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Field of Research : Biostatistics
Field of Research : Evolutionary Biology
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  • Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE180101520

    Funder
    Australian Research Council
    Funding Amount
    $365,058.00
    Summary
    Diet, variance and individual variability in life-history. This project aims to provide biologists with novel statistical tools that will shift analytical paradigms. In many species, dietary restrictions increase average lifespan, and affect average rates of growth and reproduction, also known as ‘life history’. The use of recently developed tools has shown that individual variability in life history also appears to increase under dietary restrictions. This project will explore the effects of di .... Diet, variance and individual variability in life-history. This project aims to provide biologists with novel statistical tools that will shift analytical paradigms. In many species, dietary restrictions increase average lifespan, and affect average rates of growth and reproduction, also known as ‘life history’. The use of recently developed tools has shown that individual variability in life history also appears to increase under dietary restrictions. This project will explore the effects of diet composition on variability in life-history traits, and the factors driving this variation. This is expected to improve the prediction of the effects of changing nutritional environments.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210103239

    Funder
    Australian Research Council
    Funding Amount
    $256,889.00
    Summary
    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.
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    Funded Activity

    Discovery Projects - Grant ID: DP140101915

    Funder
    Australian Research Council
    Funding Amount
    $210,000.00
    Summary
    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.
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