Spatial analysis-based risk identification for malaria elimination efforts in Papua

Papua is a region grappling with various health issues, particularly malaria. A study revealed that a staggering 92% of national malaria cases are concentrated in Papua. This poses a significant challenge given the Sustainable Development Goals (SDGs) target of malaria elimination by 2030. A risk analysis was conducted to spatially identify the distribution of malaria. This approach employed Geographic Information Systems (GIS) and Remote Sensing, utilizing the Model Builder and weighted overlay methods. The analysis indicated that 91.77% of the region is at a moderate risk, while 0.33% is at a high risk. Targeted interventions are imperative for areas classified as moderate to high risk. Comprehensive prevention strategies must address core challenges such as early detection, case management, treatment, and improved access to healthcare facilities.

Authors

Publication Details

Journal
Journal of Geospatial Science and Analytics
Published
2025-03-30
DOI
https://doi.org/10.58524/jgsa.v1i1.8
Primary Topic
Malaria Research and Control
Type
article
Field-Weighted Citation Impact
0.00
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article

Spatial analysis-based risk identification for malaria elimination efforts in Papua

Muhammad Ilham Maulana, Ridho Herman Saputra, Ida Fransiska Pandiangan
Journal of Geospatial Science and Analytics
Malaria Research and Control
article

Spatial analysis-based risk identification for malaria elimination efforts in Papua

Muhammad Ilham Maulana, Ridho Herman Saputra, Ida Fransiska Pandiangan
article en

Abstract

Papua is a region grappling with various health issues, particularly malaria. A study revealed that a staggering 92% of national malaria cases are concentrated in Papua. This poses a significant challenge given the Sustainable Development Goals (SDGs) target of malaria elimination by 2030. A risk analysis was conducted to spatially identify the distribution of malaria. This approach employed Geographic Information Systems (GIS) and Remote Sensing, utilizing the Model Builder and weighted overlay methods. The analysis indicated that 91.77% of the region is at a moderate risk, while 0.33% is at a high risk. Targeted interventions are imperative for areas classified as moderate to high risk. Comprehensive prevention strategies must address core challenges such as early detection, case management, treatment, and improved access to healthcare facilities.

Journal of Geospatial Science and AnalyticsVol. 1(1)
Openalex Percentile: Top 78%
Malaria Research and Control
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