Spatial analysis and mapping of malaria risk areas using geospatial technology in the case of Nekemte City, western Ethiopia.

Abstract

Background: Malaria is a major public health issue in Nekemte City, western Ethiopia, with various environmental and social factors influencing transmission patterns. Effective control and prevention strategies require precise identification of high-risk areas. This study aims to map malaria risk zones in Nekemte City using geospatial technologies, including remote sensing and Geographic Information Systems (GIS), to support targeted interventions and resource allocation.

Methods: The study integrated environmental and social factors to assess malaria risk in the city. Environmental factors, including climatic and geographic characteristics, such as elevation, rainfall patterns, temperature, slope, and proximity to river, were selected based on experts' opinions and literature review. These factors were weighted using the analytic hierarchy process according to their relative influence on malaria hazard susceptibility. Social factors considered within the GIS framework focused on human settlements and access to resources. These included population density, proximity to health facilities, and proximity to roads. The malaria risk analysis incorporated hazard and vulnerability layers, along with Land use/cover (LULC) data. A weighted overlay analysis method combined these layers and generate the final malaria risk map.

Results: The malaria risk map identified that 18.2% (10.5 km²) of the study area was at very high risk, 18.8% (10.9 km²) at high risk, 30.4% (17.8 km²) at moderate risk, 19.8% (11.5 km²) at low risk, and 12.6% (7.3 km²) at very low risk. A combined 37% (21.4 km²) of Nekemte City was classified as at high to very high malaria risk, highlighting key areas for intervention.

Conclusions: This malaria risk map offers a valuable tool for malaria control and elimination efforts in Nekemte City. By identifying high-risk areas, the map provides actionable insights that can guide local health strategies, optimize resource distribution, and improve the efficiency of interventions. These findings contribute to enhanced public health planning and can support future regional malaria control initiatives.