A single autoregressive integrated moving average with exogenous variable outperforms ensembles of autoregressive models for forecasting influenza hospitalizations in the contiguous United States

Abstract Infectious disease forecasting is important to public health decision-making, particularly for mitigating the burden of seasonal influenza. We propose and evaluate comprehensive autoregressive modelling approaches (autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with exogenous variables (ARIMAX)) for short-term forecasts of influenza-related hospitalizations across the contiguous United States (US). We used data from the National Healthcare