ORCID Profile
0000-0001-9222-8763
Current Organisation
Queensland University of Technology
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Applied Statistics | Statistical Theory | Statistics | Stochastic Analysis and Modelling | Systems biology | Microbiology not elsewhere classified | Biological mathematics | Applied mathematics | Biological physics
Expanding Knowledge in the Mathematical Sciences | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Medical and Health Sciences |
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.
Publisher: Elsevier BV
Date: 2013
Publisher: Walter de Gruyter GmbH
Date: 22-06-2011
Publisher: Elsevier BV
Date: 04-2014
Publisher: Wiley
Date: 30-10-2012
Publisher: Wiley
Date: 16-10-2013
DOI: 10.1111/BIOM.12081
Abstract: In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo s ling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite ex le involves a multivariate process and we assess the loss of information from not observing all variables.
Publisher: Wiley
Date: 13-08-2017
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2021
DOI: 10.1101/2021.11.24.469957
Abstract: Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalisation of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to s le size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalisation assays and similar experiments that probe cellular dynamical processes.
Publisher: Springer Science and Business Media LLC
Date: 20-02-2017
Publisher: Institute of Mathematical Statistics
Date: 03-2018
DOI: 10.1214/16-BA1045
Publisher: Elsevier BV
Date: 05-2015
DOI: 10.1016/J.MBS.2015.02.010
Abstract: Wound healing and tumour growth involve collective cell spreading, which is driven by in idual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2-6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.
Publisher: Informa UK Limited
Date: 11-07-2022
Publisher: Public Library of Science (PLoS)
Date: 16-11-2022
DOI: 10.1371/JOURNAL.PCBI.1010599
Abstract: Parameter estimation for mathematical models of biological processes is often difficult and depends significantly on the quality and quantity of available data. We introduce an efficient framework using Gaussian processes to discover mechanisms underlying delay, migration, and proliferation in a cell invasion experiment. Gaussian processes are leveraged with bootstrapping to provide uncertainty quantification for the mechanisms that drive the invasion process. Our framework is efficient, parallelisable, and can be applied to other biological problems. We illustrate our methods using a canonical scratch assay experiment, demonstrating how simply we can explore different functional forms and develop and test hypotheses about underlying mechanisms, such as whether delay is present. All code and data to reproduce this work are available at github.com/DanielVandH/EquationLearning.jl .
Publisher: Cold Spring Harbor Laboratory
Date: 19-06-2019
DOI: 10.1101/668848
Abstract: Fibrosis, the pathological excess of fibroblast activity, is a significant health issue that hinders the function of many organs in the body, in some cases fatally. However, the severity of fibrosis-derived conditions depends on both the positioning of fibrotic affliction, and the microscopic patterning of fibroblast-deposited matrix proteins within afflicted regions. Variability in an in idual’s manifestation of a type of fibrosis is an important factor in explaining differences in symptoms, optimum treatment and prognosis, but a need for ex vivo procedures and a lack of experimental control over conflating factors has meant this variability remains poorly understood. In this work, we present a computational methodology for the generation of patterns of fibrosis microstructure, demonstrating the technique using histological images of four types of cardiac fibrosis. Our generator and automated tuning method prove flexible enough to capture each of these very distinct patterns, allowing for rapid generation of new realisations for high-throughput computational studies. We also demonstrate via simulation, using the generated fibrotic patterns, the importance of micro-scale variability by showing significant differences in electrophysiological impact even within a single class of fibrosis.
Publisher: Institute of Mathematical Statistics
Date: 09-2019
DOI: 10.1214/18-BA1129
Publisher: IOP Publishing
Date: 23-10-2023
Publisher: Elsevier BV
Date: 11-2017
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 12-2018
Publisher: The Royal Society
Date: 05-2022
Abstract: Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to s le size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.
Publisher: Public Library of Science (PLoS)
Date: 07-12-2015
Publisher: Wiley
Date: 30-10-2012
Publisher: Wiley
Date: 25-03-2010
DOI: 10.1111/J.1541-0420.2010.01410.X
Abstract: We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test ex le involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 11-09-2011
Publisher: PeerJ
Date: 12-04-2018
DOI: 10.7717/PEERJ.4620
Abstract: There is convincing evidence for the benefits of resistance training on vertical jump improvements, but little evidence to guide optimal training prescription. The inability to detect small between modality effects may partially reflect the use of ANOVA statistics. This study represents the results of a sub-study from a larger project investigating the effects of two resistance training methods on load carriage running energetics. Bayesian statistics were used to compare the effectiveness of isoinertial resistance against speed-power training to change countermovement jump (CMJ) and squat jump (SJ) height, and joint energetics. Active adults were randomly allocated to either a six-week isoinertial ( n = 16 calf raises, leg press, and lunge), or a speed-power training program ( n = 14 countermovement jumps, hopping, with hip flexor training to target pre-swing running energetics). Primary outcome variables included jump height and joint power. Bayesian mixed modelling and Functional Data Analysis were used, where significance was determined by a non-zero crossing of the 95% Bayesian Credible Interval (CrI). The gain in CMJ height after isoinertial training was 1.95 cm (95% CrI [0.85–3.04] cm) greater than the gain after speed-power training, but the gain in SJ height was similar between groups. In the CMJ, isoinertial training produced a larger increase in power absorption at the hip by a mean 0.018% (equivalent to 35 W) (95% CrI [0.007–0.03]), knee by 0.014% (equivalent to 27 W) (95% CrI [0.006–0.02]) and foot by 0.011% (equivalent to 21 W) (95% CrI [0.005–0.02]) compared to speed-power training. Short-term isoinertial training improved CMJ height more than speed-power training. The principle adaptive difference between training modalities was at the level of hip, knee and foot power absorption.
Publisher: Informa UK Limited
Date: 06-06-2018
Publisher: Elsevier BV
Date: 09-2011
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2021
DOI: 10.1137/19M1286694
Publisher: Informa UK Limited
Date: 31-07-2017
Publisher: Informa UK Limited
Date: 25-10-2022
Publisher: Informa UK Limited
Date: 11-08-2020
Publisher: Computing in Cardiology
Date: 14-09-2017
Publisher: Cold Spring Harbor Laboratory
Date: 13-05-2022
DOI: 10.1101/2022.05.12.491596
Abstract: Parameter estimation for mathematical models of biological processes is often difficult and depends significantly on the quality and quantity of available data. We introduce an efficient frame-work using Gaussian processes to discover mechanisms underlying delay, migration, and proliferation in a cell invasion experiment. Gaussian processes are leveraged with bootstrapping to provide uncertainty quantification for the mechanisms that drive the invasion process. Our frame-work is efficient, parallelisable, and can be applied to other biological problems. We illustrate our methods using a canonical scratch assay experiment, demonstrating how simply we can explore different functional forms and develop and test hypotheses about underlying mechanisms, such as whether delay is present. All code and data to reproduce this work are available at github.com/DanielVandH/EquationLearning.jl . 1 In this work we introduce uncertainty quantification into equation learning methods, such as physics-informed and biologically-informed neural networks. Our framework is computationally efficient and applicable to problems with unknown nonlinear mechanisms that we wish to learn from experiments where only sparse noisy data are available. We demonstrate our methods on a canonical scratch assay experiment from cell biology and show the underlying mechanisms can be learned, providing confidence intervals for functional forms and for solutions to partial differential equation models believed to describe the experiment.
Publisher: Institute of Mathematical Statistics
Date: 03-2018
DOI: 10.1214/16-BA1033
Publisher: Informa UK Limited
Date: 06-01-2021
Publisher: Institute of Mathematical Statistics
Date: 09-2016
DOI: 10.1214/15-BA977
Publisher: The Royal Society
Date: 07-2018
Abstract: Functional response models are important in understanding predator–prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller s le sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle ( Propylea quatuordecimpunctata L.) preying on the black bean aphid ( Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications.
Publisher: Elsevier BV
Date: 06-2012
Publisher: The Royal Society
Date: 06-2021
Abstract: We compute profile likelihoods for a stochastic model of diffusive transport motivated by experimental observations of heat conduction in layered skin tissues. This process is modelled as a random walk in a layered one-dimensional material, where each layer has a distinct particle hopping rate. Particles are released at some location, and the duration of time taken for each particle to reach an absorbing boundary is recorded. To explore whether these data can be used to identify the hopping rates in each layer, we compute various profile likelihoods using two methods: first, an exact likelihood is evaluated using a relatively expensive Markov chain approach and, second, we form an approximate likelihood by assuming the distribution of exit times is given by a Gamma distribution whose first two moments match the moments from the continuum limit description of the stochastic model. Using the exact and approximate likelihoods, we construct various profile likelihoods for a range of problems. In cases where parameter values are not identifiable, we make progress by re-interpreting those data with a reduced model with a smaller number of layers.
Publisher: Human Kinetics
Date: 06-2016
Abstract: Published energy cost data for children and adolescents are lacking. The purpose of this study was to measure and describe developmental trends in the energy cost of 12 physical activities commonly performed by youth. A mixed age cohort of 209 participants completed 12 standardized activity trials on 4 occasions over a 3-year period (baseline, 12-months, 24-months, and 36-months) while wearing a portable indirect calorimeter. Bayesian hierarchical regression was used to link growth curves from each age cohort into a single curve describing developmental trends in energy cost from age 6 to 18 years. For sedentary and light-intensity household chores, YOUTH METs (METy) remained stable or declined with age. In contrast, METy values associated with brisk walking, running, basketball, and dance increased with age. The reported energy costs for specific activities will contribute to efforts to update and expand the youth compendium.
Publisher: Public Library of Science (PLoS)
Date: 28-11-2022
DOI: 10.1371/JOURNAL.PCBI.1010734
Abstract: Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.
Publisher: Institute of Mathematical Statistics
Date: 02-2015
DOI: 10.1214/14-STS498
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.
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.PHYSBEH.2018.06.026
Abstract: This study examined the effect of environmental temperature deception on the rating of perceived exertion (RPE) during 30 min of fixed-intensity cycling in the heat. Eleven trained male cyclists completed an incremental cycling test and four experimental trials. Trials consisted of 30 min cycling at 50% P RPE was higher in the heat compared to CON, but not statistically different between the hot conditions (mean [95% credible interval] DEC Participants were under the impression they were cycling in different environments however, this did not influence RPE. These data suggest that for trained cyclists, an awareness of environmental temperature does not contribute to the generation of RPE when exercising at a fixed intensity in the heat.
Publisher: Informa UK Limited
Date: 19-02-2019
Publisher: Springer Science and Business Media LLC
Date: 31-08-2018
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Wiley
Date: 15-06-2016
DOI: 10.1111/INSR.12107
Publisher: BMJ
Date: 02-2017
Publisher: American Association for the Advancement of Science (AAAS)
Date: 23-09-2022
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.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2022
DOI: 10.1007/S11222-022-10078-2
Abstract: Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from the candidate models than previous approaches using approximate Bayesian computation. Further, it is easy to assess the performance of the optimal design through the misclassification error rate. The approach is particularly useful in the presence of models with intractable likelihoods but can also provide computational advantages when the likelihoods are manageable.
Publisher: Elsevier BV
Date: 05-2023
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.
Publisher: Elsevier BV
Date: 12-2018
Publisher: BMJ
Date: 06-01-2015
DOI: 10.1136/SEXTRANS-2014-051631
Abstract: Directly measuring disease incidence in a population is difficult and not feasible to do routinely. We describe the development and application of a new method for estimating at a population level the number of incident genital chlamydia infections, and the corresponding incidence rates, by age and sex using routine surveillance data. A Bayesian statistical approach was developed to calibrate the parameters of a decision-pathway tree against national data on numbers of notifications and tests conducted (2001-2013). Independent beta probability density functions were adopted for priors on the time-independent parameters the shapes of these beta parameters were chosen to match prior estimates sourced from peer-reviewed literature or expert opinion. To best facilitate the calibration, multivariate Gaussian priors on (the logistic transforms of) the time-dependent parameters were adopted, using the Matérn covariance function to favour small changes over consecutive years and across adjacent age cohorts. The model outcomes were validated by comparing them with other independent empirical epidemiological measures, that is, prevalence and incidence as reported by other studies. Model-based estimates suggest that the total number of people acquiring chlamydia per year in Australia has increased by ∼120% over 12 years. Nationally, an estimated 356 000 people acquired chlamydia in 2013, which is 4.3 times the number of reported diagnoses. This corresponded to a chlamydia annual incidence estimate of 1.54% in 2013, increased from 0.81% in 2001 (∼90% increase). We developed a statistical method which uses routine surveillance (notifications and testing) data to produce estimates of the extent and trends in chlamydia incidence.
Publisher: The Royal Society
Date: 08-2016
Abstract: Between-subject and within-subject variability is ubiquitous in biology and physiology, and understanding and dealing with this is one of the biggest challenges in medicine. At the same time, it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube s ling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler–Reuter cardiac electrophysiological model. We show improved efficiency for SMC that produces similar responses to LHS when making out-of-s le predictions in the presence of a simulated drug block. Finally, we show the performance of our approach on a complex atrial electrophysiological model, namely the Courtemanche–Ramirez–Nattel model.
Publisher: Cold Spring Harbor Laboratory
Date: 15-09-2022
DOI: 10.1101/2022.09.13.507714
Abstract: Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures in idual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo . To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.
Publisher: Cold Spring Harbor Laboratory
Date: 21-04-2021
DOI: 10.1101/2021.04.20.440712
Abstract: We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labeling with proliferation, migration, and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all the parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, whilst difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at ichaelcarr-stats/FUCCI .
Publisher: Institute of Mathematical Statistics
Date: 08-2017
DOI: 10.1214/16-STS604
Publisher: Informa UK Limited
Date: 10-10-2023
DOI: 10.1080/17461391.2022.2124386
Abstract: This randomized cross-over study tested the hypothesis that heat acclimation training would detrimentally affect sleep variables and alter incidental physical activity compared to a thermoneutral training control condition. Eight recreationally trained males (⩒O
Publisher: Cold Spring Harbor Laboratory
Date: 21-04-2020
DOI: 10.1101/2020.04.21.052936
Abstract: Transmissible interfering dengue particles (DENV–TIPs) are engineered dengue virus mutants which are defective and can replicate only with the help of dengue wild–type virus (DENV). In vitro studies have found that when DENV–TIPs and DENV coinfect a cell, they compete for viral genomes and cell proteins for replication and packaging, and DENV–TIPs outperform DENV in this process. Thus, it is hypothesised that DENV–TIPs may be used as a novel therapeutic agent. However, the effectiveness of DENV–TIPs as an antiviral agent is yet to be explored at an epidemiological scale. We present a mathematical model for the replication of DENV and DENV–TIPs as they interact with human host cells, accounting for the effectiveness of DENV–TIPs in blocking DENV from coinfected cells. We fit the model to sequentially measured plasma viral titre data from primary and secondary dengue serotype 1 infected patients in Vietnam. We show that variation in initial DENV load is sufficient to recreate the observed variation between patients. Parameter estimates, differing in primary and secondary infections, do not confirm a significant difference between these two types of infection. We use our model to investigate the potential impact of DENV–TIPs as an antiviral agent. We conclude that, when the effectiveness of DENV–TIPs in inhibiting DENV from coinfected cells is at least 80%, a dose as high as 10 12 copies per millilitre of blood is required to reduce duration of infection and peak DENV serotype 1 infection level at any time point of infection. This work provides a quantitative understanding of the relationship between DENV–TIPs levels and their efficiency in clearing dengue viral infection. It will guide future development of mechanistic models of how DENV–TIPs might contribute as an antiviral agent in limiting natural dengue infection. Inhibition of dengue wild–type virus (DENV) by transmissible interfering dengue particles (DENV–TIPs) is seen in some in vitro studies, and it is hypothesised that DENV–TIPs may be used as a therapeutic agent. However, the efficiency of DENV–TIPs in limiting DENV infection in patients is yet to be explored at an epidemiological scale. Using data collected from dengue serotype 1 infected patients, we model how DENV replicates in an infected patient and how effective DENV–TIPs are in controlling that replication. Our results are of use in the evaluation of DENV–TIPs as a potential antiviral agent.
Publisher: Elsevier BV
Date: 12-2015
Publisher: Springer Science and Business Media LLC
Date: 05-08-2016
Publisher: Elsevier BV
Date: 02-2014
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.
Publisher: The Royal Society
Date: 27-03-2023
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’.
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.
Publisher: Informa UK Limited
Date: 26-06-2023
Publisher: Institute of Mathematical Statistics
Date: 03-2018
DOI: 10.1214/17-BA1071
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.
Publisher: Wiley
Date: 28-11-2007
DOI: 10.1111/J.1541-0420.2007.00933.X
Abstract: Summary Methicillin‐resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health‐care workers that describes, among other processes, transmission between uncolonized patients and colonized health‐care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health‐care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) in idual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health‐care workers from the model, uses Markov chain Monte Carlo and in idual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.
Publisher: Frontiers Media SA
Date: 28-08-2018
Publisher: Institute of Mathematical Statistics
Date: 06-2016
DOI: 10.1214/15-BA950
Publisher: Springer Science and Business Media LLC
Date: 04-10-2019
Publisher: The Royal Society
Date: 03-2020
DOI: 10.1098/RSOS.191315
Abstract: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to two ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
Publisher: Center for Open Science
Date: 28-03-2020
Abstract: This commentary discusses data and code sharing in sport and exercise science research.
Publisher: Springer Science and Business Media LLC
Date: 09-12-2014
Publisher: The Royal Society
Date: 09-2021
Abstract: We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at ichaelcarr-stats/FUCCI .
Publisher: Wiley
Date: 10-07-2023
Abstract: Aedes aegypti (L.) is an invasive mosquito responsible for vectoring diseases such as dengue, Zika and Chikungunya. Dengue affects a large proportion of the global population, with the World Health Organization estimating that half the global population is at risk, with 390 million infections occurring each year. Control of mosquito vector populations over large geographical scales can be improved and made economically viable by computer models with potential to aid decision support. We introduce a method for estimating mosquito abundance (population size) over time in biocontrol programmes that involve the release of sterilised insects. We employ Bayesian state‐space modelling to provide insight into population trajectories using data from an application of incompatible insect technique (IIT) biological control, in Far North Queensland, Australia. The general approach could be adapted to other insect species. We demonstrate how the modelling approach can estimate trajectories of abundance over time as an unmarked –release–recapture analysis. Additionally, it provides a means for quantifying population suppression in IIT programmes (a statistic that can be challenging to estimate in practice) using counterfactuals. Modelling results show that estimated population trajectories exhibit similar temporal patterns to raw trapping rate data collected in the field, for ex le, the presence of peaks (and troughs) associated with the timing of rainfall events. Additional confidence in our model was demonstrated through a cross‐validation study where we left out each of the six landscapes from our dataset, fit the model using the remaining five regions and assessed its predictive skill. Modelled counterfactuals allowed us to estimate that population suppression in treated landscapes was 95%–99%. Synthesis and applications . Our model can provide valuable insights that can shape decision support systems in sterile insect technique and incompatible insect technique programmes operating over large geographical scales. The model helps determine how many sterile/incompatible insects should be released over time and how population control is progressing (via use of counterfactual scenarios). These outcomes are achieved because the model provides estimates of wild‐type populations over time, even when there has been no differentiation between sterile/incompatible and wild‐type insects caught in traps.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 31-07-2012
DOI: 10.1007/S10928-012-9265-1
Abstract: Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of s ling windows is required, along with the optimal s ling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive s ling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.
Publisher: MDPI AG
Date: 05-03-2015
DOI: 10.3390/E17031063
Publisher: Oxford University Press (OUP)
Date: 13-01-2011
DOI: 10.1111/J.1467-9876.2010.00747.X
Abstract: We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.
Publisher: Informa UK Limited
Date: 02-01-2014
Publisher: Springer International Publishing
Date: 08-11-2013
Publisher: Wiley
Date: 17-05-2020
DOI: 10.1002/SIM.8568
Publisher: Elsevier BV
Date: 08-2018
Publisher: Wiley
Date: 09-10-2015
DOI: 10.1111/BIOM.12249
Abstract: Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated s ling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1-28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model ersity for thorough exploration of the model space. This algorithm was applied to a validating ex le to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission ex les of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.
Publisher: The Royal Society
Date: 05-2020
Abstract: Understanding functional response within a predator–prey dynamic is a cornerstone for many quantitative ecological studies. Over the past 60 years, the methodology for modelling functional response has gradually transitioned from the classic mechanistic models to more statistically oriented models. To obtain inferences on these statistical models, a substantial number of experiments need to be conducted. The obvious disadvantages of collecting this volume of data include cost, time and the sacrificing of animals. Therefore, optimally designed experiments are useful as they may reduce the total number of experimental runs required to attain the same statistical results. In this paper, we develop the first sequential experimental design method for predator–prey functional response experiments. To make inferences on the parameters in each of the statistical models we consider, we use sequential Monte Carlo, which is computationally efficient and facilitates convenient estimation of important utility functions. It provides coverage of experimental goals including parameter estimation, model discrimination as well as a combination of these. The results of our simulation study illustrate that for predator–prey functional response experiments sequential design outperforms static design for our experimental goals. R code for implementing the methodology is available via aydenmoffat/sequential_design_for_predator_prey_experiments .
Publisher: Public Library of Science (PLoS)
Date: 13-04-2016
Publisher: Wiley
Date: 19-11-2015
DOI: 10.1111/BIOM.12449
Abstract: In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel exact-approximate algorithm, which we refer to as alive SMC2. The advantages of this approach over competing methods are that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo, and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series, and the cumulative number of prion disease cases in mule deer.
Publisher: Public Library of Science (PLoS)
Date: 12-07-2018
Publisher: IEEE
Date: 09-2016
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-01-2018
Abstract: We describe a statistically informed calibration of in silico populations to explore variability in complex systems.
Publisher: Elsevier BV
Date: 10-2021
DOI: 10.1016/J.PLACENTA.2021.09.001
Abstract: Research into the role of ultrasound elastography to assess compromised placental tissue is ongoing. There is particular interest in evaluating its potential in the investigation of changes associated with uteroplacental dysfunction. To date, there is limited data on how different maternal and fetal considerations, such as advancing gestational age, amniotic fluid Index (AFI) and maternal body mass index (BMI) may influence shear wave velocity (SWV) measurements. This study aimed to evaluate longitudinal changes in SWV throughout gestation and model these changes with other developing fetal and maternal physiological and biological characteristics. The study utilised 238 singleton pregnancies and collected longitudinal data at repeated intervals in the 3rd trimester representing 629 in idual data points. Linear mixed model regression analysis was used to identify significant predictors for SWV. From a total of ten variables selected for modelling, only gestational age, AFI, BMI, and s le depth were found to be significant predictors of placental SWV, and gestational age and AFI were found to have only a minimal impact on SWV. Sophisticated statistical modelling demonstrates that many of the expected maternal and fetal changes in the 3rd trimester have no or minimal impact on placental SWV. Understanding which factors influence placental SWV is essential to ascertain the technique's utility in managing pregnancies complicated by placental dysfunction in the future.
Publisher: Public Library of Science (PLoS)
Date: 18-05-2020
Start Date: 2016
End Date: 2018
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2020
End Date: 12-2024
Amount: $390,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2016
End Date: 02-2019
Amount: $382,274.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2022
End Date: 06-2026
Amount: $1,026,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2029
Amount: $35,000,000.00
Funder: Australian Research Council
View Funded Activity