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Strengthening geospatial surveillance for malaria control and elimination in Indonesia: a meeting report
Abdillah Farkhan1,2,3, Massaya Sirimatayanant4, Iqbal R F Elyazar5
1Office of the Health Quarantine I Tarakan, Directorate General of Disease Control, Ministry of Health, Tarakan, Indonesia.
Abstract:
As Indonesia progresses toward malaria elimination, geospatial analysis offers opportunities to strengthen surveillance, improve intervention targeting, and support data-informed decision-making. However, despite increasing availability of georeferenced surveillance data, the routine use of geographic information systems (GIS) within malaria programs remains uneven. A three-day consultation and GIS training workshop was conducted in Jakarta, bringing together 25 representatives from National and Sub-National Malaria Programs and other disease control units. The workshop included hands-on R-based spatial analysis, followed by semi-structured focus group discussions exploring current practices, barriers, and potential strategies for strengthening geospatial surveillance. Participants reported growing recognition of the value of geospatial data and described surveillance platforms that routinely capture spatially referenced information; however, these data were often used primarily for basic visualization. Key challenges included limited capacity in advanced GIS and spatial modelling, high staff turnover, data quality concerns, fragmented health information systems, and infrastructure constraints, particularly in remote settings. Participants identified opportunities to strengthen applied training and mentorship, improve data quality and interoperability across platforms, and gradually integrate geospatial analysis into routine planning and resource allocation. The consultation highlighted both progress and continuing challenges in operationalizing geospatial analysis within Indonesia's malaria surveillance system. Continued investment in workforce capacity, data quality, infrastructure, and system integration may help strengthen the translation of routinely collected geospatial data into programmatic decision-making for malaria elimination.
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