Sep 2026· International Journal of Informatics and Communication Technology (IJ-ICT)· 0 citations· 29 references
TL;DR
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.
Abstract
Vector borne disease like dengue continues to pose a significant climate-sensitive public health challenge in tropical regions such as Brazil, Peru, and India. This study examines the feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan (SJ), Puerto Rico and Iquitos (IQ), Peru. Dengue incidence was analyzed alongside meteorological, environmental, and vegetation-based variables to capture key climatic influences. Several machine learning and deep learning approaches were evaluated, including LightGBM. Model performance was assessed using root mean square error (RMSE) and mean absolute error (MAE). The results show that LightGBM achieved the low est RMSE/MAE, indicating strong short-term predictive accuracy and excellent interpretability. Feature importance analysis and principal component analysis (PCA) identified precipitation, dew point temperature, and humidity as the most influential predictors of dengue incidence. The study demonstrates that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases. While this research focuses on dengue, the methodology is adaptable to other vector-bone datasets and diseases, offering a flexible tool for public health authorities to predict and mitigate outbreaks in diverse urban contexts.
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
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
Lassa fever remains a critical and highly perilous public health threat across West Africa, with Ondo State, Nigeria, consistently experiencing severe annual outbreaks. While accurate forecasting is essential for timely medical intervention, conventional predictive models often fail to capture the complex spatiotemporal transmission dynamics and the delayed ecological triggers of the Lassa virus. To address this, this study introduces a comprehensive Health Information Pattern Discovery framework to forecast outbreaks across four high-risk localized hotspots: Akure South, Akure North, Akoko Southwest, and Owo. Advancing beyond standard machine learning benchmarking, this research integrates spatial-temporal graph theoretic modelling to map the relational transmission velocity between regions , coupled with a Genetic Algorithm (GA) to optimize complex lagged meteorological variables, including temperature, relative humidity, and precipitation. The optimized spatiotemporal feature space was utilized to train and evaluate four distinct architectures: Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM) networks. Empirical evaluation demonstrated that the GA-optimized Random Forest ensemble outperformed the other models, achieving a Root Mean Squared Error (RMSE) of 5.627, a Mean Absolute Error (MAE) of 3.747, and an of 0.87. Beyond baseline predictive accuracy, this hybrid graph-theoretic and evolutionary approach successfully isolates the exact environmental triggers preceding an outbreak, providing interpretable, actionable intelligence to strengthen proactive public health planning and targeted interventions in endemic regions.
Unknown authors· Journal of Science, Technolo...· 0 citations
The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area outperform conventional surveillance systems in developing countries.
I. Adewumi, N. Bakare, W. Ajayi et al.· London Journal of Physics· 0 citations
Dengue fever has emerged as a major public health concern in Pakistan, particularly in Balochistan, where climatic variability and limited surveillance infrastructure have contributed to increasing disease burden. This study aimed to investigate the relationship between climatic factors and dengue transmission across multiple districts of Balochistan using a retrospective district-level epidemiological approach. Dengue surveillance data were collected from Public Health Laboratories, District Health Offices (DHOs), dengue surveillance units, and People’s Primary Healthcare Initiative (PPHI) centers, while climatic variables including temperature, rainfall, and humidity were obtained from national and global meteorological sources. The integrated datasets were analyzed using RStudio to assess seasonal trends, spatial distribution, and climate–dengue associations through descriptive statistics, correlation analysis, and negative binomial regression modeling. A total of 26,482 confirmed dengue cases were identified from 125,780 diagnostic tests conducted during 2024. The highest disease burden was observed in Turbat, Panjgur, Jhal Magsi, and Khuzdar districts. Dengue incidence showed a strong seasonal pattern, with cases increasing sharply during the summer and post-monsoon months and peaking in September. Regression analysis demonstrated that increasing temperature was significantly associated with higher dengue incidence, while rainfall exhibited delayed effects through enhanced vector breeding conditions. The combined influence of high temperature and high rainfall produced the greatest transmission intensity. The findings indicate that dengue transmission in Balochistan is highly climate-sensitive and increasingly shifting toward an endemic transmission pattern. Strengthening climate-informed surveillance systems, improving early warning mechanisms, enhancing vector control strategies, and promoting community awareness are essential for reducing the growing dengue burden in the region.
Iqra batool, Abdul Rehman, Ali Nawaz et al.· International Journal of Agr...· 0 citations