Simulating viral evolution and genetic complexity. This project has direct relevance to understanding the growth of viral infections, and therefore has possible practical applications in disease research and control. Examples of these are emerging diseases in humans such as those caused by HIV-1, SARS coronavirus and Dengue virus, which cause considerable human suffering throughout the world. A major part of current research into these diseases involves attempts to model the evolutionary geneti ....Simulating viral evolution and genetic complexity. This project has direct relevance to understanding the growth of viral infections, and therefore has possible practical applications in disease research and control. Examples of these are emerging diseases in humans such as those caused by HIV-1, SARS coronavirus and Dengue virus, which cause considerable human suffering throughout the world. A major part of current research into these diseases involves attempts to model the evolutionary genetics and dynamics of virus populations in order to understand how to control epidemics, develop vaccines and design drugs. The research program is designed to provide new computational modelling tools for this purpose, which may have wider applications as well.
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New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutriti ....New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sophisticated computer intensive methods.Read moreRead less
Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects ....Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects to develop novel, less restrictive and more realistic nonparametric curve estimation methods in these complex settings. Outcomes include new practical statistical methods and software to benefit experts in diverse fields from nutrition and epidemiology, to environmental science and digital platforms, amongst others.Read moreRead less
Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and function ....Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and functional data. The expected methodology will be able to solve frontier problems, where only sophisticated methods can access information. This is expected to benefit brain studies, economics, infectious disease, nutrition and public health.Read moreRead less
Nonparametric data analysis in statistical science. Changes in technology have enabled new types of data to be collected, often more complex and in much larger quantities than ever before, and altered fundamentally the types of questions that need to be asked of those data. The research program will develop new statistical methods for analysing new types of data, for example functional data and data with many dimensions, and will also introduce greatly improved solutions to problems that involve ....Nonparametric data analysis in statistical science. Changes in technology have enabled new types of data to be collected, often more complex and in much larger quantities than ever before, and altered fundamentally the types of questions that need to be asked of those data. The research program will develop new statistical methods for analysing new types of data, for example functional data and data with many dimensions, and will also introduce greatly improved solutions to problems that involve more conventional data types. These techniques will have critical applications to diverse fields. The program will contribute substantially to capacity building in a strategically important area, statistical science, of great value to Australia but where chronic skills shortages exist.Read moreRead less
Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extr ....Statistical Modelling in the Era of Data Science: Theory and Practice. This project aims to develop innovative statistical methodology that is interpretable, theoretically justified, and scalable to today's growing complex data. With the influx of data being collected in both the public and private sectors, making sense of this data is a fundamental task. Through a rigorous modelling framework, this project intends to facilitate the discovery of knowledge by developing powerful new tools to extract insight from these complex datasets. The outcomes of this project will benefit society by providing techniques to enable research advances and inform decision-making for a broad base of disciplines, including applications to network security, energy forecasting, environmental monitoring, and public health. Read moreRead less
Modeling Healthcare Systems. An efficient healthcare system is essential for the well-being of any society. The aim of the project is to develop major advances in the mathematical modelling of healthcare systems, in order to improve efficiency, and ultimately, patient health. The first expected outcome is the development of mathematical models that constitute a high-level description of patient flow through hospitals and subacute care, so that demands for emergency and elective capacity are met ....Modeling Healthcare Systems. An efficient healthcare system is essential for the well-being of any society. The aim of the project is to develop major advances in the mathematical modelling of healthcare systems, in order to improve efficiency, and ultimately, patient health. The first expected outcome is the development of mathematical models that constitute a high-level description of patient flow through hospitals and subacute care, so that demands for emergency and elective capacity are met given limited resources. The second is the development of a bed allocation algorithm that allocates patients to appropriate wards, so as to optimise the set of performance indicators of the system under appropriate constraints, given the current ward occupancy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101326
Funder
Australian Research Council
Funding Amount
$391,546.00
Summary
Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to ....Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to address them. The expected outcomes include a suite of novel methods designed to evaluate the impact of intervening to modify causal pathways, while also accommodating common complexities of data such as incompleteness. This project should provide major benefits to studies in public health, social sciences and economics.Read moreRead less