Summary
Hospitals face challenges when several respiratory viruses like flu, COVID-19, and RSV spread at the same time, because the total number of patients affects staff and beds all together. The authors compared two ways to predict hospital admissions: adding up separate predictions for each virus versus predicting the total number of patients directly. They found that predicting the total hospital burden in one model worked better, especially for forecasts three to four weeks ahead. This means hospitals can plan resources more efficiently by using combined forecasts while still relying on individual virus data for understanding causes.
What this means in practice
- •For hospital operations teams: Generate better short-term total patient admission forecasts for respiratory illnesses to guide staffing and resource allocation more effectively.
- •For public health planners: Use combined burden forecasts to improve regional healthcare preparedness during respiratory virus seasons involving multiple pathogens.
Abstract
Co-circulating influenza, COVID-19, and respiratory syncytial virus (RSV) create a shared hospital-admission burden, because beds, staff, testing capacity, and respiratory support are allocated to total demand rather than to one pathogen at a time. This makes respiratory forecasting a target-construction problem as well as a modeling problem: disease-specific forecasts preserve attribution, whereas hospitals also need a single planning signal for combined burden. We developed a supervised 1- to 4-week-ahead framework for weekly admissions and compared two total-burden strategies: summing independently generated pathogen-specific forecasts and forecasting the combined admission target directly. Evaluation included retrospective held-out testing and prospective operational forecasts issued before the corresponding admission weeks were observed, with performance summarized using MAE and Percent Agreement (PA; the smaller of model-generated and observed counts divided by the larger). In retrospective total-burden evaluation, the combined-burden model had PA values of 0.85, 0.78, 0.74, and 0.76 and MAE values of 80.63, 200.09, 183.97, and 156.13 from week 1 to week 4 ahead, compared with summed-forecast PA values of 0.85, 0.80, 0.69, and 0.69 and MAE values of 100.14, 200.06, 205.65, and 217.57. Prospectively, the combined-burden model had higher PA and lower MAE at all four horizons, with the largest separations at weeks 3 and 4 ahead. These results support layered respiratory forecasting: pathogen-specific forecasts remain necessary for etiologic interpretation, while combined-burden forecasts better align with hospital preparedness and shared-capacity planning.