Climate-Aware LSTM Framework for Dengue Forecasting: Evidence from Multi-City Indian and SJ/IQ Datasets

Abstract

For effective public health preparedness, it is essential to accurately predict the number of dengue cases, especially in tropical and subtropical areas where climate change has a significant impact on how the disease spreads. Standard statistical time-series models, such as the Autoregressive Integrated Moving Average (ARIMA), are commonly utilized but have certain limitations because they assume linear interactions and cannot easily capture climate-disease relationships that are delayed or nonlinear. We examine the feasibility of a Long Short-Term Memory (LSTM)–based forecasting approach that incorporates meteorological factors with lagged dengue incidence to enhance the modelling of temporal dependencies. Monthly dengue surveillance data from Gujarat, India, spanning 2010 to 2019, were integrated with meteorological variables like temperature, relative humidity, and precipitation. Lagged climatic and dengue features were created to take into account short-term effects and seasonal persistence. The dataset was separated into three parts: training, validation, and testing. We trained a single-layer LSTM network with 16 hidden units with dropout regularization. The Adam optimizer was used to train the model, and the mean absolute error (MAE), root-mean-square error (RMSE), and the coefficient of determination (R²) were used to evaluate the model's performance. To test how well the proposed model works in general, it was also tested on dengue datasets from San Juan (Puerto Rico) and Iquitos (Peru). The experimental results show that LSTM model outperformed both the ARIMA and Random Forest baselines on both datasets. The LSTM got a test MAE of about 32 cases and a R² value of 0.82 on the Gujarat test set. This shows that it works well in the real-world dataset. The datasets from Kaggle showed similar gains. These results show that climate-aware LSTM models are good at predicting dengue cases and can help build strong early warning systems in areas where cases are dependent on climate change.