Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model

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.