  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.
