2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
TL;DR
The experimental 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.
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.
Findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations, than LSTM.
George Elmar, Asriyanik, Winda Apriandari· Kontribusia (Research Dissem...· 0 citations
A high-resolution forecasting framework that integrates daily dengue case data with meteorological drivers to improve predictions in Bangladesh and provides an efficient, reproducible, and scalable approach that can strengthen dengue preparedness in public health systems with limited resources is developed.
Mahadee Al Mobin, Arju Manara Begum· PLOS Global Public Health· 0 citations
Accurate forecasting of influenza-like illness (ILI) incidence is essential for public health surveillance and early epidemic intervention. Traditional statistical models effectively capture linear temporal trends but often fail to model the complex nonlinear dynamics of disease transmission. To address this limitation, this study proposes a hybrid forecasting framework that integrates Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), temporal attention mechanisms, and Transformer architecture. Weekly ILI surveillance data collected from Xinjiang and Shandong provinces, China, between 2023 and 2025 were used for model development and validation. Meteorological variables, including weekly average temperature and relative humidity, were incorporated as exogenous features. The proposed framework decomposes the forecasting task into linear and nonlinear components, where ARIMA captures long-term linear trends and an attention-enhanced LSTM–Transformer branch models nonlinear temporal dependencies. Experimental results demonstrated that the proposed model achieved superior predictive performance on the Xinjiang Production and Construction Corps dataset, with an MAE of 29.371, RMSE of 41.334, MAPE of 10.744%, and R2 of 0.579. Diebold–Mariano tests confirmed statistically significant improvements over standalone ARIMA, LSTM, and ARIMA– LSTM models (P < 0.05). Furthermore, cross-regional validation on the Yantai High-Tech Zone dataset showed comparable forecasting accuracy, indicating strong generalization capability under different climatic conditions. These findings suggest that the proposed hybrid framework effectively captures both linear and nonlinear transmission patterns of ILI and provides a practical tool for early warning and public health resource allocation in grassroots disease surveillance systems.
Jing-Long Chen, Li-Min Xu, Q. Yuan et al.· 2026 8th International Confe...· 0 citations
Vector-borne diseases like dengue, chikungunya, and malaria continue to be a significant public health concern in India, especially in climatically sensitive areas where seasonal effects have a strong impact on disease transmission. Existing national surveillance systems are largely reactive, detecting outbreaks only after case counts rise substantially. This work proposes the CLARION-RF model, a probabilistic framework that is climate-lag aware and capable of early outbreak-risk prediction at the district level. The model combines time-series data from the weekly disease surveillance system with corresponding meteorological variables and climate lag features. The Random Forest-based probabilistic model is trained by balancing the samples to address class imbalance. Instead of deterministic predictions, the model estimates outbreak probabilities and stratifies them into three risk levels: low, medium, and high. Time-aware training (2011–2023) and testing in 2024 demonstrate stable predictive performance, with ROC-AUC and PR-AUC of 0.667 and 0.579, respectively, for the best model.
Manikandaprabhu Perumalsamy, Gururaghavendra, Yashwanth Kumar M et al.· International Conference Com...· 0 citations
Dengue is an arboviral disease of high public health relevance, characterized by pronounced temporal variability, nonlinearity, and recurrent outbreaks, which pose challenges to epidemiological surveillance and decision-making. This study evaluated the performance of machine learning methods for short-term forecasting of the weekly dengue morbidity rate in the 27 Brazilian capital cities, comprising the 26 state capitals and Brasília, Federal District, with horizons up to 4 weeks. Epidemiological, climatic, and socioeconomic data were compiled for these capital cities and used to compare a Gated Recurrent Unit (GRU) neural network, formulated as a Multi-Input Multi-Output (MIMO) model, and a Gradient Boosting model (CatBoost), implemented using a Direct forecasting strategy with horizon-specific models. Validation was conducted using a walk-forward approach, with evaluation based on absolute error metrics and the coefficient of determination. The results indicated that the GRU architecture presented recurring limitations, including underfitting, temporal lag, and low capacity to anticipate epidemic peaks. In contrast, the CatBoost model demonstrated greater robustness and better adaptation to the variability of epidemiological time series, showing superior performance in most of the analyzed capitals. The findings reinforce that greater architectural complexity does not necessarily imply better operational performance and highlight the potential of ensemble-based methods for short-term epidemiological surveillance applications. These findings contribute to dengue forecasting by showing that, under a common validation framework, ensemble-based strategies may provide greater operational robustness than recurrent MIMO architectures for short-term prediction in heterogeneous epidemiological settings.
D. C. da Cunha e Silva, L. M. Nery, Nícholas de Paula Nicomedes et al.· International journal of bio...· 0 citations
Dengue remains a major public health problem in endemic regions, including Bangladesh. Forecasting algorithms relying on climatic variables may not capture epidemiological information such as dengue serotype patterns. This study proposes a horizon-dependent dengue forecasting framework and applies it to Bangladesh. This pipeline included temporal, meteorological, demographic, and epidemiological covariates in a district-panel pipeline and compares classical, machine-learning, and deep-learning algorithms using aggregate, horizon-wise, regime-wise, outbreak-detection, and uncertainty metrics. Coefficient-based interpretation, SHAP, permutation importance, and nonlinear causal-dependence analysis were used to examine predictor impacts across horizons. SARIMAX was used as the baseline model. TFT produced quantile forecasts but showed poor interval calibration during outbreak periods. Results show that no single model performed best across all forecasting horizons: SARIMAX ranked highest for one-step outbreak alerting (precision = 0.886, recall = 0.824, F1 score = 0.854, and ROC-AUC = 0.950), whereas Prophet performed best for pooled magnitude forecasting at horizons 2–6. MLR showed competitive performance in regime-wise evaluation. These findings show that dengue forecasting algorithms should be selected according to the intended decision objective and evaluated using task-relevant protocols.
Md Muqtadir Fuad, Maha Milki, R. Aziz· PLoS ONE· 0 citations
