Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models.

Abstract

Hand, Foot, and Mouth Disease (HFMD) predominantly affects children under the age of five and remains a significant public health concern in the Asia-Pacific region. HFMD outbreaks are closely linked to seasonal changes and meteorological factors, particularly in tropical and subtropical areas. In Thailand, a total of 657,570 HFMD cases were reported between 2011 and 2022 (12 years). This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Our findings showed that the XGBoost model outperformed the other models in predicting unseen data and defining the best model. The best model can be used to detect high-risk outbreak areas and to explore the relationship between meteorological factors and HFMD outbreaks. The results highlighted the seasonal distribution of high-risk HFMD outbreak months across different provinces in Thailand, with average maximum temperature, average rainfall, and average vapor pressure identified as the most influential factors. Furthermore, the best model was used to analyze HFMD outbreaks during the COVID-19 pandemic, showing a notable reduction in high-risk outbreak months and areas, likely due to the control measures implemented during this period. Overall, our model shows great potential as a tool for warnings, providing useful insights to help public health officials reduce the impact of HFMD outbreaks.