Scalable Forecasting Geoanalytics for Epidemic Dengue Risk in East Java (Journal of Epidemiology and Public Health, 2026)
Background: Current methods for managing Dengue Hemorrhagic Fever (DHF) in East Java often separate temporal and spatial analyses, hindering proactive public health interventions. To address this limitation, this study develops an integrated spatio-temporal framework that combines multi-model forecasting and geospatial analysis to create localized dengue projections and risk maps.Subjects and Method: An ecological spatio-temporal design was conducted across 38 districts/ cities in East Java using secondary data from 2013–2023. The dependent variable was annual dengue incidence, while independent variables comprised natural factors (e.g., rainfall, humidity) and social factors (e.g., population density, poverty). Data were analyzed using Geographically Weighted Regression (GWR) for spatial modeling. Time series forecasting for the 2024–2028 period was conducted using Trigonometric seasonality, Box-Cox transformation (TBATS), Autoregressive Integrated Moving Average (ARIMA), Prophet, and Neural NETwork AutoRegression (NNETAR) models, evaluated by Root Mean Square Error (RMSE).Results: Rainfall demonstrated the strongest natural association (R²=0.49; p=0.022), followed by humidity (R²=0.26; p
Keywords: dengue; forecasting; spatial analysis; population density