SPATIAL ANALYSIS OF MALARIA RISK AND SOCIOECONOMIC DETERMINANTS IN PAPUA PROVINCE, INDONESIA USING A BAYESIAN MODELING APPROACH

Abstract

Malaria remains a major public health problem in tropical regions, including Indonesia’s Papua Province, which bears one of the highest national burdens. This study analyzes the spatial distribution of malaria and identifies key socioeconomic, geographic, and demographic predictors in Papua in 2022 using Bayesian spatial modeling. Secondary data from 28 districts/cities were analyzed with a Bayesian spatial model using the BYM2 formulation and Integrated Nested Laplace Approximation (INLA). Significant spatial disparities were identified, with high-risk clusters in the northeast and central regions. The number of polyclinics showed a significant negative association with malaria incidence, indicating a protective effect. Conversely, regional income and average years of schooling were positively associated with malaria, possibly reflecting increased mobility, detection bias, and development-related ecological change. These findings highlight strong spatial heterogeneity and multifactorial drivers of malaria transmission. Bayesian spatial modeling provides important insights for policy planning and supports the need for geographically targeted, multisectoral interventions, strengthening primary healthcare infrastructure, and context-sensitive development strategies to reduce malaria burden.