Modeling, post COVID-19

Abstract

Much of the public first learned about epidemiological modeling during the early months of the coronavirus disease 2019 (COVID-19) pandemic. The first models resulted in more confusion than clarity. Even though coronavirus cases were rising exponentially in the United States and Europe, some models predicted a rapid peak followed by a rapid decline, whereas other models predicted cycles of infection continuing over several years. Much has been learned since those early months. In retrospect, it is clear that modeling requires both reliable data and an accurate understanding of how disease spreads, and that the field of epidemiological modeling requires a diversity of approaches. Support for this field must increase and be coordinated, with a designation of responsibilities among funding agencies.