Mathematical and statistical methods for modelling invivo pathogen dynamics. This project aims to develop mathematical models and Bayesian statistical methods that better capture how natural defence responses and drugs help control infection. When viruses (e.g. influenza) or parasites (e.g. malaria) invade the human body, they begin to replicate. To date, only simple mathematical models have been developed to capture these processes, and these models are not well formulated. This project will im ....Mathematical and statistical methods for modelling invivo pathogen dynamics. This project aims to develop mathematical models and Bayesian statistical methods that better capture how natural defence responses and drugs help control infection. When viruses (e.g. influenza) or parasites (e.g. malaria) invade the human body, they begin to replicate. To date, only simple mathematical models have been developed to capture these processes, and these models are not well formulated. This project will improve biomathematics and biostatistical algorithms for pathogen dynamics and is ultimately expected to benefit public health and clinical research aimed at alleviating the effect of infectious diseases on human health.Read moreRead less
Mathematical models of diseases with complex transmission routes. This project aims to model diseases that spread via a mixture of routes including food, water, the environment, and direct spread between individuals. Key diseases include: avian influenza, which causes massive disruption to the poultry industry; gastroenteritis, which costs Australia $1,250 million each year; and leptospirosis, which causes one million severe illnesses each year globally. This project will develop mathematical a ....Mathematical models of diseases with complex transmission routes. This project aims to model diseases that spread via a mixture of routes including food, water, the environment, and direct spread between individuals. Key diseases include: avian influenza, which causes massive disruption to the poultry industry; gastroenteritis, which costs Australia $1,250 million each year; and leptospirosis, which causes one million severe illnesses each year globally. This project will develop mathematical and statistical tools to better estimate risk, analyse outbreak data, and provide guidance for disease control. This research will improve policy and enhance our ability to respond to disease outbreaks.Read moreRead less
New statistical approaches for analysing foodwebs and species distributions. Identifying how species are distributed over the landscape, interact and self-organise into foodwebs are central goals in Ecology. This project aims to provide innovative new Bayesian modelling tools to improve our understanding of species distributions and their foodweb networks. It is expected to develop a general framework for extending species distribution models to deal with multiple species, incorporating both the ....New statistical approaches for analysing foodwebs and species distributions. Identifying how species are distributed over the landscape, interact and self-organise into foodwebs are central goals in Ecology. This project aims to provide innovative new Bayesian modelling tools to improve our understanding of species distributions and their foodweb networks. It is expected to develop a general framework for extending species distribution models to deal with multiple species, incorporating both their interactions as well as errors in detection. The project also hopes to develop a robust Bayesian methodology for partitioning complex foodweb networks into ecologically relevant compartments as there are currently no reliable methods to achieve this. Both projects are of relevance to conservation policy and management of threatened species.Read moreRead less
Binary regression with additive predictors: new statistical theory with healthcare applications. This project will develop new statistical analysis techniques for predicting whether someone is at risk of adverse health outcomes. The project will then apply the new techniques to a large database on heart attacks, leading to new insights into how patient characteristics and treatments affect the chance of dying from a heart attack.
Fitting non-Gaussian diffusion models to evolutionary data: towards a generalized framework for phylogenetic comparative analyses. This project aims to develop cutting-edge statistical methods for evolutionary biology in order to answer big questions using data derived from multiple species. Such methods are needed because of the variety of multi-species data that are becoming available, which cannot be dealt with correctly using current methods. The research is significant because it will provi ....Fitting non-Gaussian diffusion models to evolutionary data: towards a generalized framework for phylogenetic comparative analyses. This project aims to develop cutting-edge statistical methods for evolutionary biology in order to answer big questions using data derived from multiple species. Such methods are needed because of the variety of multi-species data that are becoming available, which cannot be dealt with correctly using current methods. The research is significant because it will provide a new way of fitting a wide class of statistical models to evolutionary data, in a very general setting. Further, this project will unite current methodology in a broader framework so that the proposed new methods are a generalisation of currently accepted theory. The outcomes will include a freely-available software package that implements the methods in a user-friendly form.Read moreRead less
Statistical methods for the analysis of critical care data, with application to the Australian and New Zealand Intensive Care Database. The recent inquiry into Queensland's Bundaberg Base Hospital highlights the need to monitor hospital performance. This project develops new statistical methods to account for uncertainty in the assessment of provider performance and its outcomes will provide government with institutional comparisons for policy and planning.
Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This proj ....Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This project expects to increase understanding of biological mechanisms, the efficiency of genetic association analyses and the accuracy of genomic prediction, including the effects of interventions. The project will adapt human models to a wider range of organisms, in particular bacteria.Read moreRead less
Evolutionary analyses of short-read sequences from pooled samples. This project aims to provide biologists with a means of making sound, statistical inferences about evolution by using next-generation data from mixed samples. When biologists make statements about history, they use evolutionary trees, frequently reconstructed from the genetic data of many individuals. Next-generation sequencing provides large amounts of genetic data at low cost, but biologists have difficulty using these data for ....Evolutionary analyses of short-read sequences from pooled samples. This project aims to provide biologists with a means of making sound, statistical inferences about evolution by using next-generation data from mixed samples. When biologists make statements about history, they use evolutionary trees, frequently reconstructed from the genetic data of many individuals. Next-generation sequencing provides large amounts of genetic data at low cost, but biologists have difficulty using these data for evolutionary research, particularly when they sample mixtures of DNA from many individuals. The anticipated value of this project is that it allows evolutionary biologists to capitalise on the benefits of next-generation sequencing, without sacrificing their ability to make reliable inferences about history.Read moreRead less
Novel techniques for statistical and mathematical analyses of sequence data. Algorithms will be developed for analysing and comparing the sequences of DNA letters and amino acids constantly being generated in massive quantities by biological research. The novel approach taken is based on the statistical frequency of occurrence of short words and is designed specifically for situations where current methods fail.
A likelihood-based approach to combined surveys inference. This project focuses on the development of statistical theory for efficient integration of information across multiple complex sample surveys. It will develop theory and methodology that will answer complex questions about relationships between important social, economic and health related variables that are presently measured in separate surveys.