Using mathematical modelling to design infectious disease surveillance studies and indicators
Exploring methods to develop more informative surveillance studies using the same (or less) resources, with examples from influenza surveillance.
Mathematical models of infectious diseases, combined with statistical analyses, enable the synthesis and interpretation of epidemiological data. This requires consideration of the underlying mechanisms, factors, and randomness that contribute to the generation of observed data – the “observation process”. Developing methods for the robust and transparent management of imperfections in surveillance data (e.g., bias and missingness) is the bread and butter of epidemic modelling and analytics.
Given this expertise in connecting data with their utility for informing decisions, modellers are well placed not just to be passive “consumers” of data, but to also play an active role in data collection processes. Can our discipline help to develop more informative surveillance studies for the same (or less) surveillance resources? This is a research focus of mine and my colleagues.
In this talk, I will discuss two pieces of model-based research. The first aims to improve the understanding and use of commonly reported surveillance indicators for influenza. The second aims to optimise the design of surveillance studies for measuring infection burden to maximise data utility while minimising surveillance resources.
Speaker
Freya Shearer
I lead the Infectious Disease Dynamics Unit within the Centre for Epidemiology and Biostatistics at the Melbourne School of Population and Global Health, The University of Melbourne. My research focuses on the development and application of methods in mathematical modelling and data-analytics to support infectious disease prevention and control.
My work spans two broad pathogen types: those with constrained geographic distributions due to their complex transmission cycles involving multiple animal reservoir and vector species (e.g., zoonotic malaria and Japanese encephalitis); and respiratory viruses of epidemic and pandemic potential (e.g., SARS-CoV-2 and influenza).
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