Spatial Epidemiology of Infectious Diseases in Indonesia

Indonesia’s vast geography, high population density, and environmental diversity contribute to uneven patterns of infectious disease transmission. This study aims to explore the spatial distribution and environmental influences of seven key infectious diseases—acute respiratory infections (ARI), pneumonia, tuberculosis (TB), hepatitis, diarrhea, malaria, and filariasis—across the country's 34 provinces. We used data from the 2018 National Health Survey and the 2018 Environmental Quality Index (EQI). Spatial statistical techniques—including the Variance Mean Ratio (VMR), Moran’s I, Moran Scatter Plots, and Spatial Autoregressive (SAR) modeling—were applied to detect spatial clustering, spatial autocorrelation, and neighborhood effects. We also examined the role of environmental quality in shaping disease patterns. Most infectious diseases demonstrated significant spatial clustering, with Java Island and its surrounding areas showing the highest concentration. Strong spatial correlations were observed among ARI, pneumonia, TB, hepatitis, diarrhea, and filariasis. In contrast, malaria displayed a distinct, uncorrelated pattern. SAR modeling revealed positive spatial spillover effects, indicating that neighboring provinces influenced disease occurrences in the province under study. Moreover, provinces with higher EQI scores tended to have lower rates of ARI, TB, diarrhea, and other diseases—except for malaria, which followed a different pattern. The findings suggest that infectious disease patterns in Indonesia are not random but geographically clustered and environmentally influenced. Targeted health interventions that consider both spatial dynamics and environmental quality are essential for effective disease control and prevention

Environmental epidemiology · Environmental pollution · Infectious disease (medical specialty · Population · Spatial analysis · Spatial dependence · Spatial distribution · Spatial epidemiology · COVID-19 epidemiological studies · Epidemiology · Spatial and Panel Data Analysis · Zoonotic diseases and public health