Jul 2026· Kontribusia (Research Dissemination for Community Development)· 0 citations
TL;DR
Findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations, than LSTM.
Abstract
Dengue Fever (DF) remains one of the most significant vector-borne diseases in Indonesia, including Sukabumi City, where the number of reported cases exhibits strong seasonal fluctuations influenced by climatic and demographic factors. Accurate forecasting is therefore essential to support early prevention and public health decision-making. This study compares the predictive performance of Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) for forecasting monthly dengue cases in Sukabumi City. The forecasting models incorporate rainfall, temperature, humidity, and population density as predictor variables. The study follows the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset comprises 96 monthly observations collected between 2018 and 2025 and is divided into training and testing sets using an 80:20 ratio. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the SARIMAX model outperforms LSTM, achieving an MAE of 19.50 and a MAPE of 19.54%, whereas LSTM records an MAE of 20.96 and a MAPE of 21.13%. These findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations. The proposed comparison provides practical insights for selecting appropriate forecasting models to support evidence-based dengue prevention and public health planning in Sukabumi City.
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.
Amiben Mehta, Kajal Patel· ITEGAM- Journal of Engineeri...· 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
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
The feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan, Puerto Rico and Iquitos, Peru is examined, demonstrating that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases.
Pratik S. Machchar, Purvi N. Ramanuj, R. Patel et al.· International Journal of Inf...· 0 citations
While LSTM networks demonstrate promising performance in modeling nonlinear temporal dynamics, the ARIMA model provided the most accurate predictions for the studied dengue dataset, suggesting that while deep learning models are capable of capturing complex temporal patterns, their advantage over well-tuned statistical models is not guaranteed for this dataset.
Tahmid Nowsher, Shaiful Islam Arafat, Md. Kamrujjaman· Discover 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