Accounting for the interaction of immunology, epidemiology, evolution, and biophysical processes in Covid-19 mathematical models

Generally speaking, immunology, epidemiology, evolution, and biophysical processes related to pathogen transmission interact in the context of disease control. Particularly for emerging pathogens such as SARS-CoV-2, minimal data availability combined with modeling / dynamical uncertainties can make correctly accounting for these factors in population-level disease models challenging. In this talk, we will present a minimal conceptual modeling framework for Covid-19 to study these interactions. Using this framework, we will focus on three primary topics: the impact of the strength and duration of natural and vaccinal immune responses on Covid-19 case burden and timing, epidemiological and evolutionary impacts of vaccine dose spacing, and epidemiological and evolutionary consequences of how vaccines are allocated between countries. We will then discuss ongoing work to better parametrize our models as data become increasingly available. In particular, we will introduce experimental methods for studying the biophysical processes underlying viral in-host dynamics as well as the impact of seasonality on disease transmission rates. Speaker(s): Prof. Caroline Wagner, Montreal, Quebec, Canada, Virtual: https://events.vtools.ieee.org/m/315368