Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models

Abstract

Dengue fever is a significant public health issue in tropical and subtropical areas, and predicting its spread is challenging due to the complex interactions between climate factors, mosquito behavior, and case reporting. This study develops a high-resolution forecasting framework that integrates daily dengue case data with meteorological drivers (including mean, maximum, and minimum temperature; precipitation; specific and relative humidity; surface pressure; mean, minimum, and maximum wind speed; wind speed range; wind direction; and sunshine duration) to improve predictions in Bangladesh. Aggregated case counts were disaggregated using a Stochastic Bayesian Downscaling (SBD) algorithm, followed by systematic feature engineering. A wide range of deep learning models, including Artificial Neural Networks (ANN), recurrent networks (LSTM, GRU, BiLSTM, BiGRU), attention-based models, and hybrid convolution neural network (CNN) based ensembles, were optimized through Bayesian hyperparameter tuning and evaluated under a unified process. Results demonstrated that a simple ANN achieved the highest performance, with an accuracy of 97.05%, RMSE of 145.02, and MAPE of 0.51%, surpassing more complex recurrent and attention-based models. Feature importance analysis revealed that weather variables accounted for 76.2% of predictive accuracy, with lagged climate features contributing 59.4%. Short-term lags of three to seven days proved especially influential, underscoring the importance of near-real-time weather monitoring. The strongest predictors included surface pressure with a 3-day lag, precipitation, and maximum wind speed with 7-day lags, while measures of variability such as rolling standard deviations also contributed. The study highlights three key contributions: the benefit of combining downscaling, lagged climate features, and systematic feature selection; evidence that ANN models can outperform more complex architectures in dengue forecasting; and the identification of short-lag weather factors that can support early warning systems. The framework provides an efficient, reproducible, and scalable approach that can strengthen dengue preparedness in public health systems with limited resources.