Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.
Background: Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control. Objective: This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, i...
| Publicado en: | JMIR Infodemiology Vol. 5; pp. 1 - 16 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
JMIR Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191613548&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191613548 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25641891 N1MT jtl: JMIR Infodemiology issn: 25641891 maglogo: N pubinfo: dt: 2025 vid: 5 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 191613548 191613548 191613548 10.2196/75434 191613548 ppf: 1 ppct: 15 formats: tig: atl: Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data. aug: au: Chen, Yixiong Zhang, Xue Zhang, Sheng Han, Wenjie Wang, Ziqi Chen, Jian Liu, Jinfeng Feng, Jingru Shi, Jiayi Long, Haoyu Cao, Zicheng Zhang, Jie Li, Yuan Du, Xiangjun Zhang, Xindong Ren, Meng affil: Bao'an Center for Disease Control and Prevention, 3 Haixiu Road, Bao'an District, Shenzhen, China sug: subj: Hand, Foot, and Mouth Disease Epidemiology Hand, Foot, and Mouth Disease Prevention and Control Risk Assessment Environment Internet Forecasting Disease Surveillance Human Funding Source China Male Female Retrospective Design Incidence Correlational Studies Spearman's Rank Correlation Coefficient Pearson's Correlation Coefficient Factor Analysis Machine Learning Descriptive Statistics Data Analysis Software Seasons Temperature Weather Nitrogen Oxides Male Female ab: Background: Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control. Objective: This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, including historical incidence information, environmental parameters, and web-based search behavior data, to improve predictive performance. Methods: We integrated multisource data (HFMD cases, meteorology, air pollution, Baidu Index, and public health measures) from Bao'an District of Shenzhen city in Southern China (2014‐2023). Correlation analysis was used to assess the associations between HFMD incidence and systematic factors. The impacts of environmental factors were analyzed using the Distributed Lag Nonlinear Model. Seasonal Autoregressive Integrated Moving Average model and advanced machine learning methods were used to predict HFMD 1-4 weeks ahead. Risk levels for the 1- to 4-week-ahead forecasts were determined by comparing the predicted weekly incidence against predefined thresholds. Results: From 2014 to 2023, Bao'an District reported a total of 118,826 cases of HFMD. Environmental and search behavior factors (excluding sulfur dioxide) were significantly associated with HFMD incidence in nonlinear patterns. For 1-week-ahead prediction, Seasonal Autoregressive Integrated Moving Average using case data alone performed best (R ²=0.95, r =0.98, mean absolute error=53.34, and root-mean-square error=99.31). For 2- to 4-week-ahead forecasting, machine learning models incorporating web-based and environmental data showed superior performance (R ²=0.83, 0.75, and 0.64; r =0.92, 0.87, and 0.80; mean absolute error=87.84, 112.41, and 132.47; and root-mean-square error=185.08, 229.13, and 276.81). The predicted HFMD risk levels matched the observed levels with accuracies of 96%, 87%, 88%, and 83%, respectively. Conclusions: The epidemic dynamics of HFMD are influenced by multiple factors in a nonlinear manner. Integrating multisource data, particularly web-based search behavior, significantly enhances the accuracy of short- and midterm forecasts and risk assessment. This approach offers practical insights for developing digital surveillance and early warning systems in public health. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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