Tommi, Rasi Kasim Samosir, Irja Tobawan Simbiak, Inriyanti Sri Pertiwi Ginting, Anggrainy Togi Marito Siregar, Anggraeni Permata Sari

UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS IN MAPPING MALARIA VULNERABILITY

Introduction

Utilization of geographic information systems in mapping malaria vulnerability . Map malaria vulnerability in Jayapura City using GIS and SMCA to aid Indonesia's elimination target. Identifies high-risk areas like Muara Tami, aligning with case data.

Abstract

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.

Review

The submitted work, "UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS IN MAPPING MALARIA VULNERABILITY," addresses a critical public health challenge: malaria elimination in Indonesia, specifically focusing on Jayapura City, a highly endemic area. The study's primary objective is to develop a spatial model for mapping malaria vulnerability using Geographic Information Systems (GIS) combined with Spatial Multi-Criteria Analysis (SMCA). This approach leverages parameters such as geology, Normalized Difference Vegetation Index (NDVI), and land cover to identify areas at risk, thereby supporting the government's ambitious 2030 malaria elimination target. This research presents a timely and relevant contribution to public health surveillance and intervention strategies. The application of GIS-SMCA is well-suited for identifying complex spatial patterns of disease vulnerability, especially in a region with consistently high malaria incidence as detailed by the provided case numbers for Jayapura City. A significant strength lies in the reported alignment between the modeled vulnerability map and actual malaria case data from 2021-2024, particularly highlighting Muara Tami District's high vulnerability and case prevalence. This congruence bolsters the model's credibility and suggests its potential utility for targeted resource allocation and focused control programs in the most affected southern and eastern areas of Jayapura City. While promising, the abstract also hints at areas for further refinement. The phrasing "overall Jayapura City area has a high vulnerability class in the Slightly Vulnerable class" could benefit from clarification in the full paper to ensure precise interpretation of vulnerability levels within the chosen classification system. Crucially, the authors themselves acknowledge the model's need for further development, specifically through the inclusion of additional, context-appropriate parameters. Expanding the input variables could enhance the model's predictive accuracy and resolution, offering a more nuanced understanding of malaria risk factors specific to Jayapura City. Overall, this study lays a valuable foundation for evidence-based malaria control, providing a robust initial framework that, with continued development, can significantly aid in achieving elimination goals.

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