Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models
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 T...
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MDPI AG
2025-02-01
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| Series: | Tropical Medicine and Infectious Disease |
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| author | Pakorn Lonlab Suparinthon Anupong Chalita Jainonthee Sudarat Chadsuthi |
| author_facet | Pakorn Lonlab Suparinthon Anupong Chalita Jainonthee Sudarat Chadsuthi |
| author_sort | Pakorn Lonlab |
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| description | 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. |
| format | Article |
| id | doaj-art-01385d64d5b247b8a5fb77e631de6849 |
| institution | OA Journals |
| issn | 2414-6366 |
| language | English |
| publishDate | 2025-02-01 |
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| series | Tropical Medicine and Infectious Disease |
| spelling | doaj-art-01385d64d5b247b8a5fb77e631de68492025-08-20T02:03:27ZengMDPI AGTropical Medicine and Infectious Disease2414-63662025-02-011024810.3390/tropicalmed10020048Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning ModelsPakorn Lonlab0Suparinthon Anupong1Chalita Jainonthee2Sudarat Chadsuthi3Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, ThailandDepartment of Chemistry, Mahidol Wittayanusorn School (MWIT), Salaya, Nakhon Pathom 73170, ThailandResearch Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, ThailandDepartment of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, ThailandHand, 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.https://www.mdpi.com/2414-6366/10/2/48handfootand mouth diseaseoutbreak detectionmeteorological factorsmachine learning |
| spellingShingle | Pakorn Lonlab Suparinthon Anupong Chalita Jainonthee Sudarat Chadsuthi Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models Tropical Medicine and Infectious Disease hand foot and mouth disease outbreak detection meteorological factors machine learning |
| title | Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models |
| title_full | Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models |
| title_fullStr | Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models |
| title_full_unstemmed | Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models |
| title_short | Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models |
| title_sort | seasonal and meteorological drivers of hand foot and mouth disease outbreaks using data driven machine learning models |
| topic | hand foot and mouth disease outbreak detection meteorological factors machine learning |
| url | https://www.mdpi.com/2414-6366/10/2/48 |
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