Evaluating the Comparative Accuracy of COVID-19 Mortality Forecasts: An Analysis of the First-Wave Mortality Forecasts in the United States

The sudden onset of the COVID-19 pandemic posed significant challenges for forecasting professionals worldwide. This article examines the early forecasts of COVID-19 transmission, using the context of the United States, one of the early epicenters of the crisis. The article compares the relative accuracy of selected models from two forecasters who informed government policy in the first three months of the pandemic, the Institute of Health Metrics and Evaluation (IHME) and Columbia University. Furthermore, we examine whether the forecasts improved as more data became available in the subsequent months of the pandemic, using the forecasts from Los Alamos National Laboratory and the University of Texas, Austin. The analysis focuses on mortality estimates and compares forecasts using epidemiological and curve-fitting models during the first wave of the pandemic from March 2020 to October 2020. As health agencies worldwide struggled with uncertainty in models and projections of COVID-19 caseload and mortality, this article provides important insights that can be useful for crafting policy responses to the ongoing pandemic and future outbreaks.