Kirsty Bolton (University of Nottingham) – From pandemic to endemic: learning from SARS-CoV-2 and pH1N12009 to improve modelling of seasonal influenza dynamics

Despite decades of application of epidemiological models to respiratory viruses such as influenza and coronavirus, there is no standard model for capturing the life history of disease or immunological response, nor a standard approach to inference. I introduce two deterministic epidemic models applied to SARS-CoV-2 (UK) and pH1N1 2009 (UK and Australia). First, I introduce a discrete-time, deterministic model structured by age of infection, and explore how this model structure influences the performance and uncertainty quantification of both maximum likelihood and Bayesian inference methods in the context of SARS-CoV-2. I then present a two-subtype SIRS-like model and examine the conclusions that can be drawn from sparse immunological, microbiological, and serological data regarding the short- and medium-term impact of pH1N1 2009 vaccination. Finally, I review some remaining questions in seasonal influenza modelling, and discuss future work to calibrate mechanistic models that integrate climate variability, host immune dynamics, and behavioural responses.