The Disutility of SEIR Model Forecasts During the COVID-19 Pandemic

During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the Susceptible-Infected-Recovered (SIR) compartmental model was the most common. In this paper, we investigate the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We consider the following daily outcomes: hospitalizations, ICU patients and deaths. We evaluate the overall forecasting performance, highlight some stark forecast biases, and consider forecast errors conditional on different pandemic regimes. We find that this model tends to over forecast over the longer horizons and when there is a surge in viral spread. We bolster these findings by linking them to faults with the SIR framework itself.