Spatiotemporal filtering modeling of hand, foot, and mouth disease

Authors

• Xi Chen, Fudan University
• Jianbo Ba, Second Military Medical University
• Yuanhua Liu, Fudan University
• Jiaqi Huang, Fudan University
• Ke Li, Fudan University
• Yun Yin, Fudan University
• Jin Shi, Fudan University
• Jiayao Xu, Fudan University
• Rui Yuan, Fudan University
• Michael P. Ward, University of Sydney
• Wei Tu, Georgia Southern UniversityFollow
• Lili Yu, Georgia Southern UniversityFollow
• Quanyi Wang, Chinese Center for Disease Control and Prevention
• Xiaoli Wang, Chinese Center for Disease Control and Prevention
• Zhaorui Chang, Chinese Center for Disease Control and Prevention
• Zhijie Zhang, Fudan University

Document Type

Article

Publication Date

4-16-2025

Publication Title

Epidemiology and Infection

DOI

10.1017/s0950268824001080

Abstract

Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R ², and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran's I < 0.2, p > 0.05). The Bayesian spatiotemporal model's Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R ² (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Recommended Citation

Chen, Xi, Jianbo Ba, Yuanhua Liu, Jiaqi Huang, Ke Li, Yun Yin, Jin Shi, Jiayao Xu, Rui Yuan, Michael P. Ward, Wei Tu, Lili Yu, Quanyi Wang, Xiaoli Wang, Zhaorui Chang, Zhijie Zhang. 2025. "Spatiotemporal filtering modeling of hand, foot, and mouth disease." Epidemiology and Infection, 153. doi: 10.1017/s0950268824001080
https://digitalcommons.georgiasouthern.edu/geo-facpubs/218

Copyright

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