A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network.

Tian Jiao JI; Qiang Cheng; Yong Zhang; Han Ri Zeng; Jian Xing Wang; Guan Yu Yang; Wen Bo XU; Hong Tu Liu

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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network.

Author: Tian Jiao JI ¹ ; Qiang CHENG ² ; Yong ZHANG ^{3
,

4} ; Han Ri ZENG ⁵ ; Jian Xing WANG ⁶ ; Guan Yu YANG ⁷ ; Wen Bo XU ^{3
,

4} ; Hong Tu LIU ^{3
,

4}
Author Information

1. NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100026, China.
2. Academy of Cyber Science and Engineering, Southeast University, Nanjing 211189, Jiangsu, China.
3. NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100026, China
4. Center for Biosafety Mega Science, Chinese Academy of Sciences, Wuhan 430071, Hubei, China.
5. Guangdong Center for Disease Control and Prevention, Guangzhou 511430, Guangdong, China.
6. Shandong Center for Disease Control and Prevention, Jinan 250014, Shandong, China.
7. LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Southeast University, Nanjing 211189, Jiangsu, China.
Publication Type:Journal Article
Keywords: Disease prediction; Early warning model; HFMD; STGCN
MeSH: China/epidemiology*; Cities/epidemiology*; Data Visualization; Disease Outbreaks/statistics & numerical data*; Forecasting/methods*; Hand, Foot and Mouth Disease/prevention & control*; Humans; Incidence; Neural Networks, Computer; Reproducibility of Results; Spatio-Temporal Analysis; Time Factors
From: Biomedical and Environmental Sciences 2022;35(6):494-503
CountryChina
Language:English
Abstract: Objectives:Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD.
Methods:We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.
Results:As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern.
Conclusions:This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.