Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data

Abstract 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.