Accuracy of US CDC COVID-19 Forecasting Models

Accurate predictive modeling of pandemics is essential for optimally distributing resources and setting policy. Dozens of case predictions models have been proposed but their accuracy over time and by model type remains unclear. In this study, we analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that more than one-third of models fail to outperform a simple static case baseline and two-thirds fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models, and error in modeling has increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US-CDC which risks giving them an official imprimatur and further raising concerns if utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this work to serve as the starting point towards the development of more accurate models.