Discovery Early Career Researcher Award - Grant ID: DE240100635
Funder
Australian Research Council
Funding Amount
$448,801.00
Summary
Understanding the development of lifestyle behaviours in early childhood. This project adopts novel statistical modelling and machine learning approaches to understand the development of lifestyle behaviours in early childhood. Despite the pivotal role of lifestyle behaviours in influencing health and quality of life, little research exists on lifestyle behaviours in early childhood. This project will establish a comprehensive understanding of lifestyle behaviours in early childhood by identifyi ....Understanding the development of lifestyle behaviours in early childhood. This project adopts novel statistical modelling and machine learning approaches to understand the development of lifestyle behaviours in early childhood. Despite the pivotal role of lifestyle behaviours in influencing health and quality of life, little research exists on lifestyle behaviours in early childhood. This project will establish a comprehensive understanding of lifestyle behaviours in early childhood by identifying key developmental time points, mechanisms of behavioural change, and children at risk of developing poor lifestyle behaviours. The project will inform strategies and policies to optimise lifestyle behaviours from the start of life and showcase the capabilities of novel methods in advancing behavioural epidemiology.Read moreRead less
Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inf ....Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.
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Optimising disease surveillance to support decision-making. COVID-19 has demonstrated the critical role of epidemic data and analytics in guiding government response to pandemic threats, reducing disease and saving lives. The demand for epidemic analytics for response to threats of national significance will only grow. The goals of this project are to 1) determine the combination(s) of surveillance methods that provide the most useful data for epidemic analysis and 2) translate these findings in ....Optimising disease surveillance to support decision-making. COVID-19 has demonstrated the critical role of epidemic data and analytics in guiding government response to pandemic threats, reducing disease and saving lives. The demand for epidemic analytics for response to threats of national significance will only grow. The goals of this project are to 1) determine the combination(s) of surveillance methods that provide the most useful data for epidemic analysis and 2) translate these findings into the blueprint for a next-generation infectious disease surveillance system for Australia. We will use a simulation-evaluation approach, coupling methods from infectious disease modelling with those from information theory optimal design. Outcomes will enable more tailored and effective pandemic response.Read moreRead less