A summary of symbols and descriptions.

Accurate prediction of Hand, Foot, and Mouth Disease (HFMD) is crucial for effective epidemic prevention and control. Existing prediction models often overlook the cross-regional transmission dynamics of HFMD, limiting their applicability to single regions. Furthermore, their ability to perceive spatio-temporal features holistically remains limited, hindering the precise modeling of epidemic trends. To address these limitations, a novel HFMD prediction model named Seq2Seq-HMF is proposed, which is based on the Sequence-to-Sequence(Seq2Seq) framework. This model leverages hybrid perception of multi-scale features. First, the model utilizes graph structure modeling for multi-regional epidemic-related features. Secondly, a novel Spatio-Temporal Parallel Encoding(STPE) Cell is designed; multiple STPE Cells constitute an encoder capable of hybrid perception across multi-scale spatio-temporal features. Within this encoder, graph-based feature representation and iterative convolution operations enable the capture of cumulative influence of neighboring regions across temporal and spatial dimensions, facilitating efficient extraction of spatio-temporal dependencies between multiple regions. Finally, the decoder incorporates a frequency-enhanced channel attention mechanism(FECAM) to improve the model’s comprehension of temporal correlations and periodic features, further refining prediction accuracy and multi-step forecasting capabilities. Experimental results, utilizing multi-regional data from Japan to predict HFMD cases one to four weeks ahead, demonstrate that our proposed Seq2Seq-HMF model outperforms baseline models. Additionally, the model performs well on single-region data from a city in southern China, confirming its strong generalization ability.

手足口病（Hand, Foot, and Mouth Disease, HFMD）的精准预测对于有效开展疫情防控至关重要。现有预测模型往往忽视手足口病的跨区域传播动态，限制了其在单一区域的适用性；此外，其全面感知时空特征的能力仍存在不足，阻碍了对疫情趋势的精准建模。为解决这些局限，本文提出一种新型手足口病预测模型Seq2Seq-HMF，该模型基于序列到序列（Sequence-to-Sequence, Seq2Seq）框架，并利用多尺度特征混合感知机制。首先，该模型采用图结构对多区域疫情相关特征进行建模；其次，设计了一种新型时空并行编码（Spatio-Temporal Parallel Encoding, STPE）单元，多个STPE单元构成编码器，可实现多尺度时空特征的混合感知。在该编码器中，基于图的特征表示与迭代卷积操作能够捕捉邻域区域在时空维度上的累积影响，从而高效提取多区域间的时空依赖关系。最后，解码器引入频域增强通道注意力机制（frequency-enhanced channel attention mechanism, FECAM），以提升模型对时间相关性与周期特征的理解能力，进一步优化预测精度与多步预测性能。实验采用日本多区域数据开展手足口病1至4周后的病例数预测，结果表明，所提出的Seq2Seq-HMF模型性能优于各类基准模型；此外，该模型在我国南方某城市的单区域数据上同样表现优异，证实了其出色的泛化能力。