Using mathematical modelling to inform the design of infectious disease surveillance studies and indicators
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 is the bread and butter of epidemic modelling and analytics. Given this expertise in connecting data with their utility for informing decisions, modellers and modelling are/is well placed not just to be passive “consumers” of data but also play an active role in data collection processes. In this talk, I will discuss two research projects aimed at improving infectious disease surveillance. The first aims to improve 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.
In-person event. If you wish to join online, please register here.
