Covid-19 Pandemic is a defining global health event in the 21st century. Forecasting the evolution of the pandemic is a key problem for anyone trying to plan ahead. Since March 2020, IHME has been generating Covid-19 scenarios, first for US states and then for all Admin-1 locations around the world. These scenarios have been intensively used; results are uploaded weekly to an interactive website:
In this talk, we describe two core mathematical models underlying the IHME scenarios. The first model, dubbed CurveFit, used strong assumptions to get useful predictions using extremely limited data, and was used during March and April of 2020. The second model, a data-driven SEIIR model, was put in play in June 2020, and provides a flexible way to incorporate relationships with key drivers such as mobility, mask use, and pneumonia seasonality. We describe the mathematics underlying both models, and discuss the interplay between stability, scalability, and complexity in mathematical modeling.