Outbreaks of hand-foot-and-mouth disease(HFMD) have been associated with significant morbidity and, in severe cases, mortality. Accurate forecasting of daily admissions of pediatric HFMD patients is therefore crucial for aiding the hospital in preparing for potential outbreaks and mitigating nosocomial transmissions. To address this pressing need, we propose a novel transformer-based model with a U-net shape, utilizing the patching strategy and the joint prediction strategy that capitalizes on insights from herpangina, a disease closely correlated with HFMD. This model also integrates representation learning by introducing reconstruction loss as an auxiliary loss. The results show that our U-net Patching Time Series Transformer (UPTST) model outperforms existing approaches in both long- and short-arm prediction accuracy of HFMD at hospital-level. Furthermore, the exploratory extension experiments show that the model's capabilities extend beyond prediction of infectious disease, suggesting broader applicability in various domains.

手足口病(HFMD)的爆发会导致重大的发病率，并在严重病例中导致死亡。因此，准确预测儿科HFMD患者每日入院人数对于帮助医院准备潜在爆发并减轻医院内传播非常关键。为了解决这一迫切需求，我们提出了一种基于变压器的新型模型，具有U-net形状，利用了与HFMD密切相关的疱疹咽炎的见解，并在表示学习方面引入重构损失作为辅助损失。结果表明，我们的U-net Patching Time Series Transformer (UPTST) 模型在医院级别的HFMD长短臂预测准确性方面优于现有方法。此外，探索性扩展实验表明，该模型的能力不仅局限于传染病的预测，还可在各个领域具有广泛的适用性。

利用腮腺炎数据提升使用UPTST进行手足口病入院预测的医院级预测