Nowcasting outbreaks leveraging genomic and epidemiological data. This project aims to inform outbreak response planning by developing new models of infectious disease outbreaks. The project expects to generate new knowledge on the processes driving ongoing outbreaks including those of the novel coronavirus (COVID-19) and African swine fever by integrating the latest advances in Bayesian outbreak inference alongside unique simulation approaches. Expected outcomes should include a shift in how mo ....Nowcasting outbreaks leveraging genomic and epidemiological data. This project aims to inform outbreak response planning by developing new models of infectious disease outbreaks. The project expects to generate new knowledge on the processes driving ongoing outbreaks including those of the novel coronavirus (COVID-19) and African swine fever by integrating the latest advances in Bayesian outbreak inference alongside unique simulation approaches. Expected outcomes should include a shift in how models are developed and used to inform the response to outbreaks as they unfold. This should enable more rapid outbreak containment in Australia and overseas, leading to reduced impacts on public and animal health, and associated industries.Read moreRead less
Integrating niches, interactions and dispersal in species distribution models. This proposal aims to develop a framework for statistical modelling that integrates across spatial scales and disentangles the processes of environmental tolerance, biotic interactions and dispersal. Understanding the processes that drive species distributions and ecological communities is central to ecology and environmental management. This knowledge can be used to anticipate the impacts of environmental change on e ....Integrating niches, interactions and dispersal in species distribution models. This proposal aims to develop a framework for statistical modelling that integrates across spatial scales and disentangles the processes of environmental tolerance, biotic interactions and dispersal. Understanding the processes that drive species distributions and ecological communities is central to ecology and environmental management. This knowledge can be used to anticipate the impacts of environmental change on ecosystems, and the likely benefits of interventions. Current statistical models limit the data that can be used and the ecological questions that can be answered. This project expects to improve our ability to predict species distributions under changed environments given interacting species and dispersal across the landscape.Read moreRead less
High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or ....High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or time to loan default. Innovative computational methods will be developed for fitting these models. Compared to traditional prediction method, this approach allows greater flexibility while being superior in terms of statistical accuracy and bias. Extensive analyses of healthcare data from diverse fields will be undertaken.Read moreRead less
Demographic and evolutionary inferences from large, whole-genome datasets. A new data structure for genome-wide datasets has allowed great improvements in the efficiency of genomic data storage and in population genomics simulations, which are crucial to developing and testing mathematical models of population history and species evolution. We will take these advances in new directions, using efficient data structures to dramatically improve inferences about: the demographic histories of popul .... Demographic and evolutionary inferences from large, whole-genome datasets. A new data structure for genome-wide datasets has allowed great improvements in the efficiency of genomic data storage and in population genomics simulations, which are crucial to developing and testing mathematical models of population history and species evolution. We will take these advances in new directions, using efficient data structures to dramatically improve inferences about: the demographic histories of populations, rates of genome change, and phylogenetic networks, and we will develop the first inference methods for the multispecies coalescent with recombination. Outcomes will include advances in understanding the evolutionary histories of humans and other species, including pathogens of importance for global health.Read moreRead less
Increasing the efficiency and interpretability of stepped wedge trials. Stepped wedge cluster randomised trials are increasingly being used to test interventions, across many disciplines. This project aims to develop highly efficient trial designs and new methods for the estimation of causally interpretable effects when adherence to interventions is not perfect. This project expects to generate new design types that reduce resources required to test interventions, and methods to understand how t ....Increasing the efficiency and interpretability of stepped wedge trials. Stepped wedge cluster randomised trials are increasingly being used to test interventions, across many disciplines. This project aims to develop highly efficient trial designs and new methods for the estimation of causally interpretable effects when adherence to interventions is not perfect. This project expects to generate new design types that reduce resources required to test interventions, and methods to understand how these interventions work. Expected outcomes include tools to help researchers develop cheaper and more appealing trials, tools to estimate causal effects, the methodology underpinning these tools, and new collaborations. This should provide significant benefits by allowing more interventions to be tested and understood.Read moreRead less
Guiding principles and guardrails for genetic association studies. This project aims to investigate deep connections between genetic structure (population genetic processes, linkage disequilibrium and population structure) and the ability to statistically detect genetic variants responsible for variation in traits. The project expects to generate new knowledge in the areas of statistics, mathematics and biology through an innovative, multidisciplinary approach that synthesises and extends founda ....Guiding principles and guardrails for genetic association studies. This project aims to investigate deep connections between genetic structure (population genetic processes, linkage disequilibrium and population structure) and the ability to statistically detect genetic variants responsible for variation in traits. The project expects to generate new knowledge in the areas of statistics, mathematics and biology through an innovative, multidisciplinary approach that synthesises and extends foundational disciplinary results. Expected outcomes of this project include principles and methodology that underpin future genetic association studies by supplying a framework for interpreting results. This should provide significant benefits by reducing false conclusions and their associated costs.Read moreRead less