A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network / 生物医学与环境科学(英文)
Biomedical and Environmental Sciences
;
(12): 494-503, 2022.
Article
in English
| WPRIM
| ID: wpr-939587
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Time Factors
/
China
/
Incidence
/
Disease Outbreaks
/
Reproducibility of Results
/
Cities
/
Neural Networks, Computer
/
Spatio-Temporal Analysis
/
Forecasting
/
Data Visualization
Type of study:
Incidence study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Biomedical and Environmental Sciences
Year:
2022
Type:
Article
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