GIS-Based Spatial Temporal Analysis And Priority Scoring For Malaria Control In Nabire Regency
DOI:
https://doi.org/10.47709/brilliance.v6i2.8775Keywords:
Geographic Information System, malaria control, malaria surveillance, priority scoring, spatial-temporal analysisAbstract
Malaria remains a critical public health challenge in Nabire Regency, Central Papua, where escalating incidence rates necessitate targeted control strategies. Currently, routine surveillance data are predominantly presented in tabular formats, inherently limiting their utility for spatially informed decision-making. Addressing this gap, this study analyzes the spatial-temporal dynamics of malaria transmission and develops a novel Malaria Spatial Priority Index (MSPI) a GIS-based multi-criteria priority scoring model to identify high-risk intervention zones. A quantitative descriptive-analytical framework was applied to surveillance data spanning 2018- 2025, integrating epidemiological indicators Annual Parasite Incidence (API), Annual Blood Examination Rate (ABER), and Slide Positivity Rate (SPR) with localized village and Puskesmas spatial parameters. The results reveal a severe intensification of the malaria burden in 2025, characterized by a 76.85% surge in positive cases (from 3,274 to 5,790) alongside synchronized increases in API and SPR. Spatial risk stratification using the MSPI identified Samabusa and Legari as very high-priority service areas, while Kalibobo, Wami Jaya, and Sanoba emerged as the primary transmission hotspots at the village level. Crucially, the spatial distribution demonstrated that administrative boundaries do not uniformly dictate disease risk. This study concludes that synthesizing routine epidemiological metrics with GIS-based spatial prioritization successfully transforms descriptive tabular data into operational spatial intelligence. This novel MSPI framework provides local health authorities with a robust, evidence-based decision-support tool to optimize resource allocation and dismantle localized transmission chains in highly endemic regions.
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