Modeling in the Time of a Pandemic
When COVID-19 started to spread, modelers around the world rallied to provide forecasts and longer-term scenarios to guide the public health response. The pace was fast and the methods varied. However, the types of questions faced by developers were the same as for any model, and I will start by reviewing the various decisions that guide such endeavors. I will then present our work on EpiCovDA, a minimalist model developed by former graduate student Hannah Biegel, which combines simple nonlinear dynamics with data assimilation to provide short-term forecasts. EpiCovDA predictions contribute to the CDC ensemble model and the last part of the talk will describe the open-science forecasting community that was fostered by the CDC mathematical modeling team and its flu forecasting centers of excellence.