Bayesian inverse ensemble forecasting for COVID‐19

Abstract Variations in strains of COVID‐19 have a significant impact on the rate of surges and on the accuracy of forecasts of the epidemic dynamics. The primary goal for this article is to quantify the effects of varying strains of COVID‐19 on ensemble forecasts of individual “surges.” By modelling the disease dynamics with an SIR model, we solve the inverse ensemble forecasting problem to compute a probability distribution on the transmission parameters from observed population data at an early specified time during a surge. Using the computed distribution, we then forecast future surge dynamics. The solution of the inverse ensemble forecasting problem is computed by applying a Bayesian approach to the disintegration of measures. We verify the method and explore properties of the inverse solution using synthetic data. We also make ensemble forecasts using real data obtained from a specific population of towns.