arXiv：cs.AI· Gaurob Saha, MinJae Woo, Lior Rennert·· 4 小时前
超越单一病原体预测：用流感、COVID-19 和 RSV 预测共享呼吸道住院负担
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV
AI 导读
研究提出一个 1 至 4 周住院入院量预测框架，比较两种总负担策略：加总各病原体独立预测与直接预测合并入院目标。回顾性评估中，合并负担模型在第 1 至 4 周的 PA 为 0.85、0.78、0.74、0.76，MAE 为 80.63、200.09、183.97、156.13；前瞻性预测中该模型在四个时间跨度上 PA 更高、MAE 更低，第 3、4 周差距最大。
正文
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.
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.16074 [cs.AI] |
| (or arXiv:2610.16074v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16074 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: MinJae Woo [view email]
[v1]
Mon, 12 Oct 2026 02:28:45 UTC (361 KB)
来源：arXiv：cs.AI · arxiv.org