Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq

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 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.

Fig 1. The overall structure of Seq2Seq-HMF.

It comprises an encoder and a decoder. The encoder performs multi-scale hybrid perception on input time series data from multiple regions to extract features, which the decoder then processes to generate predictive time series data for those regions.

Fig 2. Structure of STPE Cell.

It is contains TGCN (A) and BiLSTM (B). Both modules receive the output of the previous STPE Cell in addition to the data features.

Fig 3. structure of FECAM.

It is consists of operations such as DCT, stack, MLP, and multiplication.

Fig 4. Bar Chart of Performance Indicators for Model Comparison Tests in multi-region data set.

Fig 5. 3D Spatiotemporal Distribution of HFMD Cases.

For the predicted performance of each model when L=1, the test set starts at week 46 in 2021 as the Y-axis and ends at week 51 in 2023, for a total of 110 weeks.

Fig 6. Line chart of predicted values(part A).

Comparison of observed and predicted values for each representative administrative area.

Fig 7. Line chart of predicted values(part B).

Comparison of observed and predicted values for each representative administrative area.

Fig 8. Error Ratio:

A geospatial map comparing the observed values with the predictions from the Seq2Seq-HMF during the HFMD epidemic peaks.

Fig 9. Radar chart illustrating the performance metrics of the Effect of Seq2Seq-HMF component.