Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model
ABSTRACT Background and Aims A life‐threatening vector‐borne disease, dengue fever (DF), poses significant global public health and economic threats, including Bangladesh. Determining dengue risk factors is crucial for early warning systems to forecast disease epidemics and develop efficient control...
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Wiley
2025-05-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70726 |
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| author | Md. Siddikur Rahman Miftahuzzannat Amrin Md. Abu Bokkor Shiddik |
| author_facet | Md. Siddikur Rahman Miftahuzzannat Amrin Md. Abu Bokkor Shiddik |
| author_sort | Md. Siddikur Rahman |
| collection | DOAJ |
| description | ABSTRACT Background and Aims A life‐threatening vector‐borne disease, dengue fever (DF), poses significant global public health and economic threats, including Bangladesh. Determining dengue risk factors is crucial for early warning systems to forecast disease epidemics and develop efficient control strategies. To address this, we propose an interpretable tree‐based machine learning (ML) model for dengue early warning systems and outbreak prediction in Bangladesh based on climatic, sociodemographic, and landscape factors. Methods A framework for forecasting DF risk was developed by using high‐performance ML algorithms, namely Random Forests, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), based on sociodemographic, climate, landscape, and dengue surveillance epidemiological data (January 2000 to December 2021). The optimal tree‐based ML model with strong interpretability was created by comparing various ML models using the hyperparameter optimization technique. The feature importance ranking and the most significant dengue driver were found using the SHapley Additive explanation (SHAP) value. Results Our study findings detected a nonlinear effect of climatic parameters on dengue at different thresholds such as mean (27°C), minimum (22°C), maximum temperatures (32°C), and relative humidity (82%). The optimal minimum and maximum temperatures, humidity, rainfall, and wind speed for dengue risk are 25−28°C, 32−34°C, 75%−85%, 10 mm, and 12 m/s, respectively. The LightGBM model accurately forecasts DF and agricultural land, population density, and minimum temperature significantly affecting the dengue outbreak in Bangladesh. Conclusion Our proposed ML model functions as an early warning system, improving comprehension of the factors that precipitate dengue outbreaks and providing a framework for sophisticated analytical techniques in public health. |
| format | Article |
| id | doaj-art-a80c6351b9e34709bd376fc9dfd341eb |
| institution | DOAJ |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-a80c6351b9e34709bd376fc9dfd341eb2025-08-20T02:55:05ZengWileyHealth Science Reports2398-88352025-05-0185n/an/a10.1002/hsr2.70726Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning ModelMd. Siddikur Rahman0Miftahuzzannat Amrin1Md. Abu Bokkor Shiddik2Department of Statistics Begum Rokeya University Rangpur BangladeshDepartment of Statistics Begum Rokeya University Rangpur BangladeshDepartment of Statistics Begum Rokeya University Rangpur BangladeshABSTRACT Background and Aims A life‐threatening vector‐borne disease, dengue fever (DF), poses significant global public health and economic threats, including Bangladesh. Determining dengue risk factors is crucial for early warning systems to forecast disease epidemics and develop efficient control strategies. To address this, we propose an interpretable tree‐based machine learning (ML) model for dengue early warning systems and outbreak prediction in Bangladesh based on climatic, sociodemographic, and landscape factors. Methods A framework for forecasting DF risk was developed by using high‐performance ML algorithms, namely Random Forests, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), based on sociodemographic, climate, landscape, and dengue surveillance epidemiological data (January 2000 to December 2021). The optimal tree‐based ML model with strong interpretability was created by comparing various ML models using the hyperparameter optimization technique. The feature importance ranking and the most significant dengue driver were found using the SHapley Additive explanation (SHAP) value. Results Our study findings detected a nonlinear effect of climatic parameters on dengue at different thresholds such as mean (27°C), minimum (22°C), maximum temperatures (32°C), and relative humidity (82%). The optimal minimum and maximum temperatures, humidity, rainfall, and wind speed for dengue risk are 25−28°C, 32−34°C, 75%−85%, 10 mm, and 12 m/s, respectively. The LightGBM model accurately forecasts DF and agricultural land, population density, and minimum temperature significantly affecting the dengue outbreak in Bangladesh. Conclusion Our proposed ML model functions as an early warning system, improving comprehension of the factors that precipitate dengue outbreaks and providing a framework for sophisticated analytical techniques in public health.https://doi.org/10.1002/hsr2.70726artificial intelligencedengueearly warninginfectious diseaseprediction |
| spellingShingle | Md. Siddikur Rahman Miftahuzzannat Amrin Md. Abu Bokkor Shiddik Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model Health Science Reports artificial intelligence dengue early warning infectious disease prediction |
| title | Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model |
| title_full | Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model |
| title_fullStr | Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model |
| title_full_unstemmed | Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model |
| title_short | Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model |
| title_sort | dengue early warning system and outbreak prediction tool in bangladesh using interpretable tree based machine learning model |
| topic | artificial intelligence dengue early warning infectious disease prediction |
| url | https://doi.org/10.1002/hsr2.70726 |
| work_keys_str_mv | AT mdsiddikurrahman dengueearlywarningsystemandoutbreakpredictiontoolinbangladeshusinginterpretabletreebasedmachinelearningmodel AT miftahuzzannatamrin dengueearlywarningsystemandoutbreakpredictiontoolinbangladeshusinginterpretabletreebasedmachinelearningmodel AT mdabubokkorshiddik dengueearlywarningsystemandoutbreakpredictiontoolinbangladeshusinginterpretabletreebasedmachinelearningmodel |