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
Discovery Early Career Researcher Award - Grant ID: DE170100785
Funder
Australian Research Council
Funding Amount
$345,491.00
Summary
Mathematical and statistical modelling of antimalarial drug action. This project aims to develop a mathematical model to optimise global antimalarial treatment policy. Malaria-causing parasites are resistant to the most potent antimalarial drug available. If left unaddressed, a catastrophic rise in global malaria incidence and mortality could occur. Changes to global antimalarial treatment policy increasingly rely on mathematical models, but they do not encompass recent breakthroughs in antimala ....Mathematical and statistical modelling of antimalarial drug action. This project aims to develop a mathematical model to optimise global antimalarial treatment policy. Malaria-causing parasites are resistant to the most potent antimalarial drug available. If left unaddressed, a catastrophic rise in global malaria incidence and mortality could occur. Changes to global antimalarial treatment policy increasingly rely on mathematical models, but they do not encompass recent breakthroughs in antimalarial drug action and the immune response. This project’s model is expected to improve antimalarial drug dosing regimens and control the spread of antimalarial drug resistance.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
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.
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