Date: Friday, September 20 at 14.00
Speaker: Prof. Laurent Hébert-Dufresne, University of Vermont.
Title: Contagion models that challenge the linear relationship between exposure and transmission
Abstract:
Models of contagions attempt to reduce complex global dynamics (like a pandemic) to a set of simple local interactions (pairwise transmissions). To do so, it is common to assume that the force of infection on a susceptible individual is linearly proportional to its exposure to the contagion. Here, I will tell two stories of models that implicitly challenge this assumption by acknowledging that the context of exposure matters. First, we will look at heterogeneous patterns of transmission occurring when infection risk varies by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic. Second, we will stop assuming that epidemics happen in a vacuum and consider interactions through co-infections, like COVID-19 and influenza. Spoiler alert: Both stories will show an induced superlinear force of infection during the emergence of a new outbreak when we are unaware of the full context of the exposure and transmission events. These results potentially have important consequences in a wide range of modelling scenarios as they illustrate that superlinear dynamics can emerge from unobserved covariates even when the dynamics is otherwise linear.