Advanced mathematical models and methods for a randomly-varying world. This project aims to develop advanced stochastic models and novel techniques, to analytically obtain performance measures and to efficiently simulate the time evolution. This project also plans to apply new models and methods to address important problems in ecology and epidemiology. The outputs of this project will advance knowledge in mathematics as well as in the intended application areas, including ultimately in improved ....Advanced mathematical models and methods for a randomly-varying world. This project aims to develop advanced stochastic models and novel techniques, to analytically obtain performance measures and to efficiently simulate the time evolution. This project also plans to apply new models and methods to address important problems in ecology and epidemiology. The outputs of this project will advance knowledge in mathematics as well as in the intended application areas, including ultimately in improved understanding, modelling, and tracking of the spread of diseases.Read moreRead less
New methods for integrating population structure and stochasticity into models of disease dynamics. Epidemics, such as the 2007 equine 'flu outbreak and 2009 swine 'flu pandemic, highlight the need to make informed decisive responses. This project will develop new methods that incorporate two important aspects of disease dynamics---host structure and chance---into mathematical models, and determine their impact in terms of controlling infections.
Developing mathematical models of infection and transmission to link biology, epidemiology and public health policy. Infectious diseases constitute a significant burden on the health of the population. Understanding how best to control them requires a multi-faceted approach, combining data from biology, medicine and population health with mathematical and computational models of disease transmission. This project will investigate the "flu" and other diseases.
Developing mathematical models and statistical methods to understand the dynamics of infectious diseases: stochasticity, structure and inference. Infectious diseases remain a major contributor to mortality and illness worldwide. The potential for future severe pandemics also continues to present a substantial threat to our health and well-being. Mathematics and statistics are increasingly becoming part of the arsenal used by governments to combat the invasion and spread of infectious diseases. I ....Developing mathematical models and statistical methods to understand the dynamics of infectious diseases: stochasticity, structure and inference. Infectious diseases remain a major contributor to mortality and illness worldwide. The potential for future severe pandemics also continues to present a substantial threat to our health and well-being. Mathematics and statistics are increasingly becoming part of the arsenal used by governments to combat the invasion and spread of infectious diseases. In such work, three themes have emerged as having the potential to revolutionise the modelling of infectious diseases: stochasticity, structure (both age and spatial), and inference. This project will develop state-of-the-art techniques, at the interface of these themes, of critical importance to understanding the dynamics of infectious diseases.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100805
Funder
Australian Research Council
Funding Amount
$382,656.00
Summary
Phylogenetic methods for genome surveillance of microbial pathogens. This project aims to develop phylogenetic approaches to harness the potential evolution of bacterial and virus pathogens data and to improve early detection of infectious outbreaks. Genome surveillance programs consist in routine sequencing of particular organisms to track their evolution over time. Such programs currently exist for important bacterial and virus pathogens. This project expects to develop computational methods t ....Phylogenetic methods for genome surveillance of microbial pathogens. This project aims to develop phylogenetic approaches to harness the potential evolution of bacterial and virus pathogens data and to improve early detection of infectious outbreaks. Genome surveillance programs consist in routine sequencing of particular organisms to track their evolution over time. Such programs currently exist for important bacterial and virus pathogens. This project expects to develop computational methods to improve our understanding of pathogen outbreak emergence and infectious spread using genome data. This project will expand our knowledge base and research capability in the evolution and epidemiology of infectious agents, and aid in the prevention and control strategies of infectious disease benefiting the research priorities of food and health.Read moreRead less
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