Abstract

The COVID-19 pandemic has made some ideas in infectious disease modelling household names: $R_0$, $R_t$ and herd immunity were very much in the public eye for many months. But the pandemic also raised new challenges for infectious disease modelling. Much of ““classical”” infectious disease modelling focused on setting up a model, determining its (usually local asymptotic) behaviour, and discovering how its basic reproduction number depended on its parameters. But in the era of the pandemic, we need models that can help us interpret data in real time, that can cope with heterogeneity, that are suitable for modelling feasible actions at a range of scales, and we need models that can incorporate data describing viral diversity. Some of the challenges call for juxtapositions of mechanistic models and statistical models, describing the distributions of variables we can observe. In this talk, I will describe new modelling and estimation approaches that we have developed in this context. First, I will introduce ““eventR””, which is like a basic reproduction number for a specific event, and outline how it can be used to help compare strategies for preventing transmission. Next, I will describe a mechanistic-statistical model with which we can estimate a key parameter for any infectious disease simulation: the per unit time, per contact transmission rate, as long as we know enough about the transmission setting. I will link these two through an analysis of the cluster size distributions in schools in four Canadian provinces in 2021, and will describe and interpret our findings about the transmission rates. Finally, I will discuss the broader challenges for infectious disease modelling in this pandemic and beyond.