Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq
Bingbing Lei, Xuanjun Zhu, Tao Zhou, Yuxi Zhang, Guangyin Jin, Guangyin Jin, Guangyin Jin
TL;DR
This paper introduces a new model for predicting Hand-Foot-Mouth Disease cases across multiple regions by capturing spatio-temporal patterns more effectively.
Contribution
The paper proposes Seq2Seq-HMF, a novel model using hybrid perception of multi-scale spatio-temporal features for multi-region HFMD prediction.
Findings
The Seq2Seq-HMF model outperforms baseline models in predicting HFMD cases in Japan.
The model demonstrates strong generalization ability when applied to single-region data from southern China.
The model captures cumulative influence of neighboring regions and improves multi-step forecasting.
Abstract
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…
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Taxonomy
TopicsViral Infections and Immunology Research · Animal Disease Management and Epidemiology · Plant and Fungal Interactions Research