To address the persistently high malaria cases, the Indonesian government has set a target of malaria elimination by 2030. One strategy implemented by the central government is to encourage the commitment of local governments, especially in highly endemic areas, in malaria control. Research that can support the government's strategy to achieve the malaria elimination target needs to be continuously conducted and developed. One study that is highly relevant to the government's malaria elimination program is mapping or zoning malaria vulnerability levels. Jayapura City itself is the second-highest area for malaria cases in Papua. According to a report from the Jayapura City Health Office, there were 28,648 malaria cases in Jayapura City in 2019 with an API of 92.55 per 1,000 residents. In 2020, there were 28,075 cases with an API of 89.35 per 1,000 residents. In 2021, there were 30,235 cases with an API of 99.49 per 1,000 residents. This condition also makes Jayapura City highly vulnerable to malaria. Therefore, mapping malaria vulnerability in Jayapura City is crucial. One technology that can be used to map malaria vulnerability is a Geographic Information System (GIS). The GIS used in this study was combined with specific methods. The method used in this study was Spatial Multi-Criteria Analysis (SMCA). The parameters used in this study were geology, NDVI, and land cover. Each parameter has its own weight and score. The results showed that the overall Jayapura City area has a high vulnerability class in the Slightly Vulnerable class. The Slightly Vulnerable class is most common in Muara Tami District, while the Not Vulnerable class is most common in North Jayapura District. Data on the distribution of malaria from 2021 to 2024 shows that Muara Tami District has the highest number of malaria cases. On average, the pattern of malaria distribution shows that the southern to eastern areas have a higher prevalence of the disease compared to the northern areas of Jayapura City. The results of the malaria vulnerability mapping model, case data, and the average distribution pattern of malaria cases indicate that the modeled data aligns with the actual data and case distribution pattern. This model still needs to be developed, particularly by adding parameters appropriate to the study area.
Collaboration network of the authors
11 authors ·
26 co-authorship links
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
Affiliation unknown
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.