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Abstract
Introduction Dengue is a mosquito-borne disease caused by the dengue virus, primarily transmitted by Aedes aegypti and Aedes albopictus. Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction and risk mapping.
Objectives This study aims to develop a spatio-temporal Bayesian model for predicting dengue incidence, integrating climatic, sociodemographic, and environmental factors to improve outbreak forecasting.
Methods An ecological study was conducted in the Special Region of Yogyakarta, Indonesia (January 2017– December 2022) using monthly panel data from 78 sub-districts. Secondary data sources included dengue surveillance (Health Office), meteorological data (NASA POWER), sociodemographic data (BPS-Statistics Indonesia), and land use data (Sentinel-2, ESRI). Predictors included rainfall, temperature, humidity, wind speed, atmospheric pressure, population density, and land use patterns. Data analysis was performed using R-INLA, with model performance assessed using Deviance Information Criterion (DIC), Watanabe-Akaike Information Criterion (WAIC), marginal log-likelihood, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
Results The INLA-based Bayesian model effectively captured spatial and temporal dengue dynamics. Key predictors included rainfall lag 1 and 2 (mean = 0.001), temperature (mean = 0.151, CI: 0.090–0.210), humidity (mean = 0.056, CI: 0.040–0.073), built area (mean = 0.001), and water area (mean = 0.008, CI: 0.005–0.011). Spatial clustering (BYM model, precision = 2163.53) indicated that dengue cases were concentrated in specific areas. The RW2 model (precision = 49.11) confirmed seasonal trends, highlighting climate’s role in disease transmission. Model evaluation metrics (DIC = 15017.88, WAIC = 15294.54, log-likelihood = -7845.857) demonstrated good predictive performance. Furthermore, the model’s accuracy was assessed using MAE and RMSE values, where MAE = 1.77 indicates an average prediction error of 1–2 cases, while RMSE = 2.97 suggests the presence of occasional larger discrepancies. The RMSE’s higher value relative to MAE highlights instances where prediction errors were more significant, as RMSE is more sensitive to large deviations.
This output contributes to the following Sustainable Development Goals (SDGs); SDG 3: Good Health and Well-being.
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