https://youtu.be/5-nVT5sWJI4

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. Second, we introduce a very simple model, SEIRb, that incorporates these features, and offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. We argue that key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

Navid Ghaffarzadegan is an Associate Professor in the Department of Industrial and Systems Engineering at Virginia Tech. His research interests include systems thinking and system dynamics modeling of complex social systems with applications in health policy. Navid’s research has been supported by various organizations such as NIH, DOD, and NSF. His research includes modeling the spread of infectious diseases; his COVID-19 related papers appear in various journals such as Lancet Planetary Health, BioScience, and PLOS Computational Biology, and have received media coverage by Washington Post, New York Times, BBC, and Le Monde. Navid has a PhD in Public Policy, and prior to joining Virginia Tech, he was a postdoctoral researcher at MIT, Engineering Systems Division.

Fun Fact: Navid enjoys watching soccer and is a fan of Persepolis F.C. and Team Melli. He ran his first half-marathon at the age of 44.