tetano
Editor, Senior Moderator
Int J Epidemiol
. 2025 Apr 12;54(3):dyaf066.
doi: 10.1093/ije/dyaf066. Forecasting COVID-19, influenza, and RSV hospitalizations over winter 2023-4 in England
Jonathon Mellor[SUP] 1 [/SUP], Maria L Tang[SUP] 1 [/SUP], Owen Jones[SUP] 1 [/SUP], Thomas Ward[SUP] 1 [/SUP], Steven Riley[SUP] 1 2 [/SUP], Sarah R Deeny[SUP] 1 [/SUP]
Affiliations
Background: Seasonal 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. Hospitalization 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.
Methods: We developed forecasting models to predict hospital admissions and bed occupancy 2 weeks ahead for COVID-19, influenza, and respiratory syncytial virus (RSV) in England over winter 2023-4. 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 effective reconciliation and trend interpretation.
Results: Admission forecasts, particularly RSV and influenza, showed high efficacy 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.
Conclusion: 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.
Keywords: COVID-19; forecasts; influenza; respiratory syncytial virus; seasonal respiratory viruses; statistical modeling; winter pressures.
. 2025 Apr 12;54(3):dyaf066.
doi: 10.1093/ije/dyaf066. Forecasting COVID-19, influenza, and RSV hospitalizations over winter 2023-4 in England
Jonathon Mellor[SUP] 1 [/SUP], Maria L Tang[SUP] 1 [/SUP], Owen Jones[SUP] 1 [/SUP], Thomas Ward[SUP] 1 [/SUP], Steven Riley[SUP] 1 2 [/SUP], Sarah R Deeny[SUP] 1 [/SUP]
Affiliations
- PMID: 40468743
- DOI: 10.1093/ije/dyaf066
Background: Seasonal 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. Hospitalization 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.
Methods: We developed forecasting models to predict hospital admissions and bed occupancy 2 weeks ahead for COVID-19, influenza, and respiratory syncytial virus (RSV) in England over winter 2023-4. 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 effective reconciliation and trend interpretation.
Results: Admission forecasts, particularly RSV and influenza, showed high efficacy 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.
Conclusion: 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.
Keywords: COVID-19; forecasts; influenza; respiratory syncytial virus; seasonal respiratory viruses; statistical modeling; winter pressures.