foreword

Publisher: Institute of Mathematical Statistics

Date: 02-2014

DOI: 10.1214/14-STS467

Symplectic geometry spectrum regression for prediction of noisy time series

Publisher: American Physical Society (APS)

Date: 20-05-2016

DOI: 10.1103/PHYSREVE.93.052217

Understanding the causation of primary angle closure disease using the sufficient component cause model

Publisher: Wiley

Date: 29-10-2013

DOI: 10.1111/CEO.12245

Abstract: Several risk factors have been associated with primary angle closure disease, but their actual role in causation of an in idual case is not clear. Concept paper. No patient participation. The sufficient component cause model is briefly explained in the context of primary angle closure disease. The framework is used to conceptualize the role of in idual mechanisms of disease. The possibility of personalized treatment for primary angle closure disease is discussed in this context. Qualitative concepts in disease causality may refine research and treatment in primary angle closure disease. The minimum set of conditions that are sufficient for primary angle closure disease to occur is considered the sufficient component cause model for that in idual case. Described risk factors (including genes) as well as currently unknown influences play a role in the model. There may be many such models and all complementary components in any sufficient-cause model must be present for disease to occur. Interruption of any one component in that model can be used for treatment. Pupillary block is likely a component of most such models and may currently be considered a universally necessary component of these models. The sufficient component cause model can be used as a framework to explain the role of in idual mechanisms of causation and treatment of primary angle closure disease. It also aids understanding of the proportion of disease due to specific causes.

Spatio-temporal relative survival of breast and colorectal cancer in Queensland, Australia 2001–2011

Publisher: Elsevier BV

Date: 11-2016

DOI: 10.1016/J.SSTE.2016.08.002

Abstract: Despite improvements in cancer survival across many developed countries, it is unclear how survival is changing over time in small areas. This study investigated changes in breast and colorectal cancer survival across 478 areas over 11 years (2001-2011), and the influence of early diagnosis on changes. Queensland Cancer Registry data were analysed using an introduced Bayesian spatio-temporal flexible parametric relative survival model. All areas showed survival improvements between 2001-2003 and 2008-2011. The median absolute 5-year survival improvement for localised breast cancer was small (1.8%), compared to advanced (4.8%) and unknown (7.9%) breast cancer, as well as localised (2.6%), advanced (5.0%) and unknown (4.8%) colorectal cancers. Improvements in non-diagnostic factors, such as patient treatment and management, appear to be the main influence on recent survival increases for breast and colorectal cancers. Important inequalities in cancer survival between small areas remain.

Spatial inequalities in colorectal and breast cancer survival: premature deaths and associated factors.

Publisher: Elsevier BV

Date: 11-2012

DOI: 10.1016/J.HEALTHPLACE.2012.07.006

Abstract: This study examines the influence of cancer stage, distance to treatment facilities and area disadvantage on breast and colorectal cancer spatial survival inequalities. We also estimate the number of premature deaths after adjusting for cancer stage to quantify the impact of spatial survival inequalities. Population-based descriptive study of residents aged <90 years in Queensland, Australia diagnosed with primary invasive breast (25,202 females) or colorectal (14,690 males, 11,700 females) cancers during 1996-2007. Bayesian hierarchical models explored relative survival inequalities across 478 regions. Cancer stage and disadvantage explained the spatial inequalities in breast cancer survival, however spatial inequalities in colorectal cancer survival persisted after adjustment. Of the 6,019 colorectal cancer deaths within 5 years of diagnosis, 470 (8%) were associated with spatial inequalities in non-diagnostic factors, i.e. factors beyond cancer stage at diagnosis. For breast cancers, of 2,412 deaths, 170 (7%) were related to spatial inequalities in non-diagnostic factors. Quantifying premature deaths can increase incentive for action to reduce these spatial inequalities.

Adjusted likelihoods for synthesising empirical evidence from studies that differ in quality and design: effects of environmental tobacco smoke

Publisher: Institute of Mathematical Statistics

Date: 08-2004

DOI: 10.1214/088342304000000350

And the Winner is …: Statistical Selection for Excellence in Quality Management

Publisher: Informa UK Limited

Date: 1993

DOI: 10.1080/01966324.1993.10737351

Statistical analysis of sheep CAT scan images using a Bayesian mixture model

Publisher: CSIRO Publishing

Date: 2004

DOI: 10.1071/AR03017

Abstract: CAT scanning techniques are available to provide images that can aid in the assessment of carcass traits in live sheep during the course of animal experiments. In this paper we present a Bayesian formulation of an analysis that allows us to determine the composition of a scan in terms of proportions of the image attributable to fat, muscle (lean tissue), and bone. The technique, known as finite mixture modelling, also provides information about the distributional properties of some of these components, such as fat and bone. In the case of muscle, the analysis estimates several Gaussian distributions that combine to provide an approximation to its likelihood.The model is estimated through the use of the Gibbs s ler, with the distributional properties of carcass components being obtained from the resultant Markov chains.

Understanding uncertainties in non-linear population trajectories: A Bayesian semi-parametric hierarchical approach to large-scale surveys of coral cover

Publisher: Public Library of Science (PLoS)

Date: 03-11-2014

DOI: 10.1371/JOURNAL.PONE.0110968

Barrow Island's biosecurity : catching the unknown invader

Publisher: Oxford University Press (OUP)

Date: 18-05-2010

DOI: 10.1111/J.1740-9713.2010.00418.X

Abstract: When industry meets a conservation area, animals or plants from outside may hitch a lift and potentially wreak havoc. How can you be sure of catching the intruders — or at least 80% sure? A government directive instructed Frith Jarrad, Peter Whittle, Susan Barrett and Kerrie Mengersen to come up with a statistically measurable scheme.

Mapping perceptions of species' threats and population trends to inform conservation efforts: The Bornean orangutan case study

Publisher: Wiley

Date: 19-01-2015

DOI: 10.1111/DDI.12286

Generalizing the use of geographical weights in biodiversity modelling

Publisher: Wiley

Date: 07-07-2014

DOI: 10.1111/GEB.12203

Virtual Reef Diver

Publisher: Ubiquity Press, Ltd.

Date: 2023

DOI: 10.5334/CSTP.571

Passive smoking in the workplace: classical and Bayesian meta-analyses

Publisher: Springer Science and Business Media LLC

Date: 10-1994

DOI: 10.1007/BF00454366

Abstract: Hyperglycemia induced oxidative stress and inflammation lead to development of diabetic cardiomyopathy. Diabetic patients are more at risk for myocardial infarction than non-diabetics. The current study has investigated the involvement of PPARγ activation in effects of crocin as a natural carotenoid against cardiac infarction in diabetic rats. Diabetes was induced in male Wistar rats by streptozotocin injection (55 mg/kg, i.p) 15 min after the administration of nicotinamide (110 mg/kg). Then saline, crocin (40 mg/kg, orally) and GW9662 (1 mg/kg, as PPARγ antagonist) were injected for 4 weeks. Isoprenaline was administrated on the 27th and 28th days to induce infarction. Cardiac injury markers, antioxidant enzymes content, blood glucose level, lipid profile, pro and anti-inflammatory cytokines, and PPARγ gene expression were measured. GSH, CAT content, CK-MB isoenzyme, LDH level, IL-10 and PPARγ gene expression in myocardial tissue were decreased in diabetic rats receiving isoprenaline and inflammatory cytokines TNFα and IL-6 and also plasma lipids were increased. Crocin administration significantly ameliorated inflammatory cytokines levels, CK-MB, and LDH contents and also it could enhance antioxidant capacity and PPARγ expression. However, GW9662 administration reversed the effects of crocin. Overexpression of PPARγ in crocin treated rats and inhibition of crocin effects by GW9662 reflected the potential involvement of PPARγ pathway in the protective effects of crocin.

Adaptive Bayesian compound designs for dose finding studies

Publisher: Elsevier BV

Date: 06-2012

DOI: 10.1016/J.JSPI.2011.12.029

Recentered Importance Sampling With Applications to Bayesian Model Validation

Publisher: Informa UK Limited

Date: 22-05-2012

DOI: 10.1080/10618600.2012.681239

Sparing healthy tissue and increasing tumor dose using bayesian modeling of geometric uncertainties for planning target volume personalization

Publisher: Elsevier BV

Date: 06-2015

DOI: 10.1016/J.IJROBP.2015.01.034

Abstract: To develop a mathematical tool that can update a patient's planning target volume (PTV) partway through a course of radiation therapy to more precisely target the tumor for the remainder of treatment and reduce dose to surrounding healthy tissue. Daily on-board imaging was used to collect large datasets of displacements for patients undergoing external beam radiation therapy for solid tumors. Bayesian statistical modeling of these geometric uncertainties was used to optimally trade off between displacement data collected from previously treated patients and the progressively accumulating data from a patient currently partway through treatment, to optimally predict future displacements for that patient. These predictions were used to update the PTV position and margin width for the remainder of treatment, such that the clinical target volume (CTV) was more precisely targeted. Software simulation of dose to CTV and normal tissue for 2 real prostate displacement datasets consisting of 146 and 290 patients treated with a minimum of 30 fractions each showed that re-evaluating the PTV position and margin width after 8 treatment fractions reduced healthy tissue dose by 19% and 17%, respectively, while maintaining CTV dose. Incorporating patient-specific displacement patterns from early in a course of treatment allows PTV adaptation for the remainder of treatment. This substantially reduces the dose to healthy tissues and thus can reduce radiation therapy-induced toxicities, improving patient outcomes.

Bayesian mixture model estimation of aerosol particle size distributions

Publisher: Wiley

Date: 24-01-2011

DOI: 10.1002/ENV.1020

Using a supervised principal components analysis for variable selection in high-dimensional datasets reduces false discovery rates

Publisher: Cold Spring Harbor Laboratory

Date: 15-05-2020

DOI: 10.1101/2020.05.15.097774

Abstract: High-dimensional datasets, where the number of variables ‘ p ’ is much larger compared to the number of s les ‘ n ’, are ubiquitous and often render standard classification and regression techniques unreliable due to overfitting. An important research problem is feature selection — ranking of candidate variables based on their relevance to the outcome variable and retaining those that satisfy a chosen criterion. In this article, we propose a computationally efficient variable selection method based on principal component analysis. The method is very simple, accessible, and suitable for the analysis of high-dimensional datasets. It allows to correct for population structure in genome-wide association studies (GWAS) which otherwise would induce spurious associations and is less likely to overfit. We expect our method to accurately identify important features but at the same time reduce the False Discovery Rate (FDR) (the expected proportion of erroneously rejected null hypotheses) through accounting for the correlation between variables and through de-noising data in the training phase, which also make it robust to outliers in the training data. Being almost as fast as univariate filters, our method allows for valid statistical inference. The ability to make such inferences sets this method apart from most of the current multivariate statistical tools designed for today’s high-dimensional data. We demonstrate the superior performance of our method through extensive simulations. A semi-real gene-expression dataset, a challenging childhood acute lymphoblastic leukemia (CALL) gene expression study, and a GWAS that attempts to identify single-nucleotide polymorphisms (SNPs) associated with the rice grain length further demonstrate the usefulness of our method in genomic applications. An integral part of modern statistical research is feature selection, which has claimed various scientific discoveries, especially in the emerging genomics applications such as gene expression and proteomics studies, where data has thousands or tens of thousands of features but a limited number of s les. However, in practice, due to unavailability of suitable multivariate methods, researchers often resort to univariate filters when it comes to deal with a large number of variables. These univariate filters do not take into account the dependencies between variables because they independently assess variables one-by-one. This leads to loss of information, loss of statistical power (the probability of correctly rejecting the null hypothesis) and potentially biased estimates. In our paper, we propose a new variable selection method. Being computationally efficient, our method allows for valid inference. The ability to make such inferences sets this method apart from most of the current multivariate statistical tools designed for today’s high-dimensional data.

Methods for Constructing Uncertainty Intervals for Queries of Bayesian Nets

Publisher: Wiley

Date: 14-11-2014

DOI: 10.1111/ANZS.12095

Sequential analysis of uncommon adverse outcomes

Publisher: Elsevier BV

Date: 10-2010

DOI: 10.1016/J.JHIN.2010.04.022

Abstract: Sequential analysis of uncommon adverse outcomes (AEs) such as surgical site infections (SSIs) is desirable. Short postoperative lengths of stay (LOS) result in many SSIs occurring after discharge and they are often superficial. Deep and organ space (complex) SSIs occur less frequently but are detected more reliably and are suitable for monitoring wound care. Those occurring post-discharge usually require readmissison and can be counted accurately. Sequential analysis of meticillin-resistant Staphylococcus aureus bacteraemia is also needed. The key to prevention is to implement systems based on evidence, e.g. using 'bundles' and checklists. Regular mortality and morbidity audit meetings are required and these may need to be followed by independent audits. Sequential statistical analysis is desirable for data presentation, to detect changes, and to discourage t ering with processes when occasional AEs occur in a reliable system. Tabulations and cumulative observed minus expected (O-E) charts and funnel plots are valuable, supplemented in the presence of apparent 'runs' of AEs by cumulative sum analysis. Used prospectively, they may enable staff to visualise and detect patterns or shifts in rates and counts that might not otherwise be apparent.

Nonparametric Bayesian Nonnegative Matrix Factorization

Publisher: Springer International Publishing

Date: 2020

DOI: 10.1007/978-3-030-57524-3_11

Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data

Publisher: American Association for the Advancement of Science (AAAS)

Date: 23-09-2022

DOI: 10.1126/SCIADV.ABM5952

Abstract: This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

Characteristics of particle number and mass concentrations in residential houses in Brisbane, Australia

Publisher: Elsevier BV

Date: 09-2003

DOI: 10.1016/S1352-2310(03)00566-1

Teaching and Learning in Statistics: Harnessing the power of modern statistical software to improve students statistical reasoning and thinking

Publisher: Universitat Politècnica València

Date: 26-06-2019

DOI: 10.4995/HEAD19.2019.9239

Abstract: The reproducibility crisis in science has launched global discussion about the need to restructure the way statistics is taught across a wide range of disciplines. While this need has been recognized and discussed in the academic community for many years, the impetus for educational reform of statistics was boosted by Ioannidis (2005), which resulted in a great deal of attention on issues regarding the inappropriate use of statistical reasoning. The availability of data across business and research has increased dramatically in recent years. This access to data has resulted in almost every member of society needing a skill set that allows them to think critically about the inferences that can validly be drawn to improve decisions based on data. One way of improving statistical literacy and thinking is through the identification and use of appropriate statistical software that will allow students, and other practitioners with basic training, access to modern statistical modeling techniques on a platform that allows them to focus on outcomes. A key component of using AutoStat for teaching statistical thinking is in alleviating the need for coding, which allows the instructors to focus on key concepts, questions and outcomes.

Bayesian change point detection in monitoring cardiac surgery outcomes

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 07-2011

DOI: 10.1097/QMH.0B013E318220897E

Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms

Publisher: Elsevier BV

Date: 1999

DOI: 10.1016/S0167-9473(98)00058-9

Is the observed lowering of intraocular pressure due to treatment?

Publisher: Medknow

Date: 2013

DOI: 10.4103/0301-4738.107908

A method for designing complex biosecurity surveillance systems: Detecting non-indigenous species of invertebrates on Barrow Island

Publisher: Wiley

Date: 05-2013

DOI: 10.1111/DDI.12056

A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset

Publisher: Public Library of Science (PLoS)

Date: 29-10-2015

DOI: 10.1371/JOURNAL.PONE.0141120

Is the association between temperature and mortality modified by age, gender and socio-economic status?

Publisher: Elsevier BV

Date: 08-2010

DOI: 10.1016/J.SCITOTENV.2010.04.058

Abstract: A number of studies have examined the relationship between high ambient temperature and mortality. Recently, concern has arisen about whether this relationship is modified by socio-demographic factors. However, data for this type of study is relatively scarce in subtropical/tropical regions where people are well accustomed to warm temperatures. To investigate whether the relationship between daily mean temperature and daily all-cause mortality is modified by age, gender and socio-economic status (SES) in Brisbane, Australia. We obtained daily mean temperature and all-cause mortality data for Brisbane, Australia during 1996-2004. A generalised additive model was fitted to assess the percentage increase in all deaths with every one degree increment above the threshold temperature. Different age, gender and SES groups were included in the model as categorical variables and their modification effects were estimated separately. A total of 53,316 non-external deaths were included during the study period. There was a clear increasing trend in the harmful effect of high temperature on mortality with age. The effect estimate among women was more than 20 times that among men. We did not find an SES effect on the percent increase associated with temperature. The effects of high temperature on all deaths were modified by age and gender but not by SES in Brisbane, Australia.

Spatiotemporal relationship between particle air pollution and respiratory emergency hospital admissions in Brisbane, Australia

Publisher: Elsevier BV

Date: 02-2007

DOI: 10.1016/J.SCITOTENV.2006.10.050

Abstract: The nature of spatial variation in the relationship between air pollution and health outcomes within a city remains an open and important question. This study investigated the spatial variability of particle matter air pollution and its association with respiratory emergency hospital admissions across six geographic areas in Brisbane, Australia. Data on particles of 10 microm or less in aerodynamic diameter per cubic metre (PM10), meteorological conditions, and daily respiratory emergency hospital admissions were obtained for the period of 1 January 1998 to 31 December 2001. A Poisson generalised linear model was used to estimate the specific effects of PM10 on respiratory emergency hospital admissions for each geographic area. A pooled effect of PM10 was then estimated using a meta-analysis approach for the whole city. The results of this study indicate that the magnitude of the association between particulate matter and respiratory emergency hospital admissions varied across different geographic areas in Brisbane. This relationship appeared to be stronger in areas with heavy traffic. We found an overall increase of 4.0% (95% confidence interval [CI]: 1.1-6.9%) in respiratory emergency hospital admissions associated with an increase of 10 microg /m3 in PM10 in the single pollutant model. The association was weaker but still statistically significant (an increase of 2.6% 95% CI: 1.0-5.5%) after adjusting for O3, but did not appear to be affected by NO2. The effect estimates of PM10 were generally consistent for three spatial methods used in this study, but appeared to be underestimated if the spatial nature of the data was ignored. Therefore, the spatial variation in the relationship between PM10 and health outcomes needs to be considered when the health impact of air pollution is assessed, particularly for big cities.

Hidden Markov Models for Complex Stochastic Processes: A Case Study in Electrophysiology

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH18

Concentrations of organochlorine pesticides in pooled human serum by age and gender

Publisher: Elsevier BV

Date: 04-2017

DOI: 10.1016/J.ENVRES.2016.12.009

Abstract: Organochlorine pesticides (OCPs) have been used for many decades in Australia with cessation of selected persistent and bioaccumulative OCPs ranging from the 1970s to as recently as 2007. The specific aims of this study were to use s les representative of an Australian population to assess age and gender differences in the concentration of OCPs in human blood sera and to investigate temporal trends in these chemicals. Serum was collected from de-identified, surplus pathology s les over five time periods (2002/03, 2006/07, 2008/09, 2010/11 and 2012/13), with 183 serum pools made from 12,175 in idual s les 26 pools in 2002/03, 85 pools in 2006/07 and 24 pools each in 2008/09, 2010/11 and 2012/13. S les were analyzed for hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH), γ -hexachlorocyclohexane (lindane) (γ-HCH), oxy-chlordane, trans-nonachlor, p,p'-DDE, o,p'-DDT, p,p'-DDT and Mirex. Stratification criteria included gender and age (0-4 5-15 16-30 31-45 46-60 and >60 years) with age additionally stratified by adults >16 years and children 0-4 and 5-15 years. All pools from all collection periods had detectable concentrations of OCPs with a detection frequency of >60% for HCB, β-HCH, trans-nonachlor, p,p'-DDT and p,p'-DDE. The overall OCP concentrations increased with age with the highest concentrations in the >60 years groups. Females did not have higher mean OCP concentrations than males except for HCB concentrations (p=0.0006). Temporal trends showed overall decreasing serum concentrations by collection period with the exception of an increase in OCP concentrations between 2006/07 and 2008/09. Excluding this data point, HCB decreased from year to year by 7-76% β-HCH concentrations decreased by 14 - 38% trans-nonachlor concentrations decreased by 10 - 65% p,p'-DDE concentrations decreased by 6 - 52% and p,p'-DDT concentrations decreased by 7 - 30%. The results indicate that OCP concentrations have decreased over time as is to be expected following the phase out of these chemicals in Australia.

Modality in ambient particle size distributions and its potential as a basis for developing air quality regulation

Publisher: Elsevier BV

Date: 03-2008

DOI: 10.1016/J.ATMOSENV.2007.09.076

Moisture, Crops and Salination: An Analysis of a Three-Dimensional Agricultural Data Set

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH14

Bayesian Change Point Detection in Monitoring Clinical Outcomes

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH11

Modelling imperfect presence data obtained by citizen science

Publisher: Wiley

Date: 21-05-2017

DOI: 10.1002/ENV.2446

Bayesian Classification and Regression Trees

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH19

Estimating uncertainty in the revised universal soil loss equation via bayesian melding

Publisher: Springer Science and Business Media LLC

Date: 28-01-2010

DOI: 10.1007/S13253-009-0005-Y

Subset selection of the t best populations

Publisher: Informa UK Limited

Date: 1986

DOI: 10.1080/03610928608829299

Overfitting Bayesian Mixture Models with an Unknown Number of Components

Publisher: Public Library of Science (PLoS)

Date: 15-07-2015

DOI: 10.1371/JOURNAL.PONE.0131739

Improving the Quality of Patient Care Using Reliability Measures: A Classification Tree Approach

Publisher: Wiley

Date: 05-01-2007

DOI: 10.1002/SIM.2461

Abstract: This paper considers the application and interpretation of new reliability measures for a classification tree-based medical risk assessment tool. Following the construction of a classification tree reliability measures may then be used to provide an estimate of the precision of the classification and the probability in each terminal node of the classification tree. Identification of unreliable nodes (those that have low precision) in this application may indicate patient groups requiring closer monitoring or scenarios in which further information about the patient is required, thereby providing medical practitioners with an avenue for more informed decision making.

Bayesian Weibull survival model for gene expression data

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH10

Corona ions from overhead transmission voltage powerlines : effect on DC e-field and ambient particle concentration levels

Publisher: American Chemical Society (ACS)

Date: 02-12-2010

DOI: 10.1021/ES9024063

Abstract: Along with their essential role in electricity transmission and distribution, some powerlines also generate large concentrations of corona ions. This study aimed at the comprehensive investigation of corona ions, vertical direct current electric field (dc e-field), ambient aerosol particle charge, and particle number concentration levels in the proximity of some high/subtransmission voltage powerlines. The influence of meteorology on the instantaneous value of these parameters and the possible existence of links or associations between the parameters measured were also statistically investigated. The presence of positive and negative polarities of corona ions was associated with variation in the mean vertical dc e-field, ambient ion, and particle charge concentration level. Though these variations increased with wind speed, their values also decreased with distance from the powerlines. Predominately positive polarities of ions were recorded up to a distance of 150 m (with the maximum values recorded 50 m downwind of the powerlines). At 200 m from the source, negative ions predominated. Particle number concentration levels, however, remained relatively constant (10(3) particle cm(-3)), irrespective of the s ling site and distance from the powerlines. Meteorological factors of temperature, humidity, and wind direction showed no influence on the electrical parameters measured. The study also discovered that e-field measurements were not necessarily a true representation of the ground-level ambient ion article charge concentrations.

Characterization of the atmospheric electrical environment near a corona ion-emitting source

Publisher: Elsevier BV

Date: 03-2008

DOI: 10.1016/J.ATMOSENV.2007.10.090

People's perceptions about the importance of forests on Borneo

Publisher: Public Library of Science (PLoS)

Date: 09-09-2013

DOI: 10.1371/JOURNAL.PONE.0073008

Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity

Publisher: Institute of Mathematical Statistics

Date: 09-2016

DOI: 10.1214/16-AOAS944

The role of pest risk analysis in plant biosecurity

Publisher: Springer Netherlands

Date: 16-10-2014

DOI: 10.1007/978-94-007-7365-3_9

An upper bound on a ratio of variances

Publisher: Informa UK Limited

Date: 1993

DOI: 10.1080/03610929308831124

Robustness to normality of a selection rule

Publisher: Informa UK Limited

Date: 1992

DOI: 10.1080/03610919308813007

Temperature enhanced effects of ozone on cardiovascular mortality in 95 large US communities, 1987-2000: Assessment using the NMMAPS data

Publisher: Informa UK Limited

Date: 19-10-2009

DOI: 10.1080/19338240903240749

Abstract: A few studies examined interactive effects between air pollution and temperature on health outcomes. In this study, the authors aimed to examine whether temperature modified effects of ozone on cardiovascular mortality in 95 large US cities. The authors separately used a nonparametric regression model and a parametric regression model to explore interactive effects of temperature and ozone on cardiovascular mortality between May and October of the years from 1987 to 2000. The authors used a Bayesian meta-analysis to pool estimates. The nonparametric and parametric regression models both showed that temperature enhanced effects of ozone on mortality, but the effect modification varied across regions. A 10-ppb increment in average ozone concentration at 3 previous days was associated with 0.41% (95% posterior interval [PI]: -0.19%, 0.93%), 0.27% (95% PI: -0.44%, 0.87%), and 1.68% (95% PI: 0.07%, 3.26%) increases in daily cardiovascular mortality corresponding to low, moderate, and high levels of temperature in all 95 US cities, respectively. The authors concluded that temperature modified effects of ozone, particularly in the northern regions.

Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis

Publisher: Elsevier BV

Date: 2015

DOI: 10.1016/J.ENVRES.2014.07.033

Abstract: A novel avian influenza A (H7N9) virus was first found in humans in Shanghai, and infected over 433 patients in China. To date, very little is known about the spatiotemporal variability or environmental drivers of the risk of H7N9 infection. This study explored the spatial and temporal variation of H7N9 infection and assessed the effects of temperature and rainfall on H7N9 incidence. A Bayesian spatial conditional autoregressive (CAR) model was used to assess the spatiotemporal distribution of the risk of H7N9 infection in Shanghai, by district and fortnight for the period 19th February-14th April 2013. Data on daily laboratory-confirmed H7N9 cases, and weather variability including temperature (°C) and rainfall (mm) were obtained from the Chinese Information System for Diseases Control and Prevention and Chinese Meteorological Data Sharing Service System, respectively, and aggregated by fortnight. High spatial variations in the H7N9 risk were mainly observed in the east and centre of Shanghai municipality. H7N9 incidence rate was significantly associated with fortnightly mean temperature (Relative Risk (RR): 1.54 95% credible interval (CI): 1.22-1.94) and fortnightly mean rainfall (RR: 2.86 95% CI: 1.47-5.56). There was a substantial variation in the spatiotemporal distribution of H7N9 infection across different districts in Shanghai. Optimal temperature and rainfall may be one of the driving forces for H7N9.

External injury documentation in major trauma victims is inadequate: Grounds for routine photography in the emergency department?

Publisher: Wiley

Date: 12-2008

DOI: 10.1111/J.1742-6723.2008.01136.X

Abstract: There is no widely accepted measure of clinical documentation quality in the ED. The present study creates a measure for comparing the quality of clinical documentation of external injuries with autopsy reports. This is used to discuss the advantages and disadvantages of introducing routine photography to improve clinical documentation of injuries. This retrospective case series addressed all non-surviving major trauma patients (Injury Severity Score > or =15) presenting to St. Vincent's Hospital ED, Sydney, within the 5 year period from 1 July 2002 to 30 June 2007. Comparison between clinical and autopsy documentation of external injuries was completed for each major trauma patient. Of the 48 major trauma patients, there were an average of 11.6 injuries missed in documentation per patient (P < 0.001, 95% CI 8.6-14.6). ED documentation recorded on average 29% (95% CI 26%-32%) of the external injuries that appeared in the autopsy report. We call this percentage the external injury documentation rate. The external injury documentation rate was influenced by injury count and body region, but was not influenced by age, sex, severity (using the Abbreviated Injury Scale and Injury Severity Score), or whether the clinician used a trauma survey or standard progress notes or not, and there was no visible trend over time. Clinical documentation of external injuries in major trauma is poor. This is presumably because of many factors, including time pressures and high-stress environments. A possible strategy to improve this documentation is routine photography, which should offer both clinical and legal benefits.

Effects of dredging on critical ecological processes for marine invertebrates, seagrasses and macroalgae, and the potential for management with environmental windows using Western Australia as a case study

Publisher: Elsevier BV

Date: 07-2017

DOI: 10.1016/J.ECOLIND.2017.03.026

Selective reporting of adjusted estimates in observational epidemiology studies: Reasons and implications for meta-analyses

Publisher: SAGE Publications

Date: 06-10-2008

DOI: 10.1177/0163278708324438

Abstract: For meta-analyses of observational epidemiology studies, unadjusted and adjusted study estimates are often extracted. However, there is evidence of selective reporting of adjusted study estimates. We investigate adjustment reporting bias, examining the reasons why some studies do not contribute an adjusted estimate to a meta-analysis. Ten published meta-analyses were re-analysed to assess evidence of adjustment reporting bias and over 100 primary studies were read to investigate why they did not contribute an adjusted estimate to a meta-analysis. Selective reporting of adjusted estimates may lead to a bias in some meta-analyses when adjusted study estimates are not reported because univariate analyses indicated a non-significant effect. We recommend that unadjusted and adjusted study estimates be extracted for a meta-analysis. If adjusted estimates cannot be obtained, the reasons for this should be investigated and sensitivity analyses could be used to assess the impact of this on the meta-analysis.

Systematic Review and Meta-analysis Using N-of-1 Trials

Publisher: Springer Netherlands

Date: 2015

DOI: 10.1007/978-94-017-7200-6_16

Species richness on coral reefs and the pursuit of convergent global estimates

Publisher: Elsevier BV

Date: 02-2015

DOI: 10.1016/J.CUB.2014.12.022

Abstract: Global species richness, whether estimated by taxon, habitat, or ecosystem, is a key bio ersity metric. Yet, despite the global importance of bio ersity and increasing threats to it (e.g., we are no better able to estimate global species richness now than we were six decades ago. Estimates of global species richness remain highly uncertain and are often logically inconsistent. They are also difficult to validate because estimation of global species richness requires extrapolation beyond the number of species known. Given that somewhere between 3% and >96% of species on Earth may remain undiscovered, depending on the methods used and the taxa considered, such extrapolations, especially from small percentages of known species, are likely to be highly uncertain. An alternative approach is to estimate all species, the known and unknown, directly. Using expert taxonomic knowledge of the species already described and named, those already discovered but not yet described and named, and those still awaiting discovery, we estimate there to be 830,000 (95% credible limits: 550,000-1,330,000) multi-cellular species on coral reefs worldwide, excluding fungi. Uncertainty surrounding this estimate and its components were often strongly skewed toward larger values, indicating that many more species on coral reefs is more plausible than many fewer. The uncertainties revealed here should guide future research toward achieving convergence in global species richness estimates for coral reefs and other ecosystems via adaptive learning protocols whereby such estimates can be tested and improved, and their uncertainties reduced, as new knowledge is acquired.

Statistical Analysis of N-of-1 Trials

Publisher: Springer Netherlands

Date: 2015

DOI: 10.1007/978-94-017-7200-6_12

Temperature enhanced effects of ozone on cardiovascular mortality in 95 large US communities, 1987-2000: Assessment using the NMMAPS data

Publisher: Informa UK Limited

Date: 19-10-2009

DOI: 10.1080/19338240903240749

Abstract: A few studies examined interactive effects between air pollution and temperature on health outcomes. In this study, the authors aimed to examine whether temperature modified effects of ozone on cardiovascular mortality in 95 large US cities. The authors separately used a nonparametric regression model and a parametric regression model to explore interactive effects of temperature and ozone on cardiovascular mortality between May and October of the years from 1987 to 2000. The authors used a Bayesian meta-analysis to pool estimates. The nonparametric and parametric regression models both showed that temperature enhanced effects of ozone on mortality, but the effect modification varied across regions. A 10-ppb increment in average ozone concentration at 3 previous days was associated with 0.41% (95% posterior interval [PI]: -0.19%, 0.93%), 0.27% (95% PI: -0.44%, 0.87%), and 1.68% (95% PI: 0.07%, 3.26%) increases in daily cardiovascular mortality corresponding to low, moderate, and high levels of temperature in all 95 US cities, respectively. The authors concluded that temperature modified effects of ozone, particularly in the northern regions.

Statistical Methods for Hospital Monitoring with R

Publisher: Wiley

Date: 04-07-2013

DOI: 10.1002/9781118639153

THE IMPACT OF METHOD CHOICE ON META‐ANALYSIS

Publisher: Wiley

Date: 03-1995

DOI: 10.1111/J.1467-842X.1995.TB00869.X

Bayesian Estimation of g-and-k Distributions using MCMC

Publisher: Springer Science and Business Media LLC

Date: 03-2005

DOI: 10.1007/BF02736120

Bayesian computation and stochastic systems

Publisher: Institute of Mathematical Statistics

Date: 02-1995

DOI: 10.1214/SS/1177010123

Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation

Publisher: BMJ

Date: 05-2016

DOI: 10.1136/BMJOPEN-2015-010253

Integrating Bayesian networks and geographic information systems: Good practice examples

Publisher: Wiley

Date: 19-09-2011

DOI: 10.1002/IEAM.262

Abstract: Bayesian networks (BNs) are becoming increasingly common in problems with spatial aspects. The degree of spatial involvement may range from spatial mapping of BN outputs based on nodes in the BN that explicitly involve geographic features, to integration of different networks based on geographic information. In these situations, it is useful to consider how geographic information systems (GISs) could be used to enhance the conceptualization, quantification, and prediction of BNs. Here, we discuss some techniques that may be used to integrate GIS and BN models, with reference to some recent literature which illustrate these approaches. We then reflect on 2 case studies based on our own experience. The first involves the integration of GIS and a BN to assess the scientific factors associated with initiation of Lyngbya majuscula, a cyanobacterium that occurs in coastal waterways around the world. The 2nd case study involves the use of GISs as an aid for eliciting spatially informed expert opinion and expressing this information as prior distributions for a Bayesian model and as input into a BN. Elicitator, the prototype software package we developed for achieving this, is also briefly described. Whereas the 1st case study demonstrates a GIS-data driven specification of conditional probability tables for BNs with complete geographical coverage for all the data layers involved, the 2nd illustrates a situation in which we do not have complete coverage and we are forced to extrapolate based on expert judgement.

Bayesian mixed effects models

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH8

Ordering of Hierarchies in Hierarchical Models: Bone Mineral Density Estimation

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH9

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Publisher: Cold Spring Harbor Laboratory

Date: 05-05-2020

DOI: 10.1101/2020.04.30.20085662

Abstract: The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of trajectories of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future. Using Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across erse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions. Countries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of second waves. Our approach is also effective at pre-empting potential second waves and flare-ups. We demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported cases and ii) broader testing is required early in the epidemic to understand the magnitude of undocumented infections and recover rapidly. We conclude that clear patterns of containment are essential prior to relaxation of restrictions and show that modelling can provide insights to this end.

Crown‐of‐thorns starfish undermine the resilience of coral populations on the Great Barrier Reef

Publisher: Wiley

Date: 27-04-2017

DOI: 10.1111/GEB.12590

SELECTING THE t BEST POPULATIONS—SCALE PARAMETER CASE

Publisher: Wiley

Date: 03-1990

DOI: 10.1111/J.1467-842X.1990.TB01006.X

A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE

Publisher: Elsevier BV

Date: 08-2017

DOI: 10.1016/J.SPASTA.2017.04.006

Probabilistic subgroup identification using Bayesian finite mixture modelling: A case study in Parkinson's disease phenotype identification

Publisher: SAGE Publications

Date: 16-12-2012

DOI: 10.1177/0962280210391012

Abstract: This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person's membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson's disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson's Disease Rating Scale (UPDRS).

Introduction

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH1

A new ecosystem for evidence synthesis

Publisher: Springer Science and Business Media LLC

Date: 23-03-2020

DOI: 10.1038/S41559-020-1153-2

A Bayesian Regression Model with Variable Selection for Genome‐Wide Association Studies

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH6

Ecological aspects of biosecurity surveillance design for the detection of multiple invasive animal species

Publisher: Springer Science and Business Media LLC

Date: 25-09-2011

DOI: 10.1007/S10530-010-9870-0

Visual symptoms following iridotomy

Publisher: Wiley

Date: 09-10-2018

DOI: 10.1111/CEO.13392

Bayesian Meta‐Analysis

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH7

Adapting ICU Mortality Models for Local Data: A Bayesian Approach

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472.CH5

Integrated Bayesian network framework for modeling complex ecological issues

Publisher: Wiley

Date: 18-11-2011

DOI: 10.1002/IEAM.274

Abstract: The management of environmental problems is multifaceted, requiring varied and sometimes conflicting objectives and perspectives to be considered. Bayesian network (BN) modeling facilitates the integration of information from erse sources and is well suited to tackling the management challenges of complex environmental problems. However, combining several perspectives in one model can lead to large, unwieldy BNs that are difficult to maintain and understand. Conversely, an oversimplified model may lead to an unrealistic representation of the environmental problem. Environmental managers require the current research and available knowledge about an environmental problem of interest to be consolidated in a meaningful way, thereby enabling the assessment of potential impacts and different courses of action. Previous investigations of the environmental problem of interest may have already resulted in the construction of several disparate ecological models. On the other hand, the opportunity may exist to initiate this modeling. In the first instance, the challenge is to integrate existing models and to merge the information and perspectives from these models. In the second instance, the challenge is to include different aspects of the environmental problem incorporating both the scientific and management requirements. Although the paths leading to the combined model may differ for these 2 situations, the common objective is to design an integrated model that captures the available information and research, yet is simple to maintain, expand, and refine. BN modeling is typically an iterative process, and we describe a heuristic method, the iterative Bayesian network development cycle (IBNDC), for the development of integrated BN models that are suitable for both situations outlined above. The IBNDC approach facilitates object-oriented BN (OOBN) modeling, arguably viewed as the next logical step in adaptive management modeling, and that embraces iterative development. The benefits of OOBN modeling in the environmental community have not yet been fully realized in environmental management research. The IBNDC approach to BN modeling is described in the context of 2 case studies. The first is the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia where 3 existing models are being integrated, and the second case study is the viability of the free-ranging cheetah (Acinonyx jubatus) population in Namibia where an integrated OOBN model is created consisting of 3 independent subnetworks, each describing a particular aspect of free-ranging cheetah population conservation.

Bayesian Approach to Predicting Acute Appendicitis Using Ultrasonographic and Clinical Variables in Children

Publisher: The Korean Society of Medical Informatics

Date: 2019

DOI: 10.4258/HIR.2019.25.3.212

Spatially stratified sampling using auxiliary information for geostatistical mapping

Publisher: Springer Science and Business Media LLC

Date: 17-09-2011

DOI: 10.1007/S10651-009-0122-3

Letter to the editor (multiple letters) [1]

Publisher: Wiley

Date: 2004

DOI: 10.1002/SIM.1683

Augmenting disease maps: a Bayesian meta-analysis approach

Publisher: The Royal Society

Date: 08-2020

DOI: 10.1098/RSOS.192151

Abstract: Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

Bayesian latent trait modelling of migraine symptom data

Publisher: Springer Science and Business Media LLC

Date: 24-04-2009

DOI: 10.1007/S00439-009-0671-4

Abstract: Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population s le using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Letter to the editor (multiple letters) [2]

Publisher: Wiley

Date: 2004

DOI: 10.1002/SIM.1685

Being Bayesian in the 2020s

Publisher: The Royal Society

Date: 27-03-2023

DOI: 10.1098/RSTA.2022.0156

Abstract: Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.

Test-retest reliability of hand-held dynamometry and functional tests in systemic lupus erythematosus

Publisher: SAGE Publications

Date: 02-2011

DOI: 10.1177/0961203310388448

Abstract: The aim of this study was to evaluate the test–retest reliability and determine the degree of measurement error of tests of isometric muscle strength and upper and lower limb function in women with systemic lupus erythematosus (SLE). Twelve women with SLE (age 39.8 ± 10 years) were assessed on two occasions separated by a 7–10-day interval. Strength of six muscle groups was measured using a hand-held dynamometer function was measured by the 30-s sit to stand test and the 30-s 1 kg arm lift. Relative reliability was estimated using the intraclass correlation coefficient (ICC), model 2,1 (ICC2,1). Absolute reliability was estimated using standard error measurement and the minimal detectable difference was calculated. All ICCs were greater than 0.87. Muscle strength would need to increase by between 18% and 39% in women with SLE to be 95% confident of detecting real changes. The functional tests demonstrated a systematic bias between trials. This study demonstrates that hand-held dynamometry in SLE can be performed with excellent reliability. Further work needs to be completed to determine the number of trials necessary for both the 30-s sit to stand and 30-s 1 kg arm lift to decrease the systematic bias.

Bayesian analysis of thematic map accuracy data

Publisher: Elsevier BV

Date: 02-2008

DOI: 10.1016/J.RSE.2008.10.008

Meta‐analytic approaches to dose‐response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke

Publisher: Wiley

Date: 15-03-1995

DOI: 10.1002/SIM.4780140514

Abstract: This paper outlines several meta‐analytic approaches to the assessment of quantal dose—response relationships that is, to the evaluation of an increase in the level of exposure to an agent and the associated relative risk of a disease when this is investigated over a number of different studies. Analysis is developed at two levels: first, a consistent method of evaluating the dose—response relationship is applied to each study, and second, an overall picture is obtained by comparing and combining these relationships. At the first stage, for an in idual study, dose—response assessment involves choices of model and appropriate tests for trend, which are influenced by such issues as dose measurement and use of the unexposed group. At the second stage, different methods for pooling results across studies must be considered. These depend on the choices made in the first stage of analysis, with additional attention paid to heterogeneity, and possible bias due to studies included in meta‐analysis. We describe these meta‐analytic approaches for three methods of evaluating dose response. The approaches are illustrated by evaluating the relationship between lung cancer and levels of exposure to environmental tobacco smoke (ETS). The strength of this relationship has been a point of debate in recent assessment of evidence for an overall carcinogenic effect of ETS exposure. We find little indication of a consistent dose response, a result explained in terms of recent models for cancer and passive smoking developed by Darby and Pike, the current meta‐analysis results of overall risk‐ratios of current studies in Tweedie and Mengersen, and misclassification models developed by the United States Environmental Protection Agency (EPA).

Pre-processing for approximate Bayesian computation in image analysis

Publisher: Springer Science and Business Media LLC

Date: 09-12-2014

DOI: 10.1007/S11222-014-9525-6

Evaluation of the pre-posterior distribution of optimized sampling times for the design of pharmacokinetic studies

Publisher: Informa UK Limited

Date: 28-12-2012

DOI: 10.1080/10543406.2010.500065

Abstract: Information theoretic methods are often used to design studies that aim to learn about pharmacokinetic and linked pharmacokinetic-pharmacodynamic systems. These design techniques, such as D-optimality, provide the optimum experimental conditions. The performance of the optimum design will depend on the ability of the investigator to comply with the proposed study conditions. However, in clinical settings it is not possible to comply exactly with the optimum design and hence some degree of unplanned suboptimality occurs due to error in the execution of the study. In addition, due to the nonlinear relationship of the parameters of these models to the data, the designs are also locally dependent on an arbitrary choice of a nominal set of parameter values. A design that is robust to both study conditions and uncertainty in the nominal set of parameter values is likely to be of use clinically. We propose an adaptive design strategy to account for both execution error and uncertainty in the parameter values. In this study we investigate designs for a one-compartment first-order pharmacokinetic model. We do this in a Bayesian framework using Markov-chain Monte Carlo (MCMC) methods. We consider log-normal prior distributions on the parameters and investigate several prior distributions on the s ling times. An adaptive design was used to find the s ling window for the current s ling time conditional on the actual times of all previous s les.

Comparison of three-dimensional profiles over time

Publisher: Informa UK Limited

Date: 07-2012

DOI: 10.1080/02664763.2012.654771

Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome

Publisher: Elsevier BV

Date: 10-2006

DOI: 10.1080/00313020600922488

Abstract: HER-2/neu lification occurs in 15-25% of breast carcinomas. This oncogene, also referred to as c-erbB-2, encodes a transmembrane tyrosine kinase receptor belonging to the epidermal growth factor receptor family. HER-2 over-expression is reported to be associated with a poor prognosis in breast carcinoma patients and in some studies is associated with a poorer response to anti-oestrogen therapy. These patients are less likely to benefit from CMF (cyclophosphamide, methotrexate, fluorouracil)-based chemotherapy compared with anthracycline-based chemotherapy. The aim of this study was to evaluate breast carcinomas to determine hormone receptor status and if there is a difference in breast cancer specific survival for HER-2 positive patients. A total of 591 breast carcinomas were evaluated using immunohistochemistry (IHC) for oestrogen receptor (ERp), progesterone receptor (PRp) and three different HER-2 antibodies (CB11, A0485 and TAB250). Percentage of tumour cells and intensity of staining for ERp were evaluated using a semiquantitative method. Of the 591 tumours, 91 (15.4%) showed 3+ membrane staining for HER-2 with one or more antibodies. Of these 91 tumours, 41 (45.1%) were ERp+/PRp+, seven (7.7%) were ERp+/PR-, six (6.6%) were ERp-/PRp+ and 37 (40.7%) were ERp-/PR-. Of HER-2 positive tumours, 5.5% showed >80% 3+ staining for ERp compared with 31.8% of 0-2+ HER-2 tumours 24.2% of HER-2-positive tumours showed 60% or more cells with 2+ or 3+ staining for ERp. Treatment data were available for 209 patients and no difference was observed in breast cancer specific survival (BCSS) with HER-2 status and tamoxifen. Oestrogen receptor status cannot be used to select tumours for evaluation of HER-2 status, and oestrogen and progesterone receptor positivity does not preclude a positive HER-2 status. There is a higher proportion of ERp negative tumours associated with HER-2 positivity, however, more than 20% of HER-2 positive tumours show moderate or strong staining for ERp. HER-2 positive patients in this study did not show an adverse BCSS with tamoxifen treatment unlike some previous studies.

Simultaneous comparisons with a control and with the “best”

Publisher: Informa UK Limited

Date: 1988

DOI: 10.1080/03610928808829695

Lowering of Intraocular Pressure After Phacoemulsification in Primary Open-Angle and Angle-Closure Glaucoma

Publisher: Asia Pacific Academy of Ophthalmology

Date: 2016

DOI: 10.1097/APO.0000000000000174

Causality and association: The statistical and legal approaches

Publisher: Institute of Mathematical Statistics

Date: 05-2007

DOI: 10.1214/07-STS234

Accurate phenotyping: Reconciling approaches through Bayesian model averaging

Publisher: Public Library of Science (PLoS)

Date: 19-04-2017

DOI: 10.1371/JOURNAL.PONE.0176136

Analysis of aggregated hospital infection data for accountability

Publisher: Elsevier BV

Date: 12-2010

DOI: 10.1016/J.JHIN.2010.06.030

Abstract: Analysis and reporting of among-institution aggregated hospital-acquired infection data are necessary for transparency and accountability. Different analytical methods are required for ensuring transparency and accountability for within-institution sequential analysis. In addition, unbiased summary information is needed for planning and informing the public. We believe that implementation of systems based on evidence is the key to improving institutional performance and safety. This must be accompanied by compliance, outcome audit and sequential analysis of outcome data, e.g. using statistical process control methods. Checklists can be a valuable aid for ensuring implementation of evidence-based systems. Aggregated outcome data analysis for transparency and accountability should concentrate primarily on accurately presenting the outcomes together with their precision. We describe tabulations, funnel plots and random-effects (shrinkage) analysis and avoid comparisons using league tables, star ratings and confidence intervals.

An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

Publisher: Elsevier BV

Date: 02-2010

DOI: 10.1016/J.MARENVRES.2009.07.004

Abstract: Blooms of the cyanobacteria Lyngbya majuscula have occurred for decades around the world. However, with the increase in size and frequency of these blooms, coupled with the toxicity of such algae and their increased biomass, they have become substantial environmental and health issues. It is therefore imperative to develop a better understanding of the scientific and management factors impacting on Lyngbya bloom initiation. This paper suggests an Integrated Bayesian Network (IBN) approach that facilitates the merger of the research being conducted by various parties on Lyngbya. Pivotal to this approach are two Bayesian networks modelling the management and scientific factors of bloom initiation. The research found that Bayesian Networks (BN) and specifically Object Oriented BNs (OOBN) and Dynamic OOBNs facilitate an integrated approach to modelling ecological issues of concern. The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multi-faceted environmental problems.

Rates of convergence of the Hastings and Metropolis algorithms

Publisher: Institute of Mathematical Statistics

Date: 02-1996

DOI: 10.1214/AOS/1033066201

Spatially explicit perceptions of ecosystem services and land cover change in forested regions of Borneo

Publisher: Elsevier BV

Date: 03-2014

DOI: 10.1016/J.ECOSER.2013.11.004

Bayesian neural network learning for prediction in the australian dairy industry

Publisher: Springer Berlin Heidelberg

Date: 1999

DOI: 10.1007/3-540-48412-4_33

Case Studies in Bayesian Statistical Modelling and Analysis

Publisher: Wiley

Date: 30-10-2012

DOI: 10.1002/9781118394472

Bayesian hidden Markov model for DNA sequence segmentation: a prior sensitivity analysis

Publisher: Elsevier BV

Date: 03-2008

DOI: 10.1016/J.CSDA.2008.07.007

Global species richness estimates have not converged

Publisher: Elsevier BV

Date: 04-2014

DOI: 10.1016/J.TREE.2014.02.002

Abstract: We demonstrate that after more than six decades, estimates of global species richness have failed to converge, remain highly uncertain, and in many cases, are logically inconsistent. Convergence in these estimates could be accelerated by adaptive learning methods where the estimation of uncertainty is prioritised and used to guide future research.

Bayesian Spatiotemporal Analysis of Socio-Ecologic Drivers of Ross River Virus Transmission in Queensland, Australia

Publisher: American Society of Tropical Medicine and Hygiene

Date: 09-2010

DOI: 10.4269/AJTMH.2010.09-0551

A Bayesian Approach to Assess Interaction Between Known Risk Factors: The Risk of Lung Cancer from Exposure to Asbestos and Smoking

Publisher: SAGE Publications

Date: 14-08-2008

DOI: 10.1177/0962280206075525

Abstract: We review the literature on the combined effect of asbestos exposure and smoking on lung cancer, and explore a Bayesian approach to assess evidence of interaction. Previous approaches have focussed on separate tests for an additive or multiplicative relation. We extend these approaches by exploring the strength of evidence for either relation using approaches which allow the data to choose between both models. We then compare the different approaches.

Effect of Weather Variability on Seasonal Influenza Among Different Age Groups in Queensland, Australia: A Bayesian Spatiotemporal Analysis

Publisher: Oxford University Press (OUP)

Date: 12-04-2017

DOI: 10.1093/INFDIS/JIX181

Abstract: The effects of weather variability on seasonal influenza among different age groups remain unclear. The comparative study aims to explore the differences in the associations between weather variability and seasonal influenza, and growth rates of seasonal influenza epidemics among different age groups in Queensland, Australia. Three Bayesian spatiotemporal conditional autoregressive models were fitted at the postal area level to quantify the relationships between seasonal influenza and monthly minimum temperature (MIT), monthly vapor pressure, school calendar pattern, and Index of Relative Socio-Economic Advantage and Disadvantage for 3 age groups (<15, 15-64, and ≥65 years). The results showed that the expected decrease in monthly influenza cases was 19.3% (95% credible interval [CI], 14.7%-23.4%), 16.3% (95% CI, 13.6%-19.0%), and 8.5% (95% CI, 1.5%-15.0%) for a 1°C increase in monthly MIT at <15, 15-64, and ≥65 years of age, respectively, while the average increase in the monthly influenza cases was 14.6% (95% CI, 9.0%-21.0%), 12.1% (95% CI, 8.8%-16.1%), and 9.2% (95% CI, 1.4%-16.9%) for a 1-hPa increase in vapor pressure. Weather variability appears to be more influential on seasonal influenza transmission in younger (0-14) age groups. The growth rates of influenza at postal area level were relatively small for older (≥65) age groups in Queensland, Australia.

Spatial analysis of risk factors for transmission of the Barmah Forest virus in Queensland, Australia

Publisher: PAGEPress Publications

Date: 11-2013

DOI: 10.4081/GH.2013.74

Abstract: Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia but few data are available on the risk factors. We assessed the impact of spatial climatic, socioeconomic and ecological factors on the transmission of BFV disease in Queensland, Australia, using spatial regression. All our analyses indicate that spatial lag models provide a superior fit to the data compared to spatial error and ordinary least square models. The residuals of the spatial lag models were found to be uncorrelated, indicating that the models adequately account for spatial and temporal autocorrelation. Our results revealed that minimum temperature, distance from coast and low tide were negatively and rainfall was positively associated with BFV disease in coastal areas, whereas minimum temperature and high tide were negatively and rainfall was positively associated with BFV disease (all P-value.

Novel Method for On-Road Emission Factor Measurements Using a Plume Capture Trailer

Publisher: American Chemical Society (ACS)

Date: 08-12-2007

DOI: 10.1021/ES060179Z

Abstract: The method outlined provides for emission factor measurements to be made for unmodified vehicles driving under real world conditions at minimal cost. The method consists of a plume capture trailer towed behind a test vehicle. The trailer collects a s le of the naturally diluted plume in a 200 L conductive bag and this is delivered immediately to a mobile laboratory for subsequent analysis of particulate and gaseous emissions. The method offers low test turnaround times with the potential to complete much larger numbers of emission factor measurements than have been possible using dynamometer testing. S les can be collected at distances up to 3 m from the exhaust pipe allowing investigation of early dilution processes. Particle size distribution measurements, as well as particle number and mass emission factor measurements, based on naturally diluted plumes are presented. A dilution profile relating the plume dilution ratio to distance from the vehicle tail pipe for a diesel passenger vehicle is also presented. Such profiles are an essential input for new mechanistic roadway air quality models.

Spatial Patterns and Socioecological Drivers of Dengue Fever Transmission in Queensland, Australia

Publisher: Environmental Health Perspectives

Date: 02-2012

DOI: 10.1289/EHP.1003270

Theoretical analysis of the motion and evaporation of exhaled respiratory droplets of mixed composition

Publisher: Elsevier BV

Date: 2011

DOI: 10.1016/J.JAEROSCI.2010.10.005

Confidence limits for ratio of two rates based on likelihood scores: Non-iterative method

Publisher: Wiley

Date: 2003

DOI: 10.1002/SIM.1431

Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Publisher: IWA Publishing

Date: 03-02-2011

DOI: 10.2166/WH.2010.073

Abstract: Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs s ling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species

Publisher: Elsevier BV

Date: 08-2005

DOI: 10.1016/J.ISPRSJPRS.2005.05.002

Spatial modelling of type II diabetes outcomes: a systematic review of approaches used

Publisher: The Royal Society

Date: 06-2015

DOI: 10.1098/RSOS.140460

Abstract: With the rising incidence of type II diabetes mellitus (DM II) worldwide, methods to identify high-risk geographical areas have become increasingly important. In this comprehensive review following Cochrane Collaboration guidelines, we outline spatial methods, outcomes and covariates used in all spatial studies involving outcomes of DM II. A total of 1894 potentially relevant citations were identified. Studies were included if spatial methods were used to explore outcomes of DM II or type I and 2 diabetes combined. Descriptive tables were used to summarize information from included studies. Ten spatial studies conducted in the USA, UK and Europe met selection criteria. Three studies used Bayesian generalized linear mixed modelling (GLMM), three used classic generalized linear modelling, one used classic GLMM, two used geographic information systems mapping tools and one compared case:provider ratios across regions. Spatial studies have been effective in identifying high-risk areas and spatial factors associated with DM II outcomes in the USA, UK and Europe, and would be useful in other parts of the world for allocation of additional services to detect and manage DM II early.

Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference

Publisher: BMJ

Date: 02-2017

DOI: 10.1136/BMJOPEN-2016-012174

Bayesian model averaging for harmful algal bloom prediction

Publisher: Wiley

Date: 10-2009

DOI: 10.1890/08-1843.1

Abstract: Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian model averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate, average monthly minimum temperature, showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilized the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Publisher: Springer Science and Business Media LLC

Date: 14-08-2011

DOI: 10.1007/S10651-010-0152-X

Experimental study of the deposition of combustion aerosols in the human respiratory tract

Publisher: Elsevier BV

Date: 08-2005

DOI: 10.1016/J.JAEROSCI.2005.03.015

Influence of demographic variables on uptake of domestic solar photovoltaic technology

Publisher: Elsevier BV

Date: 2017

DOI: 10.1016/J.RSER.2016.09.009

SELECTING THE t BEST POPULATIONS — SCALE PARAMETER CASE

Publisher: Wiley

Date: 06-1989

DOI: 10.1111/J.1467-842X.1989.TB00399.X

Improved coral population estimation reveals trends at multiple scales on Australia's Great Barrier Reef

Publisher: Springer Science and Business Media LLC

Date: 22-02-2017

DOI: 10.1007/S10021-017-0115-2

Model choice versus model criticism

Publisher: Proceedings of the National Academy of Sciences

Date: 14-01-2010

DOI: 10.1073/PNAS.0911260107

Perfect Samplers for Mixtures of Distributions

Publisher: Oxford University Press (OUP)

Date: 10-2002

DOI: 10.1111/1467-9868.00360

Abstract: We consider the construction of perfect s lers for posterior distributions associated with mixtures of exponential families and conjugate priors, starting with a perfect slice s ler in the spirit of Mira and co-workers. The methods rely on a marginalization akin to Rao–Blackwellization and illustrate the duality principle of Diebolt and Robert. A first approximation embeds the finite support distribution on the latent variables within a continuous support distribution that is easier to simulate by slice s ling, but we later demonstrate that the approximation can be very poor. We conclude by showing that an alternative perfect s ler based on a single backward chain can be constructed. This alternative can handle much larger s le sizes than the slice s ler first proposed.

Confidence limits for the ratio of two rates based on likelihood scores: Non-iterative method

Publisher: Wiley

Date: 2003

DOI: 10.1002/SIM.1405

Abstract: This paper describes a method for creating a confidence interval for the ratio of rates using the score statistic. This non-iterative and easy to apply procedure produces confidence intervals that are suitable for use with Poisson data and simulation results indicate that it is close to the nominal level for a wide range of scenarios.

Rejoinder

Publisher: Institute of Mathematical Statistics

Date: 02-1995

DOI: 10.1214/SS/1177010130

Applying a validation framework to a working airport terminal model

Publisher: Elsevier BV

Date: 07-2014

DOI: 10.1016/J.ESWA.2014.01.013

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Publisher: Elsevier BV

Date: 06-2013

DOI: 10.1016/J.SCITOTENV.2013.03.013

Abstract: Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Kernelized Sparse Bayesian Matrix Factorization

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/TNNLS.2020.2978761

Improving the value of ultrasound in children with suspected appendicitis: a prospective study integrating secondary sonographic signs

Publisher: Korean Society of Ultrasound in Medicine

Date: 2019

DOI: 10.14366/USG.17062

Methodology for assessing exposure and impacts of air pollutants in school children: Data collection, analysis and health effects - A literature review

Publisher: Elsevier BV

Date: 02-2011

DOI: 10.1016/J.ATMOSENV.2010.11.009

Reservicing some customers in M/G/1 queues under three disciplines

Publisher: Hindawi Limited

Date: 2004

DOI: 10.1155/S0161171204210389

Abstract: Consider an M / G / 1 production line in which a production item is failed with some probability and is then repaired. We consider three repair disciplines depending on whether the failed item is repaired immediately or first stockpiled and repaired after all customers in the main queue are served or the stockpile reaches a specified threshold. For each discipline, we find the probability generating function (p.g.f.) of the steady-state size of the system at the moment of departure of the customer in the main queue, the mean busy period, and the probability of the idle period.

A flexible parametric approach to examining spatial variation in relative survival

Publisher: Wiley

Date: 08-08-2016

DOI: 10.1002/SIM.7071

Abstract: Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using in idual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation

Publisher: Springer International Publishing

Date: 2019

DOI: 10.1007/978-3-030-37599-7_40

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data

Publisher: Wiley

Date: 27-01-2020

DOI: 10.1111/ELE.13465

Abstract: Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

Affine projection algorithm for acoustic feedback cancellation using prediction error method in hearing aids

Publisher: IEEE

Date: 09-2016

DOI: 10.1109/IWAENC.2016.7602885

Multivariate meta-analysis

Publisher: Wiley

Date: 2003

DOI: 10.1002/SIM.1410

Abstract: Meta-analysis is now a standard statistical tool for assessing the overall strength and interesting features of a relationship, on the basis of multiple independent studies. There is, however, recent acknowledgement of the fact that in many applications responses are rarely uniquely determined. Hence there has been some change of focus from a single response to the analysis of multiple outcomes. In this paper we propose and evaluate three Bayesian multivariate meta-analysis models: two multivariate analogues of the traditional univariate random effects models which make different assumptions about the relationships between studies and estimates, and a multivariate random effects model which is a Bayesian adaptation of the mixed model approach. Our preferred method is then illustrated through an analysis of a new data set on parental smoking and two health outcomes (asthma and lower respiratory disease) in children.

Bayesian variable selection regression of multivariate responses for group data

Publisher: Institute of Mathematical Statistics

Date: 12-2017

DOI: 10.1214/17-BA1081

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Publisher: Public Library of Science (PLoS)

Date: 13-10-2011

DOI: 10.1371/JOURNAL.PONE.0025688

Spatial variation in cancer incidence and survival over time across Queensland, Australia

Publisher: Elsevier BV

Date: 11-2017

DOI: 10.1016/J.SSTE.2017.09.002

Abstract: Interpreting changes over time in small-area variation in cancer survival, in light of changes in cancer incidence, aids understanding progress in cancer control, yet few space-time analyses have considered both measures. Bayesian space-time hierarchical models were applied to Queensland Cancer Registry data to examine geographical changes in cancer incidence and relative survival over time for the five most common cancers (colorectal, melanoma, lung, breast, prostate) diagnosed during 1997-2004 and 2005-2012 across 516 Queensland residential small-areas. Large variation in both cancer incidence and survival was observed. Survival improvements were fairly consistent across the state, although small for lung cancer. Incidence changes varied by location and cancer type, ranging from lung and colorectal cancers remaining relatively constant over time, to prostate cancer dramatically increasing across the entire state. Reducing disparities in cancer-related outcomes remains a health priority, and space-time modelling of different measures provides an important mechanism by which to monitor progress.

Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis

Publisher: Springer Science and Business Media LLC

Date: 04-07-2014

DOI: 10.1186/1471-2458-14-681

Ozone modifies associations between temperature and cardiovascular mortality: analysis of the NMMAPS data

Publisher: BMJ

Date: 04-2008

DOI: 10.1136/OEM.2007.033878

Abstract: Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified associations between maximum temperature and cardiovascular mortality in the USA. The authors obtained data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) website. They used two time-series Poisson regression models (a response surface model and a stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987-2000 in summer (June to September). Bayesian meta-analysis was used to pool estimates in each community. The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results indicate that ozone positively modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that in general the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10 degrees C increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations in all communities, respectively. Ozone modified temperature effects in different regions in the USA. It is important to evaluate the modifying role of ozone when estimating temperature-related health impacts and to further investigate the reasons behind the regional variability and mechanism for the interaction between temperature and ozone.

Using the Generalised Additive Model to model the particle number count of ultrafine particles

Publisher: Elsevier BV

Date: 10-2011

DOI: 10.1016/J.ATMOSENV.2011.05.004

Epidemiological patterns of Ross River virus disease in Queensland, Australia, 2001-2011

Publisher: American Society of Tropical Medicine and Hygiene

Date: 02-07-2014

DOI: 10.4269/AJTMH.13-0455

Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island

Publisher: Elsevier BV

Date: 06-2009

DOI: 10.1016/J.JENVMAN.2009.03.011

Abstract: Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured s ling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical ex le based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.

Bayesian change point estimation in Poisson-based control charts

Publisher: Springer Science and Business Media LLC

Date: 20-11-2013

DOI: 10.1186/2251-712X-9-32

Virtual reality for conservation

Publisher: ACM

Date: 22-07-2016

DOI: 10.1145/2945292.2945319

Nano/micro structure ammonium cerium nitrate tetrahydrate/carbon nanotube as high performance lithium storage material

Publisher: Elsevier BV

Date: 02-2015

DOI: 10.1016/J.JPOWSOUR.2014.11.044

Bayesian Goodness of Fit Testing with Mixtures of Triangular Distributions

Publisher: Wiley

Date: 27-04-2008

DOI: 10.1111/J.1467-9469.2008.00620.X

Evaluating the dosimetric effect of treatment-induced changed in virally mediated head and neck cancer patients

Publisher: Wiley

Date: 20-11-2013

DOI: 10.1002/JMRS.30

The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

Publisher: Springer Science and Business Media LLC

Date: 19-01-2010

DOI: 10.1007/S00484-009-0294-4

Abstract: This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31 degrees C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4 95% confidence interval: 30.9-47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Publisher: IOP Publishing

Date: 24-03-2014

DOI: 10.1088/1742-6596/489/1/012076

Characteristics of airborne particles and the factors affecting them at bus stations

Publisher: Elsevier BV

Date: 2011

DOI: 10.1016/J.ATMOSENV.2010.10.036

Phase randomisation: numerical study of higher cumulants behaviour

Publisher: Elsevier BV

Date: 10-2001

DOI: 10.1016/S0167-9473(01)00018-4

Robust principal component analysis in water quality index development

Publisher: AIP Publishing LLC

Date: 2014

DOI: 10.1063/1.4882620

On-road ultrafine particle concentration in the M5 East road tunnel, Sydney, Australia

Publisher: Elsevier BV

Date: 07-2009

DOI: 10.1016/J.ATMOSENV.2009.04.029

Machine Learning and Visual Analytics for Consulting Business Decision Support

Publisher: IEEE

Date: 09-2015

DOI: 10.1109/BDVA.2015.7314299

Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions

Publisher: Institute of Mathematical Statistics

Date: 03-2014

DOI: 10.1214/13-AOAS678

Socioeconomic status and infant mortality in Australia: A national study of small urban areas, 1985-89

Publisher: Elsevier BV

Date: 05-2000

DOI: 10.1016/S0277-9536(99)00300-7

Abstract: This study uses small-area data for the period 1985-89 to examine the relationship between socioeconomic status and infant mortality in each of the mainland State capital cities of Australia. An unweighted OLS regression analysis based on 195 Statistical Local Areas (SLAs) that recorded five or more deaths over the reference period shows that standardised infant mortality ratios were significantly higher in areas with greater concentrations of low income families. This relationship was independent of the effects of low birthweight, Aboriginality, ethnicity and variability between each of the capital cities. To test for the robustness of this result a sensitivity analysis was undertaken. This involved (a) performing a Principal Components Analysis on a wide range of sociodemographic variables to derive factor scales that were subsequently included in a regression analysis, (b) using weighted least-squares regression and a Poisson generalised linear model and (c) including in the analysis all SLAs irrespective of the number of infant deaths. The sensitivity analysis supported the results of this study, thus validating the observed association between the socioeconomic characteristics of urban areas and their rate of infant mortality. Despite marked reductions in overall rates of infant mortality over the last century in Australia. socioeconomic disparities were still evident during the mid-to-late 1980s. Whether and to what extent this situation persisted during the early-to-mid 1990s will be known in the near future when the next collection of area-based data are publicly released. The results of this study, therefore, represent an important baseline against which more contemporary national trends can be monitored.

Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study

Publisher: Public Library of Science (PLoS)

Date: 07-09-2016

DOI: 10.1371/JOURNAL.PONE.0162489

Projecting future transmission of malaria under climate change scenarios : challenges and research needs

Publisher: Informa UK Limited

Date: 31-12-2014

DOI: 10.1080/10643389.2013.852392

Identification of area-level influences on regions of high cancer incidence in Queensland, Australia: A classification tree approach

Publisher: Springer Science and Business Media LLC

Date: 24-07-2011

DOI: 10.1186/1471-2407-11-311

Risk Profiles for Sensorineural Hearing Loss in Patients with Head and Neck Cancer Receiving Cisplatin-based Chemoradiation

Publisher: Elsevier BV

Date: 03-2017

DOI: 10.1016/J.JMIR.2016.11.005

Abstract: Sensorineural hearing loss (SNHL) is a significant toxicity experienced by some patients undergoing cisplatin-based chemoradiation therapy for head and neck cancer. Therefore, SNHL risk profiles were created based on demographics, hearing thresholds, and treatment parameters. Thirty-eight patients with squamous cell carcinoma of the head and neck, treated with postoperative or definitive cisplatin-based chemoradiation at the Princess Alexandra Hospital between 2010 and 2013, were retrospectively reviewed. Patients with pretreatment otologic problems were excluded. Regression models analysed the contributions of collected variables. All patients (100%) received multiple audiological assessments, with 21 (55.3%) receiving baseline assessment. The mean hearing deterioration at pure-tone average 1-2-4 kHz was mild (range 22.4-27.6 dB). However, clinically significant SNHL was evident in 37 (97.3%), 24 (63.2%), and 14 (36.8%) patients at 8 kHz and pure-tone averages 0.5-1-2 kHz and 1-2-4 kHz, respectively. Principal component analysis indicated two profiles: (1) low or medium frequency SNHL and (2) high-frequency SNHL. Multivariate analysis demonstrated tobacco consumption (ρ < 0.006) and alcohol intake (ρ < 0.08) predicted high-frequency SNHL (F(3,33) = 3.59, ρ < 0.02, R Although hearing loss rates may be under reported without routine audiological assessment, the incidence of cisplatin-based chemoradiation-induced SNHL, in this study, is high. The proposed predictive model can be used as a prognostic tool and potentially mitigate adverse outcomes.

Fast Bayesian analysis of spatial dynamic factor models for multitemporal remotely sensed imagery

Publisher: Oxford University Press (OUP)

Date: 26-10-2010

DOI: 10.1111/J.1467-9876.2010.00739.X

Abstract: Remote sensing is one ex le where data sets that vary across space and time have become so large that ‘standard’ approaches employed by statistical modellers for applied analysis are no longer feasible. We present a Bayesian methodology, which makes use of recently developed algorithms in applied mathematics, for the analysis of large space–time data sets. In particular, a Markov chain Monte Carlo algorithm is proposed for the efficient estimation of spatial dynamic factor models. The spatial dynamic factor model is specified whereby spatial dependence is modelled though the columns of the factor loadings matrix by using a Gaussian Markov random field. Krylov subspace methods are used to take advantage of the sparse matrix structures that are inherent in the model. The methodology is used to analyse remotely sensed data from the Moderate Imaging Spectroradiometer satellite. In particular, the methodology proposed is used in conjunction with high resolution imagery for the classification, in terms of land type, of two regions in central Queensland, Australia.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

Publisher: IOP Publishing

Date: 24-03-2014

DOI: 10.1088/1742-6596/489/1/012074

Confidence limits for the ratio of two rates based on likelihood scores: Non-iterative methods (multiple letters)

Publisher: Wiley

Date: 2005

DOI: 10.1002/SIM.1992

The Bayesian conditional independence model for measurement error: applications in ecology

Publisher: Springer Science and Business Media LLC

Date: 26-01-2011

DOI: 10.1007/S10651-009-0130-3

An external field prior for the hidden Potts model with application to cone-beam computed tomography

Publisher: Elsevier BV

Date: 06-2015

DOI: 10.1016/J.CSDA.2014.12.001

Assessing The Combined Effect of Asbestos Exposure And Smoking on Lung Cancer: A Bayesian Approach

Publisher: Wiley

Date: 16-06-2007

DOI: 10.1002/SIM.2602

Abstract: We review the literature on the combined association between lung cancer and two environmental exposures, asbestos exposure and smoking, and explore a Bayesian approach to assess evidence of interaction between the exposures. The meta-analysis combines separate indices of additive and multiplicative relationships and multivariate relative risk estimates. By making inferences on posterior probabilities we can explore both the form and strength of interaction. This analysis may be more informative than providing evidence to support one relation over another on the basis of statistical significance. Overall, we find evidence for a more than additive and less than multiplicative relation.

Bayesian Spatial Analysis for the Evaluation of Breast Cancer Detection Methods

Publisher: Wiley

Date: 12-2013

DOI: 10.1111/ANZS.12059

Temperature, air pollution and total mortality during summers in Sydney, 1994–2004

Publisher: Springer Science and Business Media LLC

Date: 28-05-2008

DOI: 10.1007/S00484-008-0161-8

Abstract: This study investigated the effect of temperature and air pollutants on total mortality in summers in Sydney, Australia. Daily data on weather variables, mortality and air pollution for the Sydney metropolitan area from 1 January 1994 to 31 December 2004 were supplied by Australian Bureau of Meteorology, Australian Bureau of Statistics, and Environment Protection Agency of New South Wales, respectively. We examined the association of total mortality with weather indicators and air pollution using generalised additive models (GAMs). A time-series classification and regression tree (CART) model was developed to explore the interaction effects of temperature and air pollution that impacted on mortality. Our results show that the average increase in total daily mortality was 0.9% [95% confidence interval (CI): 0.6-1.3%] and 22% (95% CI: 6.4-40.5%) for a 1 degrees C increase in daily maximum temperature and 1 part per hundred million (pphm) increase in daily average concentration of sulphur dioxide (SO(2)), respectively. Time-series CART results show that maximum temperature and SO(2) on the current day had significant interaction effects on total mortality. There were 7.3% and 12.1% increases in daily average mortality when maximum temperature was over 32 degrees C and mean SO(2) exceeded 0.315 pphm, respectively. Daily maximum temperature was statistically significantly associated with daily deaths in Sydney during summers between 1994 and 2004. Elevated daily maximum temperature combined with high SO(2) concentrations appeared to have contributed to the increased mortality observed in Sydney during this period.

Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology

Publisher: Wiley

Date: 21-02-2023

DOI: 10.1111/INSR.12542

Abstract: Crowdsourcing methods facilitate the production of scientific information by non‐experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data‐driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data.

A framework for model integration and holistic modelling of socio-technical systems

Publisher: Elsevier BV

Date: 03-2015

DOI: 10.1016/J.DSS.2015.01.006

Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models

Publisher: Springer Science and Business Media LLC

Date: 2007

DOI: 10.1186/1476-072X-6-54

Principles of Experimental Design for Big Data Analysis

Publisher: Institute of Mathematical Statistics

Date: 08-2017

DOI: 10.1214/16-STS604

MODELLING OF TRANSITIONS BETWEEN EMPLOYMENT STATES FOR YOUNG AUSTRALIANS

Publisher: Wiley

Date: 08-1989

DOI: 10.1111/J.1467-842X.1989.TB00979.X

Confidence bounds and selection of the t best populations

Publisher: Informa UK Limited

Date: 1988

DOI: 10.1080/03610918808812705

Biosecurity surveillance: quantitative approaches

Publisher: CABI

Date: 2015

DOI: 10.1079/9781780643595.0000

Abstract: Biosecurity surveillance plays a vital role in protection against the introduction and spread of unwanted plants and animals. It involves not just collecting relevant information, but also analysing this information. This book focuses on methods for quantitative analysis of biosecurity surveillance data, where these data might arise from observations, sensors, remote imaging, expert opinion and so on. As emphasized in the Introduction, there is a need for exposition of these methods in the context of real world problems. The book, which is structured in three parts, is therefore focused on the practical application of quantitative methods for biosecurity surveillance. Part I presents the concepts for biosecurity surveillance Part II discusses information for biosecurity surveillance and Part III explores statistical modelling methods for designing biosecurity surveillance. Part I supports the later parts of the book, by providing a foundation for describing the statistical modelling methods presented, as well as placing later chapters in the broader international context. The chapters in Parts II and III describe methods and supporting case studies that demonstrate and/or implement the techniques, tools and methods described. It is anticipated that the book will be a resource for researchers and students in this field and in related disciplines, as well as practitioners who are engaged in the practice of biosecurity surveillance.

Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel-contaminated soil

Publisher: No publisher found

Date: 2015

DOI: 10.1002/ECE3.1493

Risk Modelling in Quality Clinical Registries: Monitoring Lesion Treatment Failure Rate in Percutaneous Coronary Interventions

Publisher: Elsevier BV

Date: 03-2013

DOI: 10.1016/J.HLC.2012.10.001

Abstract: This paper describes the development of a risk adjustment (RA) model predictive of in idual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program. Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n=3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p<0.001), occlusion type (p<0.001), patient age (p=0.001), vessel system (p<0.04), vessel diameter (p<0.001), unstable angina (p=0.003) and presence of major cardiac risk factors (p=0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer-Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated. A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.

Small area estimation of sparse disease counts using shared component models-application to birth defect registry data in New South Wales, Australia

Publisher: Elsevier BV

Date: 07-2010

DOI: 10.1016/J.HEALTHPLACE.2010.02.006

Abstract: In the field of disease mapping, little has been done to address the issue of analysing sparse health datasets. We hypothesised that by modelling two outcomes simultaneously, one would be able to better estimate the outcome with a sparse count. We tested this hypothesis utilising Bayesian models, studying both birth defects and caesarean sections using data from two large, linked birth registries in New South Wales from 1990 to 2004. We compared four spatial models across seven birth defects: spina bifida, ventricular septal defect, OS atrial septal defect, patent ductus arteriosus, cleft lip and or palate, trisomy 21 and hypospadias. For three of the birth defects, the shared component model with a zero-inflated Poisson (ZIP) extension performed better than other simpler models, having a lower deviance information criteria (DIC). With spina bifida, the ratio of relative risk associated with the shared component was 2.82 (95% CI: 1.46-5.67). We found that shared component models are potentially beneficial, but only if there is a reasonably strong spatial correlation in effect for the study and referent outcomes.

Evaluation of coronary angiographic projections to balance the clinical yield with the radiation risk

Publisher: British Institute of Radiology

Date: 09-2012

DOI: 10.1259/BJR/79460007

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Publisher: Wiley

Date: 10-08-2017

DOI: 10.1111/COBI.12950

Abstract: Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease-driven declines of hibians associated with hibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious-disease systems.

Daily average temperature and mortality among the elderly : a meta-analysis and systematic review of epidemiological evidence

Publisher: Springer Science and Business Media LLC

Date: 06-10-2011

DOI: 10.1007/S00484-011-0497-3

Abstract: The impact of climate change on the health of vulnerable groups such as the elderly has been of increasing concern. However, to date there has been no meta-analysis of current literature relating to the effects of temperature fluctuations upon mortality amongst the elderly. We synthesised risk estimates of the overall impact of daily mean temperature on elderly mortality across different continents. A comprehensive literature search was conducted using MEDLINE and PubMed to identify papers published up to December 2010. Selection criteria including suitable temperature indicators, endpoints, study-designs and identification of threshold were used. A two-stage Bayesian hierarchical model was performed to summarise the percent increase in mortality with a 1°C temperature increase (or decrease) with 95% confidence intervals in hot (or cold) days, with lagged effects also measured. Fifteen studies met the eligibility criteria and almost 13 million elderly deaths were included in this meta-analysis. In total, there was a 2-5% increase for a 1°C increment during hot temperature intervals, and a 1-2 % increase in all-cause mortality for a 1°C decrease during cold temperature intervals. Lags of up to 9 days in exposure to cold temperature intervals were substantially associated with all-cause mortality, but no substantial lagged effects were observed for hot intervals. Thus, both hot and cold temperatures substantially increased mortality among the elderly, but the magnitude of heat-related effects seemed to be larger than that of cold effects within a global context.

Modeling the viability of the free‐ranging cheetah population in Namibia: an object‐oriented Bayesian network approach

Publisher: Wiley

Date: 07-2013

DOI: 10.1890/ES12-00357.1

A new method for calculating the volume of primary tissue types in live sheep using computed tomography scanning

Publisher: CSIRO Publishing

Date: 2009

DOI: 10.1071/AN09038

Abstract: Interest in the use of computed tomography (CT) scanning in animal experimentation has increased markedly over the last decade due to the benefits of studying tissue in live subjects over time. In these experiments, the non-carcass components of the scan are removed from the collected data, allowing scientists to study the carcass of a live animal without the need to slaughter the in idual. However, there is not yet a consensus regarding the most appropriate manner in which to convert the CT numbers into a meaningful estimate of area, volume or proportion of tissue present in a carcass at the time of scanning. In this paper we use a Bayesian mixture model to estimate the area of each of three tissue types of interest, fat, muscle and bone present in in idual CT scan slices. We then use the Cavalieri principle to estimate the volume and proportion of the carcass attributable to each of these tissues. The approach is validated by analysis of experimental sheep carcasses.

Forecasting the Future Risk of Barmah Forest Virus Disease under Climate Change Scenarios in Queensland, Australia

Publisher: Public Library of Science (PLoS)

Date: 15-05-2013

DOI: 10.1371/JOURNAL.PONE.0062843

Characterising uncertainty in expert assessments: Encoding heavily skewed judgements

Publisher: Public Library of Science (PLoS)

Date: 30-10-2015

DOI: 10.1371/JOURNAL.PONE.0141697

Environmental decision-making using Bayesian networks: creating an environmental report card

Publisher: Wiley

Date: 23-09-2017

DOI: 10.1002/ASMB.2190

Nitrous oxide emissions from subtropical horticultural soils: a time series analysis

Publisher: CSIRO Publishing

Date: 2012

DOI: 10.1071/SR11100

Abstract: Time series regression models were used to examine the influence of environmental factors (soil water content and soil temperature) on the emissions of nitrous oxide (N2O) from subtropical soils, by taking into account temporal lagged environmental factors, autoregressive processes, and seasonality for three horticultural crops in a subtropical region of Australia. Fluxes of N2O, soil water content, and soil temperature were determined simultaneously on a weekly basis over a 12-month period in South East Queensland. Annual N2O emissions for soils under mango, pineapple, and custard apple were 1590, 1156, and 2038 g N2O-N/ha, respectively, with most emissions attributed to nitrification. The N2O-N emitted from the pineapple and custard apple crops was equivalent to 0.26 and 2.22%, respectively, of the applied mineral N. The change in soil water content was the key variable for describing N2O emissions at the weekly time-scale, with soil temperature at a lag of 1 month having a significant influence on average N2O emissions (averaged) at the monthly time-scale across the three crops. After accounting for soil temperature and soil water content, both the weekly and monthly time series regression models exhibited significant autocorrelation at lags of 1–2 weeks and 1–2 months, and significant seasonality for weekly N2O emissions for mango crop and for monthly N2O emissions for mango and custard apple crops in this location over this time-frame. Time series regression models can explain a higher percentage of the temporal variation of N2O emission compared with simple regression models using soil temperature and soil water content as drivers. Taking into account seasonal variability and temporal persistence in N2O emissions associated with soil water content and soil temperature may lead to a reduction in the uncertainty surrounding estimates of N2O emissions based on limited s ling effort.

Bayesian network for risk of diarrhea associated with the use of recycled water

Publisher: Wiley

Date: 12-2009

DOI: 10.1111/J.1539-6924.2009.01301.X

Abstract: Estimating potential health risks associated with recycled (reused) water is highly complex given the multiple factors affecting water quality. We take a conceptual model, which represents the factors and pathways by which recycled water may pose a risk of contracting gastroenteritis, convert the conceptual model to a Bayesian net, and quantify the model using one expert's opinion. This allows us to make various predictions as to the risks posed under various scenarios. Bayesian nets provide an additional way of modeling the determinants of recycled water quality and elucidating their relative influence on a given disease outcome. The important contribution to Bayesian net methodology is that all model predictions, whether risk or relative risk estimates, are expressed as credible intervals.

The effect of housing characteristics and occupant activities on the respiratory health of women and children in Lao PDR

Publisher: Elsevier BV

Date: 03-2011

DOI: 10.1016/J.SCITOTENV.2011.01.016

Abstract: The paper presents the results of a study conducted into the relationship between dwelling characteristics and occupant activities with the respiratory health of resident women and children in Lao People's Democratic Republic (PDR). Lao is one of the least developed countries in south-east Asia with poor life expectancies and mortality rates. The study, commissioned by the World Health Organisation, included questionnaires delivered to residents of 356 dwellings in nine Districts in Lao PDR over a five month period (December 2005-April 2006), with the aim of identifying the association between respiratory health and indoor air pollution, in particular exposures related to indoor biomass burning. Adjusted odds ratios were calculated for each health outcome separately using binary logistic regression. After adjusting for age, a wide range of symptoms of respiratory illness in women and children aged 1-4 years were positively associated with a range of indoor exposures related to indoor cooking, including exposure to a fire and location of the cooking place. Among women, "dust always inside the house" and smoking were also identified as strong risk factors for respiratory illness. Other strong risk factors for children, after adjusting for age and gender, included dust and drying clothes inside. This analysis confirms the role of indoor air pollution in the burden of disease among women and children in Lao PDR.

Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia

Publisher: Public Library of Science (PLoS)

Date: 30-08-2017

DOI: 10.1371/JOURNAL.PONE.0183653

Calculation of IBD probabilities with dense SNP or sequence data

Publisher: Wiley

Date: 09-2008

DOI: 10.1002/GEPI.20324

Abstract: The probabilities that two in iduals share 0, 1, or 2 alleles identical by descent (IBD) at a given genotyped marker locus are quantities of fundamental importance for disease gene and quantitative trait mapping and in family-based tests of association. Until recently, genotyped markers were sufficiently sparse that founder haplotypes could be modelled as having been drawn from a population in linkage equilibrium for the purpose of estimating IBD probabilities. However, with the advent of high-throughput single nucleotide polymorphism genotyping assays, this is no longer a reasonable assumption. Indeed, the imminent arrival of in idual sequencing will enable high-density single nucleotide polymorphism genotyping on a scale for which current algorithms are not equipped. In this paper, we present a simple new model in which founder haplotypes are modelled as a Markov chain. Another important innovation is that genotyping errors are explicitly incorporated into the model. We compare results obtained using the new model to those obtained using the popular genetic linkage analysis package Merlin, with and without using the cluster model of linkage disequilibrium that is incorporated into that program. We find that the new model results in accuracy approaching that of Merlin with haplotype blocks, but achieves this with orders of magnitude faster run times. Moreover, the new algorithm scales linearly with number of markers, irrespective of density, whereas Merlin scales supralinearly. We also confirm a previous finding that ignoring linkage disequilibrium in founder haplotypes can cause errors in the calculation of IBD probabilities.

Asymptotic behaviour of the posterior distribution in overfitted mixture models

Publisher: Oxford University Press (OUP)

Date: 09-08-2011

DOI: 10.1111/J.1467-9868.2011.00781.X

Abstract: We study the asymptotic behaviour of the posterior distribution in a mixture model when the number of components in the mixture is larger than the true number of components: a situation which is commonly referred to as an overfitted mixture. We prove in particular that quite generally the posterior distribution has a stable and interesting behaviour, since it tends to empty the extra components. This stability is achieved under some restriction on the prior, which can be used as a guideline for choosing the prior. Some simulations are presented to illustrate this behaviour.

Elicitator: An expert elicitation tool for regression in ecology

Publisher: Elsevier BV

Date: 2010

DOI: 10.1016/J.ENVSOFT.2009.07.003

Rejoinder

Publisher: Institute of Mathematical Statistics

Date: 09-2012

DOI: 10.1214/12-BA717REJ

Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallabyPetrogale penicillata

Publisher: Wiley

Date: 06-2009

DOI: 10.1002/ENV.935

Predicting Health Programme Participation: A Gravity-Based, Hierarchical Modelling Approach

Publisher: Oxford University Press (OUP)

Date: 10-08-2015

DOI: 10.1111/RSSC.12111

Abstract: Statistical analyses of health programme participation seek to address a number of objectives that are compatible with the evaluation of demand for current resources. In this spirit, a spatial hierarchical model is developed for disentangling patterns in participation at the small area level, as a function of population-based demand and additional variation. For the former, a constrained gravity model is proposed to quantify factors associated with spatial choice and to account for competition effects, for programmes delivered by multiple clinics. The implications of gravity model misspecification within a mixed effects framework are also explored. The model proposed is applied to participation data from a no-fee mammography programme in Brisbane, Australia. Attention is paid to the interpretation of various model outputs and their relevance for public health policy.

Inference for proportions in a 2 x 2 contingency table: HPD or not HPD?

Publisher: No publisher found

Date: 2008

DOI: 10.1111/J.1541-0420.2008.01134\_1.X

Meta‐analysis of variation: ecological and evolutionary applications and beyond

Publisher: Wiley

Date: 17-12-2014

DOI: 10.1111/2041-210X.12309

Abstract: Meta‐analysis has become a standard way of summarizing empirical studies in many fields, including ecology and evolution. In ecology and evolution, meta‐analyses comparing two groups (usually experimental and control groups) have almost exclusively focused on comparing the means, using standardized metrics such as Cohen's / Hedges’ d or the response ratio. However, an experimental treatment may not only affect the mean but also the variance. Investigating differences in the variance between two groups may be informative, especially when a treatment influences the variance in addition to or instead of the mean. In this paper, we propose the effect size statistic ln CVR (the natural logarithm of the ratio between the coefficients of variation, CV, from two groups), which enables us to meta‐analytically compare differences between the variability of two groups. We illustrate the use of lnCVR with ex les from ecology and evolution. Further, as an alternative approach to the use of lnCVR, we propose the combined use of ln s (the log standard deviation) and (the log mean) in a hierarchical (linear mixed) model. The use of ln s with overcomes potential limitations of lnCVR and it provides a more flexible, albeit more complex, way to examine variation beyond two‐group comparisons. Relevantly, we also refer to the potential use of ln s and lnCV (the log CV) in the context of comparative analysis. Our approaches to compare variability could be applied to already published meta‐analytic data sets that compare two‐group means to uncover potentially overlooked effects on the variance. Additionally, our approaches should be applied to future meta‐analyses, especially when one suspects a treatment has an effect not only on the mean, but also on the variance. Notably, the application of the proposed methods extends beyond the fields of ecology and evolution.

Classification based upon gene expression data: Bias and precision of error rates

Publisher: Oxford University Press (OUP)

Date: 28-03-2007

DOI: 10.1093/BIOINFORMATICS/BTM117

Abstract: Motivation: Gene expression data offer a large number of potentially useful predictors for the classification of tissue s les into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, s ling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Results: Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3–5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two ex le datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. Availability: R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from www.maths.qut.edu.au rofiles/wood ermr.jsp Contact: i.wood@qut.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Change point detection in risk adjusted control charts

Publisher: SAGE Publications

Date: 23-10-2011

DOI: 10.1177/0962280211426356

Abstract: Precise identification of the time when a change in a clinical process has occurred enables experts to identify a potential special cause more effectively. In this article, we develop change point estimation methods for a clinical dichotomous process in the presence of case mix. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the odds ratio and logit of risk of a Bernoulli process. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted CUSUM and EWMA control charts. In comparison with alternative EWMA and CUSUM estimators, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities enhance when probability quantification, flexibility and generaliability of the Bayesian change point detection model are also considered. The Deviance Information Criterion, as a model selection criterion in the Bayesian context, is applied to find the best change point model for a given dataset where there is no prior knowledge about the change type in the process.

An improved method for calculating toxicity-based pollutant loads: Part 1. method development

Publisher: Wiley

Date: 04-12-2017

DOI: 10.1002/IEAM.1854

Abstract: Pollutant loads are a means for assessing regulatory compliance and setting targets to reduce pollution entering receiving waterbodies. However, a pollutant load is often comprised of multiple chemicals, which may exert joint toxicity on biota. When the ultimate goal for assessing pollutant loads is to protect ecosystems from adverse effects of toxicants, then the total pollutant load needs to be calculated based on the principles of mixture toxicology. In this article, an improved method is proposed to convert a pollutant load to a toxicity-based load (toxic load) using a modified toxic equivalency factor (TEF) derivation method. The method uses the relative potencies (RePs) of multiple species to represent the response of the ecological community. The TEF is calculated from a percentile of a cumulative distribution function (CDF) fitted to the RePs. The improvements permit the determination of which percentile of the CDF generates the most environmentally relevant and robust toxic loads. That is, environmental relevance ensures that a reduction in the toxic load is likely to result in a corresponding improvement in ecosystem health and robustness ensures that the calculation of the toxic loads is not biased by the reference chemical used. The improved methodology will therefore ensure that correct management decisions will be made and ultimately, a reduction in the toxic load will lead to a commensurate improvement in water quality. Integr Environ Assess Manag 2017 :746-753. © 2016 SETAC.

Letters to the editor

Publisher: Informa UK Limited

Date: 05-2009

DOI: 10.1198/TAST.2009.0037

Zero-tolerance biosecurity protects high-conservation-value island nature reserve

Publisher: Springer Science and Business Media LLC

Date: 10-04-2017

DOI: 10.1038/S41598-017-00450-Y

Abstract: Barrow Island, north-west coast of Australia, is one of the world’s significant conservation areas, harboring marsupials that have become extinct or threatened on mainland Australia as well as a rich ersity of plants and animals, some endemic. Access to construct a Liquefied Natural Gas (LNG) plant, Australia’s largest infrastructure development, on the island was conditional on no non-indigenous species (NIS) becoming established. We developed a comprehensive biosecurity system to protect the island’s bio ersity. From 2009 to 2015 more than 0.5 million passengers and 12.2 million tonnes of freight were transported to the island under the biosecurity system, requiring 1.5 million hrs of inspections. No establishments of NIS were detected. We made four observations that will assist development of biosecurity systems. Firstly, the frequency of detections of organisms corresponded best to a mixture log-normal distribution including the high number of zero inspections and extreme values involving rare incursions. Secondly, comprehensive knowledge of the island’s biota allowed estimation of false positive detections (62% native species). Thirdly, detections at the border did not predict incursions on the island. Fourthly, the workforce detected more than half post-border incursions (59%). Similar approaches can and should be implemented for all areas of significant conservation value.

Pollutant concentrations within households in Lao PDR and association with housing characteristics and occupants' activities

Publisher: American Chemical Society (ACS)

Date: 20-12-2011

DOI: 10.1021/ES102294V

Abstract: The paper presents the results of a study conducted to investigate indoor air quality within residential dwellings in Lao PDR. Results from PM(10), CO, and NO(2) measurements inside 167 dwellings in Lao PDR over a five month period (December 2005-April 2006) are discussed as a function of household characteristics and occupant activities. Extremely high PM(10) and NO(2) concentrations (12 h mean PM(10) concentrations 1275 ± 98 μg m(-3) and 1183 ± 99 μg m(-3) in Vientiane and Bolikhamxay provinces, respectively 12 h mean NO(2) concentrations 1210 ± 94 μg m(-3) and 561 ± 45 μg m(-3) in Vientiane and Bolikhamxay, respectively) were measured within the dwellings. Correlations, ANOVA analysis (univariate and multivariate), and linear regression results suggest a substantial contribution from cooking and smoking. The PM(10) concentrations were significantly higher in houses without a chimney compared to houses in which cooking occurred on a stove with a chimney. However, no significant differences in pollutant concentrations were observed as a function of cooking location. Furthermore, PM(10) and NO(2) concentrations were higher in houses in which smoking occurred, suggestive of a relationship between increased indoor concentrations and smoking (0.05 < p < 0.10). Resuspension of dust from soil floors was another significant source of PM(10) inside the house (634 μg m(-3), p < 0.05).

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

Publisher: Springer Science and Business Media LLC

Date: 05-11-2015

DOI: 10.1038/SREP16105

Abstract: Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects.

An intuitive dashboard for bayesian network inference

Publisher: IOP Publishing

Date: 11-03-2014

DOI: 10.1088/1742-6596/490/1/012023

Potential of Bayesian networks for adaptive management in water recycling

Publisher: Elsevier BV

Date: 05-2017

DOI: 10.1016/J.ENVSOFT.2017.01.016

Understanding the reliability of citizen science observational data using item response models

Publisher: Wiley

Date: 19-05-2021

DOI: 10.1111/2041-210X.13623

Abstract: Citizen science projects have become increasingly popular in many fields, including ecology. However, the quality of this information is frequently debated within the scientific community. Modern citizen science implementations therefore require measures of the users' proficiency. We introduce a new methodological framework of item response that quantifies a citizen scientist's ability, taking into account the difficulty of the task. We focus on citizen science programs involving the classification of images. Our approach accommodates spatial autocorrelation within the item difficulties, and provides deeper insights and relevant ecological measures of species and site‐related difficulties, discriminatory power and guessing behaviour. The identification of very capable versus less skilled participants can facilitate selective use of data in analyses and more targeted training programs for citizen scientists. This paper also addresses challenges in fitting such models to very large datasets. We found that the suggested methods outperform the traditional item response models in terms of RMSE, accuracy and WAIC, based on leave‐one‐out cross‐validation on simulated and empirical data. We present a comprehensive implementation using a case study of species identification in the Serengeti, Tanzania. The R and Stan codes are provided for full reproducibility. Multiple statistical illustrations and visualizations are given, which allow extrapolation to a wide range of citizen science ecological problems.

Spatial and temporal patterns of locally-acquired dengue transmission in Northern Queensland, Australia, 1993-2012

Publisher: Public Library of Science (PLoS)

Date: 04-2014

DOI: 10.1371/JOURNAL.PONE.0092524

Spatially varying coefficient inequalities: Evaluating how the impact of patient characteristics on breast cancer survival varies by location

Publisher: Public Library of Science (PLoS)

Date: 05-05-2016

DOI: 10.1371/JOURNAL.PONE.0155086

Elicitator : a user-friendly, interactive tool to support scenario-based elicitation of expert knowledge

Publisher: No publisher found

Date: 2011

DOI: 10.1007/978-1-4614-1034-8\_3

Developing the atlas of cancer in Queensland: Methodological issues

Publisher: Springer Science and Business Media LLC

Date: 2011

DOI: 10.1186/1476-072X-10-9

Bayesian hidden Markov models in DNA sequence segmentation using R: the case of Simian Vacuolating virus (SV40)

Publisher: Informa UK Limited

Date: 29-06-2017

DOI: 10.1080/00949655.2017.1344666

Using decision trees to understand structure in missing data

Publisher: BMJ

Date: 06-2015

DOI: 10.1136/BMJOPEN-2014-007450

Does geographic location impact the survival differential between screen- and interval-detected breast cancers?

Publisher: Springer Science and Business Media LLC

Date: 07-03-2016

DOI: 10.1007/S00477-015-1050-4

A Bayesian Solution to Reconstructing Centrally Censored Distributions

Publisher: Springer Science and Business Media LLC

Date: 03-2005

DOI: 10.1198/108571105X28697

Environmental tobacco smoke and ischaemic heart disease: A case study in applying causal criteria

Publisher: Springer Science and Business Media LLC

Date: 11-05-1999

DOI: 10.1007/PL00014202

Abstract: Whether ischaemic heart disease (IHD) is caused by exposure to environmental tobacco smoke (ETS), commonly known as "passive smoking", has been debated from both epidemiological and biological perspectives. In this paper we use Bradford Hill criteria to synthesize results from the biological and epidemiological literature in a formal assessment of the strength of support for such a relationship. Although we find that these criteria, designed for clinical trials, do not give an ideal framework for assessment of epidemiological and biological studies, nevertheless they do provide systematic guidance for this assessment. For the general population, of the nine tests proposed by Hill we find that one (biological plausibility) seems to be supported, though not unarguably three (strength, consistency. specificity) appear to fail by accepted standards and the remaining five have insufficient data for a clear evaluation (biological gradient, experimental evidence, temporality, coherence, analogy). Overall, this provides at best weak support for a causal association between ETS and IHD across the general community. Conversely, there appears to be more support, especially in the biology studies, for an association between ETS and IHD for those with preexisting disease, although epidemiological studies are limited in this area. One of the outcomes of this review is the identification of areas of focus for future epidemiological and biological research. First, we find that stronger associations may be found in the particular subpopulation with pre-existing IHD. In this case, more convincing biological plausibility and experimental evidence indicate a need for relevant epidemiological studies, although in idual responses are very variable. Second, we identify the need for further, more detailed evaluations of the nature of vessel wall thickenings occurring in experimental models of ETS exposure. Third, we propose long-term animal studies of initiation of IHD, including direct assessment of effects on the accumulation of lipid in vessel walls, at appropriate ETS exposure levels.

Methods for identifying SNP interactions: A review on variations of logic regression, Random Forest and bayesian logistic regression

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 11-2011

DOI: 10.1109/TCBB.2011.46

What is the value of hospital mortality indicators, and are there ways to do better?

Publisher: CSIRO Publishing

Date: 2012

DOI: 10.1071/AH11132

Abstract: Monitoring hospital performance using patient safety indicators is one of the key components of healthcare reform in Australia. Mortality indicators, including the hospital standardised mortality ratio and deaths in low mortality diagnosis reference groups have been included in the core national hospital-based outcome indicator set recommended for local generation and review and public reporting. Although the face validity of mortality indicators such as these is high, an increasing number of studies have demonstrated that there are concerns regarding their internal, construct and criterion validity. Use of indicators with poor validity has the consequence of potentially incorrectly classifying hospitals as performance outliers and expenditure of limited hospital staff time on activities which may provide no gain to hospital quality and safety and may in fact cause damage to morale. This paper reviews the limitations of current approaches to monitoring hospital quality and safety performance using mortality indicators. It is argued that there are better approaches to improving performance than monitoring with mortality indicators generated from hospital administrative data. These approaches include use of epidemiologically sound, clinically relevant data from clinical-quality registries, better systems of audit, evidence-based bundles, checklists, simulators and application of the science of complex systems. What is known about the topic? Public reporting of adverse events such as hospital standardised mortality ratios deaths in low mortality diagnosis reference groups is a key component of Australian healthcare reform. There is much debate in Australia and internationally concerning the appropriateness of this approach. What does the paper add? We extend the current literature and debate by reviewing the statistical limitations, challenges and biases inherent in these indicators. Alternatives for quality and safety performance monitoring that are more robust are presented. What are the implications for practitioners? The hospital standardised mortality ratio and death in low mortality diagnosis reference groups indicators should be used with extreme caution. Although public reporting of quality and safety indicators is necessary there are likely to be better methods to detect substandard performance. These include: properly structured morbidity and mortality meetings, independent audits, evidence-based bundles and checklists, sequential data analysis (e.g. using CUSUMS), and the use of simulators. To achieve maximum safety it is necessary, in addition to using these methods, to understand the characteristics of hospitals as complex systems that exhibit safe emergent behaviour, e.g. using the science of complex systems and its tools. Genuine safety cannot be achieved simply be studying ‘unsafety’. In addition, epidemiologically sound, clinically relevant clinical-quality registries are required.

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

Publisher: Elsevier BV

Date: 07-2015

DOI: 10.1016/J.ENVINT.2015.03.013

Abstract: Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations. This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed. A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA. The applications were notable in their ersity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning. The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.

Efficient Bayesian Estimation of Multivariate State Space Models

Publisher: Elsevier BV

Date: 10-2008

DOI: 10.1016/J.CSDA.2009.04.019

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

Publisher: Elsevier BV

Date: 09-2014

DOI: 10.1016/J.TRC.2014.05.005

Consensus priors for multinomial and binomial ratios

Publisher: Springer Science and Business Media LLC

Date: 05-08-2016

DOI: 10.1080/15598608.2016.1219684

Inference for Proportions in a 2 × 2 Contingency Table: HPD or not HPD?

Publisher: Wiley

Date: 24-11-2008

DOI: 10.1111/J.1541-0420.2008.01134_1.X

Abstract: Highest posterior density intervals are common in Bayesian inference, but as noted by Agresti and Min (2005, Biometrics 61, 515-523) they are not invariant under transformations. Agresti and Min suggested central or "tail" intervals as preferable in the context of the relative risk and odds ratio. A modification to this is proposed for extreme outcomes, as invariance is maintained when replacing central intervals by one-sided intervals. Bayes-Laplace priors for the binomial parameters appear preferable here, compared to Jeffreys priors, contrary to Agresti and Min's suggestion based on frequentist coverage.

Water quality mediates resilience on the Great Barrier Reef

Publisher: Springer Science and Business Media LLC

Date: 11-03-2019

DOI: 10.1038/S41559-019-0832-3

Abstract: Threats from climate change and other human pressures have led to widespread concern for the future of Australia's Great Barrier Reef (GBR). Resilience of GBR reefs will be determined by their ability to resist disturbances and to recover from coral loss, generating intense interest in management actions that can moderate these processes. Here we quantify the effect of environmental and human drivers on the resilience of southern and central GBR reefs over the past two decades. Using a composite water quality index, we find that while reefs exposed to poor water quality are more resistant to coral bleaching, they recover from disturbance more slowly and are more susceptible to outbreaks of crown-of-thorns starfish and coral disease-with a net negative impact on recovery and long-term hard coral cover. Given these conditions, we find that 6-17% improvement in water quality will be necessary to bring recovery rates in line with projected increases in coral bleaching among contemporary inshore and mid-shelf reefs. However, such reductions are unlikely to buffer projected bleaching effects among outer-shelf GBR reefs dominated by fast-growing, thermally sensitive corals, demonstrating practical limits to local management of the GBR against the effects of global warming.

Bayesian model-based approach for developing a river water quality index

Publisher: AIP Publishing LLC

Date: 2014

DOI: 10.1063/1.4894330

Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation

Publisher: Cold Spring Harbor Laboratory

Date: 09-09-2019

DOI: 10.1101/762765

Abstract: The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making. The Banana Bunchy Top Virus (BBTV) poses one of the greatest threats to the food security of developing nations and the banana industry throughout the Asia-Pacific Basin. Decision-makers face significant challenges in mitigating BBTV spread in banana plantations due to the vector-borne spread of this disease, which is significantly influenced by a vast array of external environmental factors that are unique to each plantation. We propose a flexible network-based model that describes the spread of BBTV in a real banana plantation through a random process while accounting for in idual plantation characteristics and utilise a principled methodology for estimating model parameters. Our findings quantify the effect of seasonal changes and plantation configuration on BBTV spread and predict for high-risk areas in this plantation. We believe that our model might be used by decision-makers to evaluate the effectiveness of current disease management strategies and explore opportunities for improvements.

Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19

Publisher: Cold Spring Harbor Laboratory

Date: 03-11-2020

DOI: 10.1101/2020.10.28.20221077

Abstract: Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they are able to model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by retrospectively modelling daily counts of deaths as two distinct phases in the progression of the COVID-19 outbreak: the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We consider various countries that have been adversely affected by the epidemic, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures, in particular, the types and timings of preventative actions. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could be used to explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Publisher: MDPI AG

Date: 14-01-2016

DOI: 10.3390/S16010097

Characterization of expiration air jets and droplet size distributions immediately at the mouth opening

Publisher: Elsevier BV

Date: 02-2009

DOI: 10.1016/J.JAEROSCI.2008.10.003

Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm

Publisher: Public Library of Science (PLoS)

Date: 28-09-2017

DOI: 10.1371/JOURNAL.PONE.0182455

The sound of silence: Listening to the villagers to learn about orangutans

Publisher: Oxford University Press (OUP)

Date: 08-07-2010

DOI: 10.1111/J.1740-9713.2010.00434.X

Abstract: Orangutans, those shy and gentle primates of Borneo and Sumatra, are iconic of species under threat. Efforts to conserve them have met with little success. But Kerrie Mengersen and her colleagues have brought statistics to bear to provide practical guidance towards saving the creature whose name means “old man of the forests”.

Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 10-2019

DOI: 10.1109/TIP.2019.2912292

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Publisher: Springer Science and Business Media LLC

Date: 28-10-2010

DOI: 10.1186/1471-2334-10-311

Bayesian methods for comparing species physiological and ecological response curves

Publisher: Elsevier BV

Date: 07-2016

DOI: 10.1016/J.ECOINF.2016.03.001

Difference in Mosquito Species (Diptera: Culicidae) and the Transmission of Ross River Virus Between Coastline and Inland Areas in Brisbane, Australia

Publisher: Oxford University Press (OUP)

Date: 02-2010

DOI: 10.1603/EN07037

Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration

Publisher: Wiley

Date: 17-05-2020

DOI: 10.1002/SIM.8568

A review of models and model usage scenarios for an airport complex system

Publisher: Elsevier BV

Date: 2013

DOI: 10.1016/J.TRA.2012.10.015

Microbial colonisation of follicular fluid : alterations in cytokine expression and adverse assisted reproductive technology outcomes

Publisher: Elsevier BV

Date: 08-2010

DOI: 10.1016/J.JRI.2010.06.055

Use in practice of importance sampling for repeated MCMC for Poisson models

Publisher: Institute of Mathematical Statistics

Date: 2010

DOI: 10.1214/09-EJS527

Assessing the effectiveness of medical treatment for glaucoma

Publisher: Wiley

Date: 14-06-2011

DOI: 10.1111/J.1442-9071.2011.02582.X

Reliability measures for local nodes assessment in classification trees

Publisher: Informa UK Limited

Date: 06-2003

DOI: 10.1198/1061860031734

Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation

Publisher: Public Library of Science (PLoS)

Date: 18-05-2020

DOI: 10.1371/JOURNAL.PCBI.1007878

Assessing the impacts of grazing levels on bird density in woodland habitat: a Bayesian approach using expert opinion

Publisher: Wiley

Date: 2005

DOI: 10.1002/ENV.732

Policies for Easing COVID-19 Pandemic Travel Restrictions

Publisher: Cold Spring Harbor Laboratory

Date: 20-04-2021

DOI: 10.1101/2021.04.14.21255465

Abstract: During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network meta-population model to compare the effectiveness of international travel policies, with a focus on evaluating the benefit of policy coordination. Because country-level epidemiological parameters are unknown, they need to be estimated from data we accomplished this using approximate Bayesian computation, given the nature of our complex stochastic disease transmission model. Based on simulation and theoretical insights we find that, under our proposed policy, international airline travel may resume up to 58% of the pre-pandemic level with pandemic control comparable to that of a complete shutdown of all airline travel. Our results demonstrate that global coordination is necessary to allow for maximum travel with minimum effect on viral spread.

Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience

Publisher: Springer Science and Business Media LLC

Date: 02-11-2017

DOI: 10.1038/S41467-017-01306-9

Abstract: Better mitigation of anthropogenic stressors on marine ecosystems is urgently needed to address increasing bio ersity losses worldwide. We explore opportunities for stressor mitigation using whole-of-systems modelling of ecological resilience, accounting for complex interactions between stressors, their timing and duration, background environmental conditions and biological processes. We then search for ecological windows, times when stressors minimally impact ecological resilience, defined here as risk, recovery and resistance. We show for 28 globally distributed seagrass meadows that stressor scheduling that exploits ecological windows for dredging c aigns can achieve up to a fourfold reduction in recovery time and 35% reduction in extinction risk. Although the timing and length of windows vary among sites to some degree, global trends indicate favourable windows in autumn and winter. Our results demonstrate that resilience is dynamic with respect to space, time and stressors, varying most strongly with: (i) the life history of the seagrass genus and (ii) the duration and timing of the impacting stress.

Phase randomisation: a convergence diagnostic test for MCMC

Publisher: Wiley

Date: 09-2005

DOI: 10.1111/J.1467-842X.2005.00396.X

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Publisher: Elsevier BV

Date: 06-2015

DOI: 10.1016/J.CANEP.2015.03.001

Abstract: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Strong spatial patterns were observed in the underlying risk factor estimates for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48 to 2.00) and females (median RR range across SLAs 0.53-1.80), with high risks observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final 2 years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. The modelled underlying risks appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

The rule of three, its variants and extensions

Publisher: Wiley

Date: 08-2009

DOI: 10.1111/J.1751-5823.2009.00078.X

Improving accuracy and intelligibility of decisions

Publisher: Springer Science and Business Media LLC

Date: 08-04-2011

DOI: 10.1007/S00003-011-0694-3

Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space–time SIR-based model

Publisher: Elsevier BV

Date: 04-2016

DOI: 10.1016/J.ENVRES.2016.01.013

Abstract: A pandemic strain of influenza A spread rapidly around the world in 2009, now referred to as pandemic (H1N1) 2009. This study aimed to examine the spatiotemporal variation in the transmission rate of pandemic (H1N1) 2009 associated with changes in local socio-environmental conditions from May 7-December 31, 2009, at a postal area level in Queensland, Australia. We used the data on laboratory-confirmed H1N1 cases to examine the spatiotemporal dynamics of transmission using a flexible Bayesian, space-time, Susceptible-Infected-Recovered (SIR) modelling approach. The model incorporated parameters describing spatiotemporal variation in H1N1 infection and local socio-environmental factors. The weekly transmission rate of pandemic (H1N1) 2009 was negatively associated with the weekly area-mean maximum temperature at a lag of 1 week (LMXT) (posterior mean: -0.341 95% credible interval (CI): -0.370--0.311) and the socio-economic index for area (SEIFA) (posterior mean: -0.003 95% CI: -0.004--0.001), and was positively associated with the product of LMXT and the weekly area-mean vapour pressure at a lag of 1 week (LVAP) (posterior mean: 0.008 95% CI: 0.007-0.009). There was substantial spatiotemporal variation in transmission rate of pandemic (H1N1) 2009 across Queensland over the epidemic period. High random effects of estimated transmission rates were apparent in remote areas and some postal areas with higher proportion of indigenous populations and smaller overall populations. Local SEIFA and local atmospheric conditions were associated with the transmission rate of pandemic (H1N1) 2009. The more populated regions displayed consistent and synchronized epidemics with low average transmission rates. The less populated regions had high average transmission rates with more variations during the H1N1 epidemic period.

Beyond compliance : project on an integrated systems approach for pest risk management in South East Asia

Publisher: Wiley

Date: 04-2012

DOI: 10.1111/J.1365-2338.2012.02551.X

Spatial distribution of submicrometre particles and CO in an urban microscale environment

Publisher: Elsevier BV

Date: 07-2005

DOI: 10.1016/J.ATMOSENV.2005.03.049

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference

Publisher: Springer Science and Business Media LLC

Date: 12-2017

DOI: 10.1186/S12942-017-0120-X

Time course of temperature effects on cardiovascular mortality in Brisbane, Australia

Publisher: BMJ

Date: 12-04-2011

DOI: 10.1136/HRT.2010.217166

Abstract: To quantify the lagged effects of mean temperature on deaths from cardiovascular diseases in Brisbane, Australia. Polynomial distributed lag models were used to assess the percentage increase in mortality up to 30 days associated with an increase (or decrease) of 1°C above (or below) the threshold temperature. Brisbane, Australia. 22 805 cardiovascular deaths registered between 1996 and 2004. Deaths from cardiovascular diseases. The results show a longer lagged effect in cold days and a shorter lagged effect in hot days. For the hot effect, a statistically significant association was observed only for lag 0-1 days. The percentage increase in mortality was found to be 3.7% (95% CI 0.4% to 7.1%) for people aged ≥65 years and 3.5% (95% CI 0.4% to 6.7%) for all ages associated with an increase of 1°C above the threshold temperature of 24°C. For the cold effect, a significant effect of temperature was found for 10-15 lag days. The percentage estimates for older people and all ages were 3.1% (95% CI 0.7% to 5.7%) and 2.8% (95% CI 0.5% to 5.1%), respectively, with a decrease of 1°C below the threshold temperature of 24°C. The lagged effects lasted longer for cold temperatures but were apparently shorter for hot temperatures. There was no substantial difference in the lag effect of temperature on mortality between all ages and those aged ≥65 years in Brisbane, Australia.

Multiple Evolutionary Rate Classes in Animal Genome Evolution

Publisher: Oxford University Press (OUP)

Date: 02-12-2009

DOI: 10.1093/MOLBEV/MSP299

Abstract: The proportion of functional sequence in the human genome is currently a subject of debate. The most widely accepted figure is that approximately 5% is under purifying selection. In Drosophila, estimates are an order of magnitude higher, though this corresponds to a similar quantity of sequence. These estimates depend on the difference between the distribution of genomewide evolutionary rates and that observed in a subset of sequences presumed to be neutrally evolving. Motivated by the widening gap between these estimates and experimental evidence of genome function, especially in mammals, we developed a sensitive technique for evaluating such distributions and found that they are much more complex than previously apparent. We found strong evidence for at least nine well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least seven classes in an alignment of four mammals, including human. We also identified at least three rate classes in human ancestral repeats. By positing that the largest of these ancestral repeat classes is neutrally evolving, we estimate that the proportion of nonneutrally evolving sequence is 30% of human ancestral repeats and 45% of the aligned portion of the genome. However, we also question whether any of the classes represent neutrally evolving sequences and argue that a plausible alternative is that they reflect variable structure-function constraints operating throughout the genomes of complex organisms.

The Langat River water quality index based on principal component analysis

Publisher: AIP

Date: 2013

DOI: 10.1063/1.4801283

Effect of vitamin D supplementation on muscle strength: a systematic review and meta-analysis

Publisher: Springer Science and Business Media LLC

Date: 06-10-2011

DOI: 10.1007/S00198-010-1407-Y

Abstract: This systematic review demonstrates that vitamin D supplementation does not have a significant effect on muscle strength in vitamin D replete adults. However, a limited number of studies demonstrate an increase in proximal muscle strength in adults with vitamin D deficiency. The purpose of this study is to systematically review the evidence on the effect of vitamin D supplementation on muscle strength in adults. A comprehensive systematic database search was performed. Inclusion criteria included randomised controlled trials (RCTs) involving adult human participants. All forms and doses of vitamin D supplementation with or without calcium supplementation were included compared with placebo or standard care. Outcome measures included evaluation of strength. Outcomes were compared by calculating standardised mean difference (SMD) and 95% confidence intervals. Of 52 identified studies, 17 RCTs involving 5,072 participants met the inclusion criteria. Meta-analysis showed no significant effect of vitamin D supplementation on grip strength (SMD -0.02, 95%CI -0.15,0.11) or proximal lower limb strength (SMD 0.1, 95%CI -0.01,0.22) in adults with 25(OH)D levels > 25 nmol/L. Pooled data from two studies in vitamin D deficient participants (25(OH)D 25 nmol/L. However, a limited number of studies demonstrate an increase in proximal muscle strength in adults with vitamin D deficiency.

Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/TSP.2020.3003120

Geographically assisted elicitation of expert opinion for regression models

Publisher: Institute of Mathematical Statistics

Date: 03-2007

DOI: 10.1214/07-BA205

Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images

Publisher: Elsevier BV

Date: 05-2007

DOI: 10.1016/J.CSDA.2006.05.013

Head and neck adaptive radiotherapy: Predicting the time to replan

Publisher: Wiley

Date: 20-07-2016

DOI: 10.1111/AJCO.12516

Abstract: Head and neck (H&N) cancer patients can undergo anatomical change throughout radiotherapy treatment. Adaptive radiotherapy (ART) is effective in addressing the impact of this change on the planned dose distribution. The aim of this study was to identify pretreatment factors that influence the need for and timing of replanning for patients receiving chemoradiotherapy for node-positive nasopharyngeal (NPC) and oropharyngeal carcinoma (OPC). Of 110 patients enrolled in a prospective H&N ART study, 21 (19%) underwent a second planning scan (re-CT) and were included in this review. Univariate and multivariate analysis was used to compare those patients who were replanned with those that were not. Factors influencing the timing of replanning were assessed including patient and tumor characteristics and structure volume details. Of the five replanned patients, three were diagnosed with NPC (P = 0.06) and had significantly larger initial nodal volumes (median volume 140.3 cc vs. 39.1 cc, P = 0.019). Overall the median time of re-CT was significantly different between replanned and non-replanned patients, with replanned patients having an earlier re-CT: median fraction 18 versus fraction 23 (P = 0.01). Specifically, NPC patients who were replanned had a re-CT performed earlier than OPC patients (median fraction 11 vs. 20). For H&N patients with large nodes receiving definitive chemoradiotherapy, replanning may be considered at the commencement of week 3 for NPC patients and in week 4 of treatment for OPC patients. This information may facilitate a forward planning approach to H&N ART that enables allocation of departmental resources prior to treatment commencement.

Now You See Them, Soon You Won’t: Statistical and Mathematical Models for Cheetah Conservation Management

Publisher: Elsevier

Date: 2018

DOI: 10.1016/B978-0-12-804088-1.00038-1

Implementation of multivariate control charts in a clinical setting

Publisher: Oxford University Press (OUP)

Date: 10-08-2010

DOI: 10.1093/INTQHC/MZQ044

Abstract: In most clinical monitoring cases there is a need to track more than one quality characteristic. If separate univariate charts are used, the overall probability of a false alarm may be inflated since correlation between variables is ignored. In such cases, multivariate control charts should be considered. This paper considers the implementation and performance of the T(2), multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) charts in light of the challenges faced in clinical settings. We discuss how to handle incomplete records and non-normality of data, and we provide recommendations on chart selection. Our discussion is supported by a case study involving the monitoring of radiation delivered to patients undergoing diagnostic coronary angiogram procedures at St Andrew's War Memorial Hospital, Australia. We also perform a simulation study to investigate chart performance for various correlation structures, patterns of mean shifts, amounts of missing data and methods of imputation. The MEWMA and MCUSUM charts detect small to moderate shifts quickly, even when the quality characteristics are uncorrelated. The T(2) chart performs less well overall, although it is useful for rapid detection of large shifts. When records are incomplete, we recommend using multiple imputation.

Data quality improvement in clinical databases using statistical quality control: Review and case study

Publisher: Springer Science and Business Media LLC

Date: 2013

DOI: 10.1177/2168479012469957

Abstract: Ensuring the quality of data being collected in clinical and medical contexts is a concern for data managers and users. Quality assurance frameworks, systematic audits, and correction procedures have been proposed to enhance the accuracy and completeness of databases. Following an overview of the undertaken approaches, particularly statistical methods, the authors promote acceptance s ling plans (ASPs) and statistical process control (SPC) tools, including control charts and root cause analysis, as the technical core of the data quality improvement mechanism. They review ASP and SPC techniques and discuss their implementation in data quality evaluation and improvement. Two case studies are presented in which the authors apply some of the techniques to databases maintained by a local hospital. Finally, guidelines are proposed for which techniques are appropriate with regard to dataflow and database specifications.

Trends in size classified particle number concentration in subtropical Brisbane, Australia, based on a 5 year study

Publisher: Elsevier BV

Date: 02-2007

DOI: 10.1016/J.ATMOSENV.2006.09.020

Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora

Publisher: PeerJ

Date: 12-06-2017

DOI: 10.7717/PEERJ.3438

Abstract: Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef declines globally. However, little is known about how seasonal scale variations in environmental factors influence disease dynamics at the level of in idual coral colonies. In this study, we applied a multi-state Markov model (MSM) to investigate the dynamics of black band disease (BBD) developing from apparently healthy corals and/or a precursor-stage, termed ‘cyanobacterial patches’ (CP), in relation to seasonal variation in light and seawater temperature at two reef sites around Pelorus Island in the central sector of the Great Barrier Reef. The model predicted that the proportion of colonies transitioning from BBD to Healthy states within three months was approximately 57%, but 5.6% of BBD cases resulted in whole colony mortality. According to our modelling, healthy coral colonies were more susceptible to BBD during summer months when light levels were at their maxima and seawater temperatures were either rising or at their maxima. In contrast, CP mostly occurred during spring, when both light and seawater temperatures were rising. This suggests that environmental drivers for healthy coral colonies transitioning into a CP state are different from those driving transitions into BBD. Our model predicts that (1) the transition from healthy to CP state is best explained by increasing light, (2) the transition between Healthy to BBD occurs more frequently from early to late summer, (3) 20% of CP infected corals developed BBD, although light and temperature appeared to have limited impact on this state transition, and (4) the number of transitions from Healthy to BBD differed significantly between the two study sites, potentially reflecting differences in localised wave action regimes.

Enter the reverend: Introduction to and application of Bayes' theorem in clinical ophthalmology

Publisher: Wiley

Date: 26-07-2011

DOI: 10.1111/J.1442-9071.2011.02592.X

Abstract: Ophthalmic practice utilizes numerous diagnostic tests, some of which are used to screen for disease. Interpretation of test results and many clinical management issues are actually problems in inverse probability that can be solved using Bayes' theorem. Use two-by-two tables to understand Bayes' theorem and apply it to clinical ex les. Specific ex les of the utility of Bayes' theorem in diagnosis and management. Two-by-two tables are used to introduce concepts and understand the theorem. The application in interpretation of diagnostic tests is explained. Clinical ex les demonstrate its potential use in making management decisions. Positive predictive value and conditional probability. The theorem demonstrates the futility of testing when prior probability of disease is low. Application to untreated ocular hypertension demonstrates that the estimate of glaucomatous optic neuropathy is similar to that obtained from the Ocular Hypertension Treatment Study. Similar calculations are used to predict the risk of acute angle closure in a primary angle closure suspect, the risk of pupillary block in a diabetic undergoing cataract surgery, and the probability that an observed decrease in intraocular pressure is due to the medication that has been started. The ex les demonstrate how data required for management can at times be easily obtained from available information. Knowledge of Bayes' theorem helps in interpreting test results and supports the clinical teaching that testing for conditions with a low prevalence has a poor predictive value. In some clinical situations Bayes' theorem can be used to calculate vital data required for patient management.

A comparison of Bayes-Laplace, Jeffreys, and other priors: the case of zero events

Publisher: Informa UK Limited

Date: 02-2008

DOI: 10.1198/000313008X267839

The selection of the Bayesian coincident-index models using model comparison criterion with application in Langat river water quality data

Publisher: AIP

Date: 2013

DOI: 10.1063/1.4801278

Population Monte Carlo Algorithm in High Dimensions

Publisher: Springer Science and Business Media LLC

Date: 26-08-2009

DOI: 10.1007/S11009-009-9154-2

Monitoring pertussis infections using internet search queries

Publisher: Springer Science and Business Media LLC

Date: 05-09-2017

DOI: 10.1038/S41598-017-11195-Z

Abstract: This study aims to assess the utility of internet search query analysis in pertussis surveillance. This study uses an empirical time series model based on internet search metrics to detect the pertussis incidence in Australia. Our research demonstrates a clear seasonal pattern of both pertussis infections and Google Trends (GT) with specific search terms in time series seasonal decomposition analysis. The cross-correlation function showed significant correlations between GT and pertussis incidences in Australia and each state at the lag of 0 and 1 months, with the variation of correlations between 0.17 and 0.76 (p 0.05). A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed to track pertussis epidemics pattern using GT data. Reflected values for this model were generally consistent with the observed values. The inclusion of GT metrics improved detective performance of the model (β = 0.058, p 0.001). The validation analysis indicated that the overall agreement was 81% (sensitivity: 77% and specificity: 83%). This study demonstrates the feasibility of using internet search metrics for the detection of pertussis epidemics in real-time, which can be considered as a pre-requisite for constructing early warning systems for pertussis surveillance using internet search metrics.

Missing in space: An evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Publisher: Springer Science and Business Media LLC

Date: 2014

DOI: 10.1186/1476-072X-13-47

Effect of Multiple Physiotherapy Sessions on Functional Outcomes in the Initial Postoperative Period After Primary Total Hip Replacement: A Randomized Controlled Trial

Publisher: Elsevier BV

Date: 10-2009

DOI: 10.1016/J.APMR.2009.04.012

Abstract: Stockton KA, Mengersen KA. Effect of multiple physiotherapy sessions on functional outcomes in the initial postoperative period after primary total hip replacement: a randomized controlled trial. To determine whether increasing physiotherapy input from once to twice per day will result in earlier achievement of functional milestones (ie, independence in mobility and transfers) and decreased length of stay (LOS) in patients undergoing a primary total hip replacement. Randomized controlled trial. Metropolitan private hospital. Patients (N=57) with primary total hip replacement were randomly assigned to the twice daily (treatment, n=30) and once daily (control, n=27) groups. Patients who chose to attend hydrotherapy were excluded from the randomization process however, they gave consent for outcome measures to be collected for comparison with the randomized groups. The control group received usual care, and the treatment group received twice-daily physiotherapy from day 1 after surgery to discharge. The Iowa Level of Assistance at postoperative days 3 and 6 and LOS. This study demonstrates that patients who received twice-daily land-based physiotherapy after primary total hip replacement attained earlier achievement of functional milestones than patients that received once-daily physiotherapy. A statistically significant (P=.041) but not clinically significant difference was evident in the Iowa Level of Assistance score at day 3. There was no difference between the groups in Iowa Level of Assistance measures on day 6 or on LOS. Patients who received twice-daily physiotherapy showed a trend toward earlier achievement of functional milestones however, this finding did not translate to decreased LOS.

Wayfinding: A simple concept, a complex process

Publisher: Informa UK Limited

Date: 11-2012

DOI: 10.1080/01441647.2012.712555

Analysis of the predictive value of clinical and sonographic variables in children with suspected acute appendicitis using decision tree algorithms

Publisher: Wiley

Date: 31-07-2018

DOI: 10.1002/SONO.12156

Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

Publisher: Public Library of Science (PLoS)

Date: 04-06-2013

DOI: 10.1371/JOURNAL.PONE.0065039

Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

Publisher: IOP Publishing

Date: 06-02-2015

DOI: 10.1088/0031-9155/60/5/1793

Abstract: Collected real-life clinical target volume (CTV) displacement data show that some patients undergoing external beam radiotherapy (EBRT) demonstrate significantly more fraction-to-fraction variability in their displacement ('random error') than others. This contrasts with the common assumption made by historical recipes for margin estimation for EBRT, that the random error is constant across patients. In this work we present statistical models of CTV displacements in which random errors are characterised by an inverse gamma (IG) distribution in order to assess the impact of random error variability on CTV-to-PTV margin widths, for eight real world patient cohorts from four institutions, and for different sites of malignancy. We considered a variety of clinical treatment requirements and penumbral widths. The eight cohorts consisted of a total of 874 patients and 27 391 treatment sessions. Compared to a traditional margin recipe that assumes constant random errors across patients, for a typical 4 mm penumbral width, the IG based margin model mandates that in order to satisfy the common clinical requirement that 90% of patients receive at least 95% of prescribed RT dose to the entire CTV, margins be increased by a median of 10% (range over the eight cohorts -19% to +35%). This substantially reduces the proportion of patients for whom margins are too small to satisfy clinical requirements.

A statistical view of synthesizing patterns of species richness along productivity gradients: Devils, forests, and trees

Publisher: Wiley

Date: 09-2010

DOI: 10.1890/09-1039.1

An improved method for calculating toxicity-based pollutant loads: Part 2. application to contaminants discharged to the great barrier reef, queensland, australia

Publisher: Wiley

Date: 04-12-2017

DOI: 10.1002/IEAM.1860

Abstract: Pollutant loads are widely used to set pollution reduction targets and assess regulatory compliance for the protection of receiving waterbodies. However, when a pollutant load consists of a mixture of chemicals, reducing the overall load (mass) will not necessarily reduce the toxicity by a similar amount. This can be overcome by setting targets based on toxicity-based loads (toxic loads, TLs), where the load is modified according to the relative toxicity (expressed as toxic equivalency factors [TEFs]) of each toxicant. Here, we present the second article of a 2-part series in which a case study is used to demonstrate the application of the toxic load method proposed in Part 1. The toxic load method converts a pollutant load, comprised of multiple chemicals, to a toxic load, using a modified TEF approach. The modified approach uses a cumulative distribution of relative potency (ReP) estimates of multiple species to determine a TEF. It further improves upon previously published methods by including two tests to select the optimal percentile of the ReP distribution to determine the TEF. The first test is a test for environmental relevance that compares results against an independent mixture method, identifying the percentile that produces the most environmentally relevant TEFs and TLs. The second is a test for robustness which ensures the results are independent of the ReP of the selected reference chemical. Here, the TL method is applied to mixtures of pesticides that are discharged from agricultural land to the Great Barrier Reef (GBR) to test its utility. In this case study, the most environmentally relevant and robust TLs were generated using the 75th percentile of the ReP cumulative distribution. The results demonstrate that it is essential to develop pollution reduction targets based on toxic loads and making progress to meeting them will lead to a commensurate reduction in toxic effects caused by toxicants in waters of the GBR. Integr Environ Assess Manag 2017 :754-764. © 2016 SETAC.

Estimation of the time of a linear trend in monitoring survival time

Publisher: Springer Science and Business Media LLC

Date: 12-04-2014

DOI: 10.1007/S10742-014-0115-Z

Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions

Publisher: Wiley

Date: 07-08-2014

DOI: 10.1002/JRSM.1087

Abstract: Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating ex le that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence.

A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data

Publisher: Informa UK Limited

Date: 10-06-2015

DOI: 10.1080/03610918.2014.936467

Supporting statistics in the workplace: experiences with two hospitals

Publisher: Hindawi Limited

Date: 2003

DOI: 10.1155/S1173912603000117

Abstract: This paper provides some reflections on the promotion of lifelong learning in statistics in the workplace. The initiative from which the reflections are drawn is a collaboration between a university and two public hospitals, of which one of the stated aims is to develop statistical skills among the hospitals' researchers. This is realized in the provision of ‘biostatistical clinics’ in which workplace teaching and learning of statistics takes place in one-on-one or small group situations. The central issue that is identified is the need to accommodate ersity: in backgrounds, motivations and learning needs of workplace learners (in this case medical researchers), in the workplace environments themselves and in the projects encountered. Operational issues for the statistician in providing such training are addressed. These considerations may reflect the experiences of the wider community of statisticians involved in service provision within a larger organization.

Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters

Publisher: Institute of Mathematical Statistics

Date: 03-2009

DOI: 10.1214/09-BA405

Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis

Publisher: Public Library of Science (PLoS)

Date: 31-08-2011

DOI: 10.1371/JOURNAL.PONE.0023903

Looking deeper than (just) below the surface

Publisher: Wiley

Date: 02-10-2014

DOI: 10.1111/CEO.12404

Spatio-temporal variation in soil derived nitrous oxide emissions under sugarcane

Publisher: Elsevier BV

Date: 10-2011

DOI: 10.1016/J.SCITOTENV.2011.07.044

Abstract: Nitrous oxide (N(2)O) is a significant greenhouse gas with a global warming potential that is 300 times than that of carbon dioxide. Soil derived N(2)O emissions usually display a high degree of spatial and temporal variability because of their dependence on soil chemical and physical properties, and climate dependent environmental factors. However, there is little research that incorporates spatial dependence in the estimation of N(2)O emissions allowing for environmental factors in the same model. This study aims to examine the impact of two environmental factors (soil temperature and soil moisture) on N(2)O emissions and explore the spatial structure of N(2)O in the sub-tropical South East Queensland region of Australia. The replicated data on N(2)O emissions and soil properties were collected at a typical sugarcane land site covering 25 uniform grid points across 3600 m(2) between October 2007 and September 2008. A Bayesian conditional autoregressive (CAR) model was used to model spatial dependence. Results showed that soil moisture and soil temperature appeared to have substantially different effects on N(2)O emissions after taking spatial dependence into account in the four seasons. There was a substantial variation in the spatial distribution of N(2)O emission in the different seasons. The high N(2)O emission regions were accompanied by high uncertainty and changed in varying seasons in this study site. Spatial CAR models might be more plausible to elucidate and account for the uncertainty arising from unclear variables and spatial variability in the assessment of N(2)O emissions in soils, and more accurately identify relationships with key environmental factors and help to reduce the uncertainty of the soil parameters.

Using centers for disease control national nosocomial infections surveillance surgical site infection risk-adjustment for a group of related orthopaedic procedures

Publisher: Elsevier BV

Date: 09-2011

DOI: 10.1071/HI11003

The potential for meta-analysis to support decision analysis in ecology

Publisher: Wiley

Date: 30-11-2013

DOI: 10.1002/JRSM.1105

Abstract: Meta-analysis and decision analysis are underpinned by well-developed methods that are commonly applied to a variety of problems and disciplines. While these two fields have been closely linked in some disciplines such as medicine, comparatively little attention has been paid to the potential benefits of linking them in ecology, despite reasonable expectations that benefits would be derived from doing so. Meta-analysis combines information from multiple studies to provide more accurate parameter estimates and to reduce the uncertainty surrounding them. Decision analysis involves selecting among alternative choices using statistical information that helps to shed light on the uncertainties involved. By linking meta-analysis to decision analysis, improved decisions can be made, with quantification of the costs and benefits of alternate decisions supported by a greater density of information. Here, we briefly review concepts of both meta-analysis and decision analysis, illustrating the natural linkage between them and the benefits from explicitly linking one to the other. We discuss some ex les in which this linkage has been exploited in the medical arena and how improvements in precision and reduction of structural uncertainty inherent in a meta-analysis can provide substantive improvements to decision analysis outcomes by reducing uncertainty in expected loss and maximising information from across studies. We then argue that these significant benefits could be translated to ecology, in particular to the problem of making optimal ecological decisions in the face of uncertainty.

Exact Bayesian analysis of mixtures

Publisher: Wiley

Date: 15-04-2011

DOI: 10.1002/9781119995678.CH11

Time-series analysis of the risk factors for haemorrhagic fever with renal syndrome: comparison of statistical models

Publisher: Cambridge University Press (CUP)

Date: 19-06-2006

DOI: 10.1017/S0950268806006649

Abstract: Three conventional regression models were compared using the time-series data of the occurrence of haemorrhagic fever with renal syndrome (HFRS) and several key climatic and occupational variables collected in low-lying land, Anhui Province, China. Model I was a linear time series with normally distributed residuals model II was a generalized linear model with Poisson-distributed residuals and a log link and model III was a generalized additive model with the same distributional features as model II. Model I was fitted using least squares whereas models II and III were fitted using maximum likelihood. The results show that the correlations between the HFRS incidence and the independent variables measured (i.e. difference in water level, autumn crop production and density of Apodemus agrarius ) ranged from −0·40 to 0·89. The HFRS incidence was positively associated with density of A. agrarius and crop production, but was inversely associated with difference in water level. The residual analyses and the examination of the accuracy of the models indicate that model III may be the most suitable in the assessment of the relationship between the incidence of HFRS and the independent variables.

A Variational Bayesian Gaussian Mixture-Nonnegative Matrix Factorization Model to Extract Movement Primitives for Robust Control

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 12-2022

DOI: 10.1109/THMS.2022.3194593

Biosecurity threats: The design of surveillance systems, based on power and risk

Publisher: Springer Science and Business Media LLC

Date: 23-05-2010

DOI: 10.1007/S10651-009-0113-4

Bayesian estimation of extent of recovery for aspects of verbal memory in women undergoing adjuvant chemotherapy treatment for breast cancer

Publisher: Oxford University Press (OUP)

Date: 11-2011

DOI: 10.1111/J.1467-9876.2011.00766.X

Abstract: Decline in cognitive function can be experienced by up to 50% of women while undergoing adjuvant chemotherapy with a subset of patients experiencing long-term effects. Bayesian latent basis longitudinal random-effects and latent class growth models were used to assess the degree of verbal memory recovery for learning, immediate retention and delayed recall in women undergoing chemotherapy for early stage breast cancer and who were assessed before, and at 1, 6 and 18 months post chemotherapy. The latent basis model, with the initial time point fixed to 1 and the second to 0, enabled the estimation of the degree of recovery at times 3 and 4. In the single-class model, three prior distributions for the random effects were used, resulting in minimal differences in parameter estimation. These models resulted in a near complete recovery for learning, and 57–60% for immediate retention and delayed recall at 18 months post chemotherapy. A range of two- to five-class Bayesian mixture models were fitted, with classes differing for baseline and degree of recovery. In the three-class model, the ‘Low’ class indicated 29.0%, 11.0% and 9.6% recovery at 18 months post treatment for the three outcome measures respectively, indicating an extended time of reduced verbal memory function.

Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes

Publisher: Informa UK Limited

Date: 14-02-2021

DOI: 10.1080/01431161.2021.1881183

Viewing systematic reviews and meta-analysis in social research through different lenses

Publisher: Springer Science and Business Media LLC

Date: 10-09-2014

DOI: 10.1186/2193-1801-3-511

Abstract: Systematic reviews and meta-analyses are used to combine results across studies to determine an overall effect. Meta-analysis is especially useful for combining evidence to inform social policy, but meta-analyses of applied social science research may encounter practical issues arising from the nature of the research domain. The current paper identifies potential resolutions to four issues that may be encountered in systematic reviews and meta-analyses in social research. The four issues are: scoping and targeting research questions appropriate for meta-analysis selecting eligibility criteria where primary studies vary in research design and choice of outcome measures dealing with inconsistent reporting in primary studies and identifying sources of heterogeneity with multiple confounded moderators. The paper presents an overview of each issue with a review of potential resolutions, identified from similar issues encountered in meta-analysis in medical and biological sciences. The discussion aims to share and improve methodology in systematic reviews and meta-analysis by promoting cross-disciplinary communication, that is, to encourage ‘viewing through different lenses’.

Bayesian mixture models: A blood-free dissection of a sheep

Publisher: Wiley

Date: 15-04-2011

DOI: 10.1002/9781119995678.CH14

Quantifying Killing of Orangutans and Human-Orangutan Conflict in Kalimantan, Indonesia

Publisher: Public Library of Science (PLoS)

Date: 11-11-2011

DOI: 10.1371/JOURNAL.PONE.0027491

Investigating the use of a bayesian network to model the risk of lyngbya majuscula bloom initiation in Deception Bay, Queensland, Australia

Publisher: Informa UK Limited

Date: 09-11-2007

DOI: 10.1080/10807030701655616

INVESTIGATING EFFECTIVE WAYFINDING IN AIRPORTS: A BAYESIAN NETWORK APPROACH

Publisher: Vilnius Gediminas Technical University

Date: 25-03-2014

DOI: 10.3846/16484142.2014.898695

Abstract: Effective wayfinding is the successful interplay of human and environmental factors resulting in a person successfully moving from their current position to a desired location in a timely manner. To date this process has not been modelled to reflect this interplay. This paper proposes a complex modelling system approach of wayfinding by using Bayesian Networks to model this process, and applies the model to airports. The model suggests that human factors have a greater impact on effective wayfinding in airports than environmental factors. The greatest influences on human factors are found to be the level of spatial anxiety experienced by travellers and their cognitive and spatial skills. The model also predicted that the navigation pathway that a traveller must traverse has a larger impact on the effectiveness of an airport’s environment in promoting effective wayfinding than the terminal design.

Transgenic Cavendish bananas with resistance to Fusarium wilt tropical race 4

Publisher: Springer Science and Business Media LLC

Date: 14-11-2017

DOI: 10.1038/S41467-017-01670-6

Abstract: Banana ( Musa spp.) is a staple food for more than 400 million people. Over 40% of world production and virtually all the export trade is based on Cavendish banana. However, Cavendish banana is under threat from a virulent fungus, Fusarium oxysporum f. sp. cubense tropical race 4 (TR4) for which no acceptable resistant replacement has been identified. Here we report the identification of transgenic Cavendish with resistance to TR4. In our 3-year field trial, two lines of transgenic Cavendish, one transformed with RGA2 , a gene isolated from a TR4-resistant diploid banana, and the other with a nematode-derived gene, Ced9 , remain disease free. Transgene expression in the RGA2 lines is strongly correlated with resistance. Endogenous RGA2 homologs are also present in Cavendish but are expressed tenfold lower than that in our most resistant transgenic line. The expression of these homologs can potentially be elevated through gene editing, to provide non-transgenic resistance.

Limiting risk of hospital adverse events: avoiding train wrecks is more important than counting and reporting them

Publisher: Elsevier BV

Date: 12-2010

DOI: 10.1016/J.JHIN.2010.06.010

Abstract: Targets implemented at national or state levels have been employed in response to excessive numbers of adverse events (AEs) such as multiple antibiotic-resistant Staphylococcus aureus bacteraemias. Hospital resources are limited and setting such targets can result in resource ersion to dealing with the targeted AEs. There may be initial success as judged by decreasing counts but underlying problems are not necessarily addressed, and there is evidence that other non-targeted AEs may increase. Moreover, the values of in idual observations can be greatly influenced by random variation. This can make it difficult using comparisons and targets to draw conclusions about the work of an institution. Although counting AEs is essential, the key to avoiding episodes of patient harm is prevention. This requires the implementation of evidence-based systems. These are already available for many AEs in the form of 'bundles' and checklists. When these systems are properly implemented and sustained, AE rates tend to occur at minimum predictable levels. Unfortunately, in spite of widespread knowledge and aggressive promotion, high levels of compliance have often been difficult to achieve and sustain. Better understanding and implementation of methods to sustain evidence-based systems are needed. Checklists, used as part of an overall system involving leadership and empowerment, application of evidence, culture change and measurement, may help to overcome this problem.

Modelling microbial health risk of wastewater reuse: A systems perspective

Publisher: Elsevier BV

Date: 11-2015

DOI: 10.1016/J.ENVINT.2015.08.001

Abstract: There is a widespread need for the use of quantitative microbial risk assessment (QMRA) to determine reclaimed water quality for specific uses, however neither faecal indicator levels nor pathogen concentrations alone are adequate for assessing exposure health risk. The aim of this study was to build a conceptual model representing factors contributing to the microbiological health risks of reusing water treated in maturation ponds. This paper describes the development of an unparameterised model that provides a visual representation of theoretical constructs and variables of interest. Information was collected from the peer-reviewed literature and through consultation with experts from regulatory authorities and academic disciplines. In this paper we explore how, considering microbial risk as a modular system, following the QMRA framework enables incorporation of the many factors influencing human exposure and dose response, to better characterise likely human health impacts. By using and expanding upon the QMRA framework we deliver new insights into this important field of environmental exposures. We present a conceptual model of health risk of microbial exposure which can be used for maturation ponds and, more importantly, as a generic tool to assess health risk in erse wastewater reuse scenarios.

Bayesian computation via empirical likelihood

Publisher: Proceedings of the National Academy of Sciences

Date: 07-01-2013

DOI: 10.1073/PNAS.1208827110

Abstract: Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective s le size. The method is illustrated using several ex les, including estimation of standard distributions, time series, and population genetics models.

Allowing for the effect of data binning in a Bayesian Normal mixture model

Publisher: Elsevier BV

Date: 04-2010

DOI: 10.1016/J.CSDA.2009.10.003

Differences in airborne particle and gaseous concentrations in urban air between weekdays and weekends

Publisher: Elsevier BV

Date: 09-2002

DOI: 10.1016/S1352-2310(02)00337-0

Geographic variation in the intended choice of adjuvant treatments for women diagnosed with screen-detected breast cancer in Queensland.

Publisher: Springer Science and Business Media LLC

Date: 12-2015

DOI: 10.1186/S12889-015-2527-2

Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

Publisher: Elsevier BV

Date: 09-2012

DOI: 10.1016/J.ENVINT.2012.03.010

Abstract: Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71% 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5% 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

Motivation, development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover

Publisher: Elsevier BV

Date: 2010

DOI: 10.1016/J.ISPRSJPRS.2009.08.002

Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

Publisher: Informa UK Limited

Date: 11-2012

DOI: 10.1080/02664763.2012.710897

Investigation of the relationship between smoking and appendicitis in Australian twins

Publisher: Elsevier BV

Date: 08-2008

DOI: 10.1016/J.ANNEPIDEM.2008.04.004

Abstract: Appendicitis is an inflammation of the appendix, the etiology of which is still poorly understood. Previous studies have shown an increased risk for cigarette smokers but no accounts for the timing of exposure to smoking relative to appendectomy were made. Based on questionnaire data, both cohort and co-twin case-control analyses were conducted to assess the effect of active cigarette smoking on appendectomy in 3808 Australian twin pairs. Smoking status was defined as a time-dependent covariate to account for differences in timing of smoking initiation and onset of appendicitis. The questionnaire had a 65% pairwise response rate. After controlling for sex, age, and year of birth, appendectomy risk in current smokers was statistically significantly increased by 65% relative to never-smokers. This was largely unchanged by the duration or intensity of smoking and was not affected by socioeconomic status or father's occupation. The effect was stronger in females. Among former smokers, increased time since quitting significantly reduced the odds ratio of appendectomy by 15% for every year since quitting. After adjustment for age and other confounders, there was an increase in risk of appendectomy among current smokers relative to never-smokers, particularly in females. This study adds to the body of knowledge on the effects of tobacco smoking on the gastrointestinal tract.

Association between location of laser iridotomy and frequency of visual symptoms: a Bayesian learning analysis

Publisher: Wiley

Date: 09-12-2015

DOI: 10.1111/CEO.12667

Semiparametric Bayesian circular statistics

Publisher: Elsevier BV

Date: 06-2008

DOI: 10.1016/J.CSDA.2008.03.016

Eliciting Expert Knowledge in Conservation Science

Publisher: Wiley

Date: 26-01-2012

DOI: 10.1111/J.1523-1739.2011.01806.X

Abstract: Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

Spatial and temporal clusters of Barmah Forest virus disease in Queensland, Australia

Publisher: Wiley

Date: 11-04-2011

DOI: 10.1111/J.1365-3156.2011.02775.X

Abstract: To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems and spatial scan statistic (SaTScan). We obtained BFV disease cases, population and statistical local areas (SLAs) boundary data from Queensland Health and Australian Bureau of Statistics, respectively, during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200 km radius and varying time windows from 1 to 12 months were chosen (for the space-time analysis). The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations. This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing ongoing surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.

Preface

Publisher: Wiley

Date: 15-04-2011

DOI: 10.1002/9781119995678

Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments

Publisher: Springer Science and Business Media LLC

Date: 26-06-2010

DOI: 10.1007/S11356-009-0210-9

Abstract: Urban motor vehicle fleets are a major source of particulate matter pollution, especially of ultrafine particles (diameters < 0.1 microm), and exposure to particulate matter has known serious health effects. A considerable body of literature is available on vehicle particle emission factors derived using a wide range of different measurement methods for different particle sizes, conducted in different parts of the world. Therefore, the choice as to which are the most suitable particle emission factors to use in transport modelling and health impact assessments presented as a very difficult task. The aim of this study was to derive a comprehensive set of tailpipe particle emission factors for different vehicle and road type combinations, covering the full size range of particles emitted, which are suitable for modelling urban fleet emissions. A large body of data available in the international literature on particle emission factors for motor vehicles derived from measurement studies was compiled and subjected to advanced statistical analysis, to determine the most suitable emission factors to use in modelling urban fleet emissions. This analysis resulted in the development of five statistical models which explained 86%, 93%, 87%, 65% and 47% of the variation in published emission factors for particle number, particle volume, PM(1), PM(2.5) and PM(10), respectively. A sixth model for total particle mass was proposed but no significant explanatory variables were identified in the analysis. From the outputs of these statistical models, the most suitable particle emission factors were selected. This selection was based on examination of the statistical robustness of the statistical model outputs, including consideration of conservative average particle emission factors with the lowest standard errors, narrowest 95% confidence intervals and largest s le sizes and the explanatory model variables, which were vehicle type (all particle metrics), instrumentation (particle number and PM(2.5)), road type (PM(10)) and size range measured and speed limit on the road (particle volume). A multiplicity of factors need to be considered in determining emission factors that are suitable for modelling motor vehicle emissions, and this study derived a set of average emission factors suitable for quantifying motor vehicle tailpipe particle emissions in developed countries. The comprehensive set of tailpipe particle emission factors presented in this study for different vehicle and road type combinations enable the full size range of particles generated by fleets to be quantified, including ultrafine particles (measured in terms of particle number). These emission factors have particular application for regions which may have a lack of funding to undertake measurements, or insufficient measurement data upon which to derive emission factors for their region. In urban areas motor vehicles continue to be a major source of particulate matter pollution and of ultrafine particles. It is critical that in order to manage this major pollution source methods are available to quantify the full size range of particles emitted for transport modelling and health impact assessments.

Predicting the natural occurrence of blackbutt and Gympie messmate in southeast Queensland

Publisher: Informa UK Limited

Date: 2000

DOI: 10.1080/00049158.2000.10674832

Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle

Publisher: Elsevier BV

Date: 02-2010

DOI: 10.1016/J.ECOLMODEL.2009.11.012

[HDDA] sparse subspace constrained partial least squares

Publisher: Informa UK Limited

Date: 09-12-2018

DOI: 10.1080/00949655.2018.1555830

Geographical and spatial disparities in the incidence and survival of rare cancers in Australia

Publisher: Wiley

Date: 21-12-2023

DOI: 10.1002/IJC.34395

Abstract: Rare cancers collectively account for around a quarter of cancer diagnoses and deaths. However, epidemiological studies are sparse. We describe spatial and geographical patterns in incidence and survival of rare cancers across Australia using a population‐based cancer registry cohort of rare cancer cases diagnosed among Australians aged at least 15 years, 2007 to 2016. Rare cancers were defined using site‐ and histology‐based categories from the European RARECARE study, as in idual cancer types having crude annual incidence rates of less than 6/100 000. Incidence and survival patterns were modelled with generalised linear and Bayesian spatial Leroux models. Spatial heterogeneity was tested using the maximised excess events test. Rare cancers (n = 268 070) collectively comprised 22% of all invasive cancer diagnoses and accounted for 27% of all cancer‐related deaths in Australia, 2007 to 2016 with an overall 5‐year relative survival of around 53%. Males and those living in more remote or more disadvantaged areas had higher incidence but lower survival. There was substantial evidence for spatial variation in both incidence and survival for rare cancers between small geographical areas across Australia, with similar patterns so that those areas with higher incidence tended to have lower survival. Rare cancers are a substantial health burden in Australia. Our study has highlighted the need to better understand the higher burden of these cancers in rural and disadvantaged regions where the logistical challenges in their diagnosis, treatment and support are magnified.

A software tool for elicitation of expert knowledge about species richness or similar counts

Publisher: Elsevier BV

Date: 12-2012

DOI: 10.1016/J.ENVSOFT.2011.11.011

The effect of temperature and humidity on size segregated traffic exhaust particle emissions

Publisher: Elsevier BV

Date: 03-2008

DOI: 10.1016/J.ATMOSENV.2007.12.038

Elicitator: A user-friendly, interactive tool to support scenario-based elicitation of expert knowledge

Publisher: Springer New York

Date: 20-09-2014

DOI: 10.1007/978-1-4614-1034-8_3

Spatial Analysis of Notified Cryptosporidiosis Infections in Brisbane, Australia

Publisher: Elsevier BV

Date: 12-2009

DOI: 10.1016/J.ANNEPIDEM.2009.06.004

Abstract: This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia. We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis. Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

Spatial variation in particle number size distributions in a large metropolitan area

Publisher: Copernicus GmbH

Date: 28-02-2008

DOI: 10.5194/ACP-8-1127-2008

Abstract: Abstract. Air quality studies have indicated that particle number size distribution (NSD) is unevenly spread in urban air. To date, these studies have focussed on differences in concentration levels between s ling locations rather than differences in the underlying geometries of the distributions. As a result, the existing information on the spatial variation of the NSD in urban areas remains incomplete. To investigate this variation in a large metropolitan area in the southern hemisphere, NSD data collected at nine different locations during different c aigns of varying duration were compared using statistical methods. The spectra were analysed in terms of their modal structures (the graphical representation of the number size distribution function), cumulative distribution and number median diameter (NMD). The study found that with the exception of one site all distributions were bimodal or suggestive of bimodality. In general, peak concentrations were below 30 nm and NMDs below 50 nm, except at a site dominated by diesel trucks, where it shifted to around 50 and 60 nm respectively. Ultrafine particles (UFPs ( nm)) contributed to 82–90% of the particle number, nanoparticles ( nm) to around 60–70%, except at the diesel traffic site, where their contribution dropped to 50%. Statistical analyses found that the modal structures heterogeneously distributed throughout Brisbane whereas it was not always the case for the NMD. The discussion led to the following site classification: (1) urban sites dominated by petrol traffic, (2) urban sites affected by the proximity to the road and (3) an isolated site dominated by diesel traffic. Comparisons of weekday and weekend data indicated that, the distributions were not statistically different. The only exception occurred at one site, where there is a significant drop in the number of diesel buses on the weekend. The differences in s ling period between sites did not affect the results. The statistics instead suggested variations in traffic composition. However, the relative contribution of petrol vehicle emissions at each site could not be assessed due to the limited traffic information available.

Multifaceted Modelling of Complex Business Enterprises

Publisher: Public Library of Science (PLoS)

Date: 06-08-2015

DOI: 10.1371/JOURNAL.PONE.0134052

Weather Variability and the Incidence of Cryptosporidiosis: Comparison of Time Series Poisson Regression and SARIMA Models

Publisher: Elsevier BV

Date: 09-2007

DOI: 10.1016/J.ANNEPIDEM.2007.03.020

Abstract: Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53 Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

Geographical and spatial variations in bowel cancer screening participation, Australia, 2015–2020

Publisher: Public Library of Science (PLoS)

Date: 20-07-2023

DOI: 10.1371/JOURNAL.PONE.0288992

Abstract: Participation in bowel cancer screening programs remains poor in many countries. Knowledge of geographical variation in participation rates may help design targeted interventions to improve uptake. This study describes small-area and broad geographical patterns in bowel screening participation in Australia between 2015–2020. Publicly available population-level participation data for Australia’s National Bowel Cancer Screening Program (NBCSP) were modelled using generalized linear models to quantify screening patterns by remoteness and area-level disadvantage. Bayesian spatial models were used to obtain smoothed estimates of participation across 2,247 small areas during 2019–2020 compared to the national average, and during 2015–2016 and 2017–2018 for comparison. Spatial heterogeneity was assessed using the maximized excess events test. Overall, screening participation rates was around 44% over the three time-periods. Participation was consistently lower in remote or disadvantaged areas, although heterogeneity was evident within these broad categories. There was strong evidence of spatial differences in participation over all three periods, with little change in patterns between time periods. If the spatial variation was reduced (so low participation areas were increased to the 80th centile), an extra 250,000 screens (4% of total) would have been conducted during 2019–2020. Despite having a well-structured evidence-based government funded national bowel cancer screening program, the substantial spatial variation in participation rates highlights the importance of accounting for the unique characteristics of specific geographical regions and their inhabitants. Identifying the reasons for geographical disparities could inform interventions to achieve more equitable access and a higher overall bowel screening uptake.

Spatial variation of airborne pollutant concentrations in Brisbane, Australia and its potential impact on population exposure assessment

Publisher: Elsevier BV

Date: 07-2002

DOI: 10.1016/S1352-2310(02)00293-5

Drosophila 3′ UTRs Are More Complex than Protein-Coding Sequences

Publisher: Public Library of Science (PLoS)

Date: 13-05-2014

DOI: 10.1371/JOURNAL.PONE.0097336

Case Studies in Bayesian Statistical Modelling and Analysis. Wiley Series in Probability and Statistics. Edited by Clair L. Alston, Kerrie L. Mengersen, and Anthony N. Pettitt. Hoboken (New Jer

Publisher: University of Chicago Press

Date: 09-2015

DOI: 10.1086/682596

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Publisher: Elsevier BV

Date: 12-2011

DOI: 10.1016/J.CSDA.2011.06.022

Association between indoor air pollution measurements and respiratory health in women and children in Lao PDR

Publisher: Hindawi Limited

Date: 15-09-2011

DOI: 10.1111/J.1600-0668.2010.00679.X

Abstract: This article presents the results of a study on the association between measured air pollutants and the respiratory health of resident women and children in Lao PDR, one of the least developed countries in Southeast Asia. The study, commissioned by the World Health Organisation, included PM(10), CO and NO(2) measurements made inside 181 dwellings in nine districts within two provinces in Lao PDR over a 5-month period (12/05-04/06), and respiratory health information (via questionnaires and peak expiratory flow rate (PEFR) measurements) for all residents in the same dwellings. Adjusted odds ratios were calculated separately for each health outcome using binary logistic regression. There was a strong and consistent positive association between NO(2) and CO for almost all questionnaire-based health outcomes for both women and children. Women in dwellings with higher measured NO(2) had more than triple of the odds of almost all of the health outcomes, and higher concentrations of NO(2) and CO were significantly associated with lower PEFR. This study supports a growing literature confirming the role of indoor air pollution in the burden of respiratory disease in developing countries. The results will directly support changes in health and housing policy in Lao PDR. This is the first study that investigated indoor air quality and its impact within residential dwellings in Lao PDR, which is one of the poorest and least developed countries in south-east Asia, with a life-expectancy of 56 years in 2008. While there have been other studies published on indoor air quality in other developing countries, the situation in Laos is different because the majority of houses in Laos used wood stoves, and therefore, emissions from wood burning are the dominant sources of indoor air pollution. In other countries, and studies, while emission from wood burning was investigated, wood was rarely the main or the only fuel used, as the houses used in addition (or solely) dung, kerosene or coal. The study quantified, for the first time, concentrations in houses two provinces in Laos PDR and shed light on the impact of human activities and urban design on pollutant concentrations and respiratory health. This study contributes to the accumulation of evidence to provide more reliable estimates of risk and a more informed basis for decision-making by concerned governments and communities.

Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef

Publisher: Elsevier BV

Date: 11-2017

DOI: 10.1016/J.ECOLMODEL.2017.09.006

Change Point Estimation in Monitoring Survival Time

Publisher: Public Library of Science (PLoS)

Date: 16-03-2012

DOI: 10.1371/JOURNAL.PONE.0033630

Role of overcrowding in meticillin-resistant Staphylococcus aureus transmission: Bayesian network analysis for a single public hospital

Publisher: Elsevier BV

Date: 06-2011

DOI: 10.1016/J.JHIN.2011.01.016

Abstract: The transmission of multiple antibiotic-resistant organisms (MROs) in hospitals is affected by many inter-related factors. These include the background prevalence of the organism (burden), hand hygiene, the efficiency of patient screening, the isolation or cohorting of carriers, the quality of hospital cleaning, and bed occupancy. In addition, the prevalence of one MRO may influence the transmission of another by occupying isolation beds, and thus reducing isolation resources for the latter. For ex le, the overuse of third generation cephalosporin antibiotics can increase extended-spectrum β-lactamase-producing Klebsiella pneumoniae, thus indirectly influencing the transmission of meticillin-resistant Staphylococcus aureus (MRSA). In order to study this complex system of interrelationships, we have employed a Bayesian network. We report results of the first two years of analysis for a single public hospital. We conclude that, within this institution, the association between high bed occupancy and increased transmission of MRSA may be subject to a dynamic multidimensional threshold and tipping point. This may be influenced by other factors such as MRSA burden and whether the high bed occupancy interferes with preparation and cleaning of beds for new patients and with hand hygiene and efforts to isolate or cohort carriers.

Automated replication of cone beam CT-guided treatments in the Pinnacle3 treatment planning system for adaptive radiotherapy

Publisher: Wiley

Date: 03-10-2015

DOI: 10.1002/JMRS.141

Does temperature modify short-term effects of ozone on total mortality in 60 large eastern US communities? --- An assessment using the NMMAPS data

Publisher: Elsevier BV

Date: 05-2008

DOI: 10.1016/J.ENVINT.2007.10.001

Abstract: Many studies have indicated that ozone is associated with morbidity and mortality. A few studies have reported that the association is heterogeneous across seasons and geographic regions. However, little information is available on whether both temperature and geographic factors simultaneously modify the ozone effect. This study used a Poisson regression model to explore whether temperature modifies the effect of ozone on mortality in the 60 large eastern US communities during April to October, 1987-2000. Results show that temperature modified ozone-mortality associations and that such modification varied across geographic regions. In the northeast region, a 10-ppb increment in ozone was associated with an increase of 2.22% (95% posterior interval [PI]: 1.19%, 3.13%), 3.06% (95% PI: 2.21%, 3.76%) and 6.22% (95% PI: 4.77%, 7.56%) in mortality at low, moderate and high temperature level, respectively, while in the southeast region a 10-ppb increment in ozone was associated with an increase of 1.13% (95% PI:-1.12%, 3.18%), 1.50% (95% PI: 0.22%, 2.81%) and 1.29% (95% PI:-0.33%, 2.96%) in mortality, respectively. We concluded that temperature synergistically modified the ozone-mortality association in the northeast region, but such a pattern was not apparent in the southeast region. Thus, both temperature and geographic factors should be considered in the assessment of ozone effects.

It’s Not Just Conflict That Motivates Killing of Orangutans

Publisher: Public Library of Science (PLoS)

Date: 09-10-2013

DOI: 10.1371/JOURNAL.PONE.0075373

Oil palm–community conflict mapping in Indonesia: A case for better community liaison in planning for development initiatives

Publisher: Elsevier BV

Date: 2017

DOI: 10.1016/J.APGEOG.2016.10.005

Bayesian neural network learning for prediction in the Australian dairy industry

Publisher: No publisher found

Date: 1999

DOI: 10.1007/3-540-48412-4\_33

A Generalized QMRA Beta-Poisson Dose-Response Model

Publisher: Wiley

Date: 05-02-2016

DOI: 10.1111/RISA.12561

Abstract: Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single-hit dose-response models are the most commonly used dose-response models in QMRA. Denoting PI(d) as the probability of infection at a given mean dose d, a three-parameter generalized QMRA beta-Poisson dose-response model, PI(d|α,β,r*), is proposed in which the minimum number of organisms required for causing infection, K

Guidelines for Use of the Approximate Beta-Poisson Dose–Response Model

Publisher: Wiley

Date: 05-10-2017

DOI: 10.1111/RISA.12682

Abstract: For dose-response analysis in quantitative microbial risk assessment (QMRA), the exact beta-Poisson model is a two-parameter mechanistic dose-response model with parameters α>0 and β>0, which involves the Kummer confluent hypergeometric function. Evaluation of a hypergeometric function is a computational challenge. Denoting PI(d) as the probability of infection at a given mean dose d, the widely used dose-response model PI(d)=1-(1+dβ)-α is an approximate formula for the exact beta-Poisson model. Notwithstanding the required conditions α< >1, issues related to the validity and approximation accuracy of this approximate formula have remained largely ignored in practice, partly because these conditions are too general to provide clear guidance. Consequently, this study proposes a probability measure Pr(0 < r (22α̂)0.50 for 0.02<α̂<2 as a rule of thumb to ensure an accurate approximation (e.g., Pr(0 < r 0.99) . This validity measure and rule of thumb were validated by application to all the completed beta-Poisson models (related to 85 data sets) from the QMRA community portal (QMRA Wiki). The results showed that the higher the probability Pr(0 < r < 1 | α̂, β̂), the better the approximation. The results further showed that, among the total 85 models examined, 68 models were identified as valid approximate model applications, which all had a near perfect match to the corresponding exact beta-Poisson model dose-response curve.

Erratum: Confidence limits for the ratio of two rates based on likelihood scores: Non-iterative method (Statistics in Medicine (2003) vol. 22 (2071-2083))

Publisher: Wiley

Date: 25-10-2004

DOI: 10.1002/SIM.2046

Bayesian prediction of winning times for elite swimming events

Publisher: Informa UK Limited

Date: 20-09-2022

DOI: 10.1080/02640414.2021.1976485

Abstract: To develop a statistical model of winning times for international swimming events with the aim of predicting winning time distributions and the probability of winning for the 2020 and 2024 Olympic Games. The data set included first and third place times from all in idual swimming events from the Olympics and World Ch ionships from 1990 to 2019. We compared different model formulations fitted with Bayesian inference to obtain predictive distributions comparisons were based on mean percentage error in out-of-s le predictions of Olympics and World Ch ionships winning swim times from 2011 to 2019. The Bayesian time series regression model, comprising auto-regressive and moving average terms and other predictors, had the smallest mean prediction error of 0.57% (CI 0.46-0.74%). For context, using the respective previous Olympics or World Ch ionships winning time resulted in a mean prediction error of 0.70% (CI 0.59-0.82%). The Olympics were on average 0.5% (CI 0.3-0.7%) faster than World Ch ionships over the study period. The model computes the posterior predictive distribution, which allows coaches and athletes to evaluate the probability of winning given an in idual's swim time, and the probability of being faster or slower than the previous winning time or even the world record.

Wetlands, climate zones and Barmah Forest virus disease in Queensland, Australia

Publisher: Oxford University Press (OUP)

Date: 12-2012

DOI: 10.1016/J.TRSTMH.2012.08.003

Abstract: Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia, but the linkages of the wetlands and climate zones with BFV transmission remain unclear. We aimed to examine the relationship between the wetlands, climate zones and BFV risk in Queensland, Australia. Data on the wetlands, climate zones, population and BFV cases for the period 1992 to 2008 were obtained from relevant government agencies. BFV risk was grouped as low-, medium- and high-level based on BFV incidence percentiles. The buffer zones around each BFV case were made using 1, 5, 10, 15, 20, 25 and 50km distances. We performed a discriminant analysis to determine the differences between wetland classes and BFV risk within each climate zone. The discriminant analyses show that saline 1, riverine and saline tidal influence were the most significant contributors to BFV risk in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. These models had classification accuracies of 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV risk varies with wetland class and climate zone. The discriminant analysis is a useful tool to quantify the links between wetlands, climate zones and BFV risk.

Performance Monitoring in Cardiac Surgery: Application of Statistical Process Control to a Single-site Database

Publisher: Elsevier BV

Date: 08-2013

DOI: 10.1016/J.HLC.2013.01.011

Abstract: Graphical Statistical Process Control (SPC) tools have been shown to promptly identify significant variations in clinical outcomes in a range of health care settings. We explored the application of these techniques to quantitatively inform the routine cardiac surgical (CAS) morbidity and mortality (M&M) review processes at a single site. Baseline clinical and procedural data relating to 5265 consecutive cardiac surgical procedures, performed at St Andrew's War Memorial Hospital (SAWMH) between the 1st January 2003 and the 30th April 2012, were retrospectively evaluated. A range of appropriate clinical outcome indicators (COIs) were developed and evaluated using a combination of Cumulative Sum charts, Exponentially Weighted Moving Average charts and Funnel Plots. Charts were updated regularly and discussed at the cardiac surgery unit's bi-monthly M&M meetings. Risk adjustment (RA) for the COIs was developed and validated for incorporation into the charts to improve monitoring performance. Discrete and aggregated measures, including blood product/reoperation, major acute post-procedural complications, cardiopulmonary bypass duration and Length of Stay/Readmission < 28 days have proved to be valuable measures for monitoring outcomes. Instances of variation in performance identified using the charts were examined thoroughly and could be related to changes in clinical practice (e.g. antifibrinolytic use) as well as differences in in idual operator performance (in some instances, driven by case mix). SPC tools can promptly detect meaningful changes in clinical outcome thereby allowing early intervention to address altered performance. Careful interpretation of charts for group and in idual operators has proven helpful in detecting and differentiating systemic versus in idual variation.

Latent class piecewise linear trajectory modelling for short-term cognition responses after chemotherapy for breast cancer patients

Publisher: Informa UK Limited

Date: 24-03-2010

DOI: 10.1080/02664760902729641

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study: Assessing the viability and sustainability of

Publisher: Elsevier BV

Date: 2014

DOI: 10.1016/J.JENVMAN.2013.12.013

Abstract: Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.

Climate change and dengue: A critical and systematic review of quantitative modelling approaches

Publisher: Springer Science and Business Media LLC

Date: 26-03-2014

DOI: 10.1186/1471-2334-14-167

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Publisher: Elsevier BV

Date: 10-2011

DOI: 10.1016/J.ECOLMODEL.2011.08.013

Ultrasound of paediatric appendicitis and its secondary sonographic signs: providing a more meaningful finding

Publisher: Wiley

Date: 20-01-2016

DOI: 10.1002/JMRS.154

Modelling survival data to account for model uncertainty: A single model or model averaging?

Publisher: Springer Science and Business Media LLC

Date: 12-2013

DOI: 10.1186/2193-1801-2-665

Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling

Publisher: Springer Science and Business Media LLC

Date: 11-02-2015

DOI: 10.1007/S10651-015-0311-1

Identification of two‐phase recovery for interpretation of coral reef monitoring data

Publisher: Wiley

Date: 27-10-2022

DOI: 10.1111/1365-2664.14039

Abstract: The world's coral reefs are under threat as climate change causes increases in frequency and severity of acute thermal stress. This is compounded by chronic pressures including rises in sea surface temperature, overfishing and decline in water quality. Monitoring to understand the recovery dynamics of corals is paramount to enable effective management of coral reefs. While detailed mechanistic models provide insight into reef recovery patterns, colony scale monitoring is not viable for reefs over a large geographical extent, such as the Great Barrier Reef (GBR). Consequently, aggregated coral cover data are utilised in practice and phenomenological analysis directly applicable to these monitoring programmes is essential for reef health reporting. These analyses are especially challenging for assessment of recovery potential of reefs reduced to very low coral cover ( %) after disturbance since standard modelling assumptions may not hold. Through the application of an innovative diagnostic approach modified from methods used in cancer cell biology, we found that almost 50% of reefs recovering from low cover exhibited a previously undocumented initial phase of slower growth per unit cover before sigmoid coral cover recovery trajectories were observed. Without properly accounting for these reduced growth periods, the expected performance of reefs may be overestimated immediately after disturbance events. The presence of two‐phase recovery patterns has a profound negative impact on the continued provision of ecological services from these reefs as major disturbance frequencies increase. Projections show that if the time between major disturbances is years, then reefs with two‐phase growth are never likely to reach 15% cover. Synthesis and applications . This work provides a method to detect two‐phase recovery, the tendency for observed reef recovery to be slower than expected after a major disturbance. This phenomenon is observed across the Great Barrier Reef with serious implications for recovery potential as major disturbances occur more frequently due to climate change. Identification of reefs prone to two‐phase recovery can assist the triage of reefs for intervention actions in response to disturbances. Understanding mechanisms will inform interventions and management actions targeted towards the unique marine ecosystems across the world's reefs.

Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods on twin data

Publisher: Springer Science and Business Media LLC

Date: 19-03-2009

DOI: 10.1007/S00439-009-0652-7

Abstract: Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods-latent class analysis, grade of membership and grade of membership "fuzzy" clustering (Fanny)-to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Framework for assessing and easing global COVID-19 travel restrictions

Publisher: Springer Science and Business Media LLC

Date: 28-04-2022

DOI: 10.1038/S41598-022-10678-Y

Abstract: During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network meta-population model to compare the effectiveness of international travel policies, with a focus on evaluating the benefit of policy coordination. Because country-level epidemiological parameters are unknown, they need to be estimated from data we accomplished this using approximate Bayesian computation, given the nature of our complex stochastic disease transmission model. Based on simulation and theoretical insights we find that, under our proposed policy, international airline travel may resume up to 58% of the pre-pandemic level with pandemic control comparable to that of a complete shutdown of all airline travel. Our results demonstrate that global coordination is necessary to allow for maximum travel with minimum effect on viral spread.

Executive Function and Postural Instability in People with Parkinson’s Disease

Publisher: Hindawi Limited

Date: 2014

DOI: 10.1155/2014/684758

Abstract: The specific aspects of cognition contributing to balance and gait have not been clarified in people with Parkinson’s disease (PD). Twenty PD participants and twenty age- and gender-matched healthy controls were assessed on cognition and clinical mobility tests. General cognition was assessed with the Mini Mental State Exam and Addenbrooke’s Cognitive Exam. Executive function was evaluated using the Trail Making Tests (TMT-A and TMT-B) and a computerized cognitive battery which included a series of choice reaction time (CRT) tests. Clinical gait and balance measures included the Tinetti, Timed Up & Go, Berg Balance, and Functional Reach tests. PD participants performed significantly worse than the controls on the tests of cognitive and executive function, balance, and gait. PD participants took longer on Trail Making Tests, CRT-Location, and CRT-Colour (inhibition response). Furthermore, executive function, particularly longer times on CRT-Distracter and greater errors on the TMT-B, was associated with worse balance and gait performance in the PD group. Measures of general cognition were not associated with balance and gait measures in either group. For PD participants, attention and executive function were impaired. Components of executive function, particularly those involving inhibition response and distracters, were associated with poorer balance and gait performance in PD.

Bayesian estimation of quantile distributions

Publisher: Springer Science and Business Media LLC

Date: 25-07-2009

DOI: 10.1007/S11222-008-9083-X

Considering ethnicity in teaching and learning statistics: should I worry about where my students come from?

Publisher: Hindawi Limited

Date: 2003

DOI: 10.1155/S1173912603000129

Abstract: In the past decade there has been strong interest in the special needs of overseas students attending Australian universities, with respect to teaching and learning. This paper reports on three action research studies that address the question of whether such issues remain in the teaching and learning of statistics in particular.

Combining Expert Opinions in Prior Elicitation

Publisher: Institute of Mathematical Statistics

Date: 09-2012

DOI: 10.1214/12-BA717

Strategies to strengthen public health inputs to water policy in response to climate change: An Australian perspective

Publisher: SAGE Publications

Date: 03-2011

DOI: 10.1177/1010539510397038

Abstract: Under current climate change projections, the capacity to provide safe drinking water to Australian communities will be challenged. Part of this challenge is the lack of an adaptive governance strategy that transcends jurisdictional boundaries to support integrated policy making, regulation, or infrastructural adaptation. Consequently, some water-related health hazards may not be adequately captured or forecast under existing water resource management policies to ensure safe water supplies. Given the high degree of spatial and temporal variability in climate conditions experienced by Australian communities, new strategies for national health planning and prioritization for safe water supplies are warranted. The challenges facing public health in Australia will be to develop flexible and robust governance strategies that strengthen public health input to existing water policy, regulation, and surveillance infrastructure through proactive risk planning, adopting new technologies, and intersectoral collaborations. The proposed approach could assist policy makers avert or minimize risk to communities arising from changes in climate and water provisions both in Australia and in the wider Asia Pacific region.

Utility of Bayesian networks in QMRA-based evaluation of risk reduction options for recycled water

Publisher: Elsevier BV

Date: 2016

DOI: 10.1016/J.SCITOTENV.2015.10.030

Abstract: Quantitative microbial risk assessment (QMRA), the current method of choice for evaluating human health risks associated with disease-causing microorganisms, is often constrained by issues such as availability of required data, and inability to incorporate the multitude of factors influencing risk. Bayesian networks (BNs), with their ability to handle data paucity, combine quantitative and qualitative information including expert opinions, and ability to offer a systems approach to characterisation of complexity, are increasingly recognised as a powerful, flexible tool that overcomes these limitations. We present a QMRA expressed as a Bayesian network (BN) in a wastewater reuse context, with the objective of demonstrating the utility of the BN method in health risk assessments, particularly for evaluating a range of exposure and risk mitigation scenarios. As a case study, we examine the risk of norovirus infection associated with wastewater-irrigated lettuce. A Bayesian network was developed following a QMRA approach, using published data, and reviewed by domain experts using a participatory process. Employment of a BN facilitated rapid scenario evaluations, risk minimisation, and predictive comparisons. The BN supported exploration of conditions required for optimal outcomes, as well as investigation of the effect on the reporting nodes of changes in 'upstream' conditions. A significant finding was the indication that if maximum post-treatment risk mitigation measures were implemented, there was a high probability (0.84) of a low risk of infection regardless of fluctuations in other variables, including norovirus concentration in treated wastewater. BNs are useful in situations where insufficient empirical data exist to satisfy QMRA requirements and they are exceptionally suited to the integration of risk assessment and risk management in the QMRA context. They allow a comprehensive visual appraisal of major influences in exposure pathways, and rapid interactive risk assessment in multifaceted water reuse scenarios.

Processing passengers efficiently: An analysis of airport processing times for international passengers

Publisher: Elsevier BV

Date: 10-2015

DOI: 10.1016/J.JAIRTRAMAN.2015.06.016

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

Publisher: Springer Science and Business Media LLC

Date: 21-07-2021

DOI: 10.1007/S11121-021-01279-8

Why Don't We Ask? A Complementary Method for Assessing the Status of Great Apes

Publisher: Public Library of Science (PLoS)

Date: 31-03-2011

DOI: 10.1371/JOURNAL.PONE.0018008

First integrative trend analysis for a great ape species in Borneo

Publisher: Springer Science and Business Media LLC

Date: 07-07-2017

DOI: 10.1038/S41598-017-04435-9

Abstract: For many threatened species the rate and drivers of population decline are difficult to assess accurately: species’ surveys are typically restricted to small geographic areas, are conducted over short time periods, and employ a wide range of survey protocols. We addressed methodological challenges for assessing change in the abundance of an endangered species. We applied novel methods for integrating field and interview survey data for the critically endangered Bornean orangutan ( Pongo pygmaeus ), allowing a deeper understanding of the species’ persistence through time. Our analysis revealed that Bornean orangutan populations have declined at a rate of 25% over the last 10 years. Survival rates of the species are lowest in areas with intermediate rainfall, where complex interrelations between soil fertility, agricultural productivity, and human settlement patterns influence persistence. These areas also have highest threats from human-wildlife conflict. Survival rates are further positively associated with forest extent, but are lower in areas where surrounding forest has been recently converted to industrial agriculture. Our study highlights the urgency of determining specific management interventions needed in different locations to counter the trend of decline and its associated drivers.

Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities

Publisher: Elsevier BV

Date: 03-2009

DOI: 10.1016/J.JAEROSCI.2008.11.002

Weather Variability, Sunspots, and the Blooms of Cyanobacteria

Publisher: Springer Science and Business Media LLC

Date: 03-2009

DOI: 10.1007/S10393-009-0223-3

Abstract: The roles of weather variability and sunspots in the occurrence of cyanobacteria blooms, were investigated using cyanobacteria cell data collected from the Fred Haigh Dam, Queensland, Australia. Time series generalized linear model and classification and regression tree (CART) model were used in the analysis. Data on notified cell numbers of cyanobacteria and weather variables over the periods 2001 and 2005 were provided by the Australian Department of Natural Resources and Water, and Australian Bureau of Meteorology, respectively. The results indicate that monthly minimum temperature (relative risk [RR]: 1.13, 95% confidence interval [CI]: 1.02-1.25) and rainfall (RR: 1.11 95% CI: 1.03-1.20) had a positive association, but relative humidity (RR: 0.94 95% CI: 0.91-0.98) and wind speed (RR: 0.90 95% CI: 0.82-0.98) were negatively associated with the cyanobacterial numbers, after adjustment for seasonality and auto-correlation. The CART model showed that the cyanobacteria numbers were best described by an interaction between minimum temperature, relative humidity, and sunspot numbers. When minimum temperature exceeded 18 degrees C and relative humidity was under 66%, the number of cyanobacterial cells rose by 2.15-fold. We conclude that weather variability and sunspot activity may affect cyanobacteria blooms in dams.

Bayesian Estimation of Small Effects in Exercise and Sports Science

Publisher: Public Library of Science (PLoS)

Date: 13-04-2016

DOI: 10.1371/JOURNAL.PONE.0147311

What is an expert? A systems perspective on expertise

Publisher: Wiley

Date: 16-06-2012

DOI: 10.1002/ECE3.926

Assessing the relationship between global warming and mortality: Lag effects of temperature fluctuations by age and mortality categories

Publisher: Elsevier BV

Date: 07-2011

DOI: 10.1016/J.ENVPOL.2011.03.039

Abstract: Although interests in assessing the relationship between temperature and mortality have arisen due to climate change, relatively few data are available on lag structure of temperature-mortality relationship, particularly in the Southern Hemisphere. This study identified the lag effects of mean temperature on mortality among age groups and death categories using polynomial distributed lag models in Brisbane, Australia, a subtropical city, 1996-2004. For a 1 °C increase above the threshold, the highest percent increase in mortality on the current day occurred among people over 85 years (7.2% (95% CI: 4.3%, 10.2%)). The effect estimates among cardiovascular deaths were higher than those among all-cause mortality. For a 1 °C decrease below the threshold, the percent increases in mortality at 21 lag days were 3.9% (95% CI: 1.9%, 6.0%) and 3.4% (95% CI: 0.9%, 6.0%) for people aged over 85 years and with cardiovascular diseases, respectively. These findings may have implications for developing intervention strategies to reduce and prevent temperature-related mortality.

ARC Centre Of Excellence For Mathematical And Statistical Frontiers

Location: Australia

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Bond University

Location: Australia

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Queensland University Of Technology

Location: Australia

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Central Queensland University Brisbane Campus

Location: Australia

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Colorado State University

Location: United States of America

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(Bayesian Statistical Methods For Enhancing Evidence-based Practice In Australia's Hospitals)

Start Date: 2006

End Date: 2009

Funder: Australian Research Council

(Dynamic Spatio-temporal Approach To Environmental Health Modelling)

Start Date: 2003

End Date: 2005

Funder: Australian Research Council

(Measuring And Presenting Uncertainty In Complex Natural Resource Monitoring Programs)

Start Date: 2006

End Date: 2008

Funder: Australian Research Council

(A Toolkit Of Statistical Methodology For A State-of-the-art Software And Decision Support System For Forest Assessment Using New Airborne Data)

Start Date: 2002

End Date: 2004

Funder: Australian Research Council

(Bayesian Methodology For Analysis Of Genome Data With Focus On The Livestock Industry)

Start Date: 2003

End Date: 2005

Funder: Australian Research Council

(New Bayesian Methodology For Understanding Complex Systems Using Hidden Markov Models And Expert Opinion, Environmental, Robotics And Genomics Applications)

Start Date: 2003

End Date: 2005

Funder: Australian Research Council

(Elicitation And Integration Of Expert Information For Natural Resource Management With A Focus On Water)

Start Date: 2005

End Date: 2006

Funder: Australian Research Council

(National Macadamia Conservation Program)

Start Date: 2005

End Date: 2008

Funder: Australian Research Council

Quantification Of Interactions During The Dispersion Of Corona Ions And Airborne Particles Near Power Lines

Start Date: 2005

End Date: 2008

Funder: Australian Research Council

(Making The Most Of Database Information In Patient-Based Decision-Making - A Bayesian Approach)

Start Date: 2007

End Date: 2009

Funder: Australian Research Council

(Doing Bayesian Statistics Better: An Inter-Disciplinary Perspective For Improving Models, Priors, Design And Applications)

Start Date: 2006

End Date: 2008

Funder: Australian Research Council

(International Networks In Applied Bayesian Statistics: Improving Australia''s Knowledge Through Intelligent Data Analysis And Modelling)

Start Date: 2008

End Date: 2010

Funder: Australian Research Council

Project LE140100061

Start Date: 2014

End Date: 2014

Funder: Australian Research Council

Centre For Research Excellence In Reducing Healthcare Associated Infection

Start Date: 2012

End Date: 2016

Funder: National Health and Medical Research Council

Centre For Complex Dynamic Systems And Control

Start Date: 2003

End Date: 2010

Funder: Australian Research Council

Project DP140103564

Start Date: 2014

End Date: 2016

Funder: Australian Research Council

(From Science To Policy: Quantifying And Managing The Risk Of Mosquito Borne Disease In The Context Of Climate Change)

Start Date: 2011

End Date: 2013

Funder: Australian Research Council

Statistical Review And Re-Design Of The National Bee Pest Surveillance Program

Start Date: 2015