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.

翻译：手足口病（HFMD）暴发与显著发病率和重症病例死亡率密切相关。因此，准确预测手足口病儿科患者的每日入院人数，对于协助医院应对潜在疫情暴发和减少院内传播至关重要。为满足这一迫切需求，我们提出了一种基于U型结构的Transformer新型模型，该模型采用分块策略和联合预测策略，充分利用与手足口病密切相关的疱疹性咽峡炎数据信息。该模型还通过引入重构损失作为辅助损失，整合了表示学习。实验结果表明，我们的U型分块时间序列Transformer（UPTST）模型在医院级别的手足口病长短程预测精度上均优于现有方法。此外，探索性扩展实验表明，该模型的能力不仅限于传染病预测，在多个领域具有更广泛的应用潜力。