Forecasting COVID-19, Influenza and RSV hospitalisations over winter 2023/24 in England

Abstract

AbstractSeasonal respiratory viruses cause substantial pressure on healthcare systems, particularly over winter. System managers can mitigate the impact on patient care when they anticipate hospital admissions due to these viruses. Hospitalisation forecasts were used widely during the SARS-CoV-2 pandemic. Now, resurgent seasonal respiratory pathogens add complexity to system planning. We describe how a suite of forecasts for respiratory pathogens, embedded in national and regional decision-making structures, were used to mitigate the impact on hospital systems and patient care.We developed forecasting models predicting hospital admissions and bed occupancy two weeks ahead for COVID-19, influenza, and RSV in England over winter 2023/24. Bed occupancy forecasts were informed by the ensemble admissions models. Forecasts were delivered in real-time at multiple scales. The use of sample-based forecasting allowed for effective reconciliation and trend interpretation.Admission forecasts, particularly RSV and influenza, showed high skill at regional levels. Bed occupancy forecasts had well-calibrated coverage, owing to informative admissions forecasts and slower moving trends. National admissions forecasts had mean absolute percentage errors of 27.3%, 30.9% and 15.7% for COVID-19, influenza, and RSV respectively, with corresponding 90% coverages of 0.439, 0.807 and 0.779.These real-time winter infectious disease forecasts produced by the UK Health Security Agency for healthcare system managers played an informative role in mitigating seasonal pressures. The models were delivered regularly and shared widely across the system to key users. This was achieved by producing reliable, fast, and epidemiologically informed ensembles of models. Though, a higher diversity of model approaches could have improved forecast accuracy.