An accurate hierarchical model to forecast diverse seasonal infectious diseases

Since 2021, the seasonal tripledemic composed of COVID-19, influenza, and respiratory syncytial virus (RSV) has threatened healthcare capacity globally. Short-term forecasts can provide public health officials and healthcare leaders time to effectively respond to epidemics, but many forecast approaches are bespoke to specific diseases or localities. We present a hierarchical forecast model that flexibly accounts for spatial and seasonal transmission dynamics and test its performance on hospital admissions in the United States over two years. The model outcompetes a baseline forecast model by 42%, 44%, and 41% for COVID-19, influenza, and RSV respectively, and it was the top individual forecast model in the 2023-2024 CDC FluSight forecast challenge. We use it to quantify the single-peaked timing and shape for influenza and RSV epidemics and the biannual seasonality of COVID-19. Additionally, we estimate regional disease burden differences across the country with higher burden in the South and lower burden in the West and Northeast. Given its flexible nature and robust performance, our model provides a straightforward way to expand forecasting to additional regions and for other seasonal diseases such as Dengue virus or malaria.