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<p>Malaria remains a significant public health challenge in Metema District, Ethiopia, affecting 85% of the population despite ongoing prevention and control measures. This study, conducted during 2022<b>,</b> applied geospatial techniques combined with a multi-criteria evaluation (MCE) approach to identify malaria vulnerability hotspots. Environmental, socioeconomic, and epidemiological factors were analyzed, including elevation, slope, proximity to water bodies, temperature, population density, and annual parasite incidence. Data were processed using advanced GIS and remote sensing tools, employing the Analytic Hierarchy Process (AHP) for weighting parameter influence. The results delineate four vulnerability classes: Very Low (0.9%, 55&#xa0;km<sup>2</sup>), Low (20.8%, 1270.4&#xa0;km<sup>2</sup>), Moderate (22.2%, 1355&#xa0;km<sup>2</sup>), High (23%, 1415&#xa0;km<sup>2</sup>), and very high (33%, 2017.61&#xa0;km<sup>2</sup>). Villages such as Tumet, Shinfa, Zebach Bahir, and Mendar (6, 7, 8) fall into the very high vulnerability category, characterized by low elevation, proximity to rivers, and high temperatures conducive to mosquito breeding. Conversely, low-vulnerability areas are primarily in the district's northeastern and southeastern regions, marked by higher elevations and reduced proximity to water sources. This study underscores the efficacy of geospatial technologies in modeling malaria risk and highlights their utility for targeted resource allocation. The findings provide critical insights for optimizing malaria prevention and control strategies, serving as a valuable input for policymakers and stakeholders aiming to achieve malaria elimination.</p>

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Spatial identification of malaria vulnerability hotspots in Metema district, northwest Ethiopia

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Mulugeta Demisse Negesse

摘要

Malaria remains a significant public health challenge in Metema District, Ethiopia, affecting 85% of the population despite ongoing prevention and control measures. This study, conducted during 2022, applied geospatial techniques combined with a multi-criteria evaluation (MCE) approach to identify malaria vulnerability hotspots. Environmental, socioeconomic, and epidemiological factors were analyzed, including elevation, slope, proximity to water bodies, temperature, population density, and annual parasite incidence. Data were processed using advanced GIS and remote sensing tools, employing the Analytic Hierarchy Process (AHP) for weighting parameter influence. The results delineate four vulnerability classes: Very Low (0.9%, 55 km2), Low (20.8%, 1270.4 km2), Moderate (22.2%, 1355 km2), High (23%, 1415 km2), and very high (33%, 2017.61 km2). Villages such as Tumet, Shinfa, Zebach Bahir, and Mendar (6, 7, 8) fall into the very high vulnerability category, characterized by low elevation, proximity to rivers, and high temperatures conducive to mosquito breeding. Conversely, low-vulnerability areas are primarily in the district's northeastern and southeastern regions, marked by higher elevations and reduced proximity to water sources. This study underscores the efficacy of geospatial technologies in modeling malaria risk and highlights their utility for targeted resource allocation. The findings provide critical insights for optimizing malaria prevention and control strategies, serving as a valuable input for policymakers and stakeholders aiming to achieve malaria elimination.