STG-Mixer: A Multi-Region, Multi-Step Forecasting Network for Hand, Foot, and Mouth Disease with Multi-Timescale Feature Extraction and Spatial Association Learning

Accurate forecasting of hand, foot, and mouth disease (HFMD) incidence is crucial for effective prevention and control. However, existing multi-region HFMD forecasting models remain limited in jointly capturing multi-timescale temporal patterns and interregional spatial associations, which affects multi-step forecasting performance. In this study, we propose Spatiotemporal Graph Mixer (STG-Mixer), a multi-region, multi-step forecasting network for HFMD based on multi-timescale feature extraction and spatial association learning. STG-Mixer constructs a graph structure based on interregional adjacency relationships and adopts a dual-branch spatiotemporal encoder consisting of an Adaptive Fusion Spatial Branch (AF-SB) and a Multi-Scale Energy-Gated Temporal Branch (MEG-TB). Specifically, AF-SB employs a dual-layer Temporal Graph Convolutional Network (T-GCN), TimeKAN block, and Orthogonal Residual Gated Fusion mechanism to learn spatial representations, while MEG-TB extracts and adaptively weights temporal features across multiple scales using dilated temporal convolutions and an energy-gated mechanism. The learned spatial and temporal representations are fused and fed into an autoregressive decoder for multi-step forecasting. Experiments on HFMD surveillance and meteorological data from 47 prefectures in Japan demonstrate that STG-Mixer outperforms baseline models in one- to four-week-ahead forecasting. Additional experiments on an independent HFMD dataset from southern China further evaluate the forecasting capability of the proposed model under a different temporal resolution.