Info Theory Forum<br><br>Title: <b>Modeling the Heterogeneity in COVID-19’s Reproductive Number and Its Impact on Predictive Scenarios</b><br>Speaker: Claire Donnat, Professor, University of Chicago<br>Date: April 5<br>Time: 12:00pm<br>Event link: <a href="https://stanford.zoom.us/meeting/register/tJEpcOyopzwjGdFFJD1G5LooJcdMID... id="ow490" __is_owner="true">https://stanford.zoom.us/meeting/register/tJEpcOyopzwjGdFFJD1G5LooJcdMID... correct evaluation of the reproductive number R for COVID-19 — which characterizes the average number of secondary cases generated by each typical primary case— is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modelled as a universal constant for the virus across outbreak clusters and individuals— effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, demographics, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified and lead to inaccurate predictions and/or risk evaluation. From the statistical modeling perspective, the magnitude of the impact of this averaging remains an open question: how can this intrinsic variability be percolated into epidemic models, and how can its impact on uncertainty quantification and predictive scenarios be better quantified? In this talk, we discuss a Bayesian perspective on this question, creating a bridge between the agent-based and compartmental approaches commonly used in the literature. After deriving a Bayesian model that captures at scale the heterogeneity of a population and environmental conditions, we simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results. We base our discussion on both synthetic experiments — thereby quantifying of the reliability and the magnitude of the effects — and real COVID-19 data.<p></p><p><br></p><h3>Bio</h3><br>Claire Donnat is an Assistant Professor in the Statistics Department at the University of Chicago. After completing her undergraduate and graduate studies at Ecole Polytechnique (France), she pursued her PhD in Statistics at Stanford University under the supervision of Professor Susan Holmes, and graduated in Spring 2020. Her research interests consist in devising statistical methods for inference on graphs and heterogeneous datasets, and in particular, with applications to biomedical data.