Adaptive COVID-19 Trajectory Forecasting Using MAB-Inspired Ensemble Weighting
Forecasting epidemic trajectories is important for public health decision-making, but no single model is consistently reliable across epidemic phases and forecasting settings. We evaluate Multi-Armed Bandit (MAB)-inspired adaptive weighting strategies for combining epidemic forecasting models when component-model performance changes over time. Using U.S. COVID-19 incidence data from three epidemic waves, we compare UCB, EXP3, and epsilon-greedy weighting rules under fixed short-window and growing calibration windows, with both deterministic and stochastic ensemble variants. The model pool includes SIR, SEIR, GLM, Gompertz, Richards, ARIMA, random walk with drift, simple exponential smoothing, Holt's linear trend method, and exponential growth. Adaptive ensembles are compared with individual models and with naive, unweighted, and inverse-WIS weighted ensemble benchmarks. Forecast performance is assessed using RMSE, weighted interval score (WIS), 95% prediction-interval coverage, and mean 95% prediction-interval width. Across waves, calibration windows, and forecast horizons, EXP3Stoch, EXP3Det, and EPSStoch achieved the lowest mean forecast WIS. The main gains were in probabilistic forecast quality, especially WIS and interval coverage, rather than uniformly lower point forecast error. Simple benchmarks, including the unweighted and inverse-WIS ensembles, remained competitive in several settings. These results suggest that MAB-inspired adaptive weighting is a useful complementary tool for epidemic forecasting, especially when model skill is time-varying and forecast uncertainty is substantial.