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<p>Mosquito-borne diseases pose a significant public health threat, particularly in regions where environmental conditions favor mosquito proliferation. Identifying high-risk breeding habitats is essential for effective vector control and disease prevention. This study employs multi-temporal Landsat satellite imagery combined with spatial analyses to assess the ecological risk of mosquito breeding habitats in Beni Sweif Governorate, Egypt. Two field surveys, conducted in September 2020 and January 2021, were used to locate breeding sites and collect mosquito larvae following standard WHO protocols. To characterize the environmental factors influencing mosquito distribution, Landsat images synchronized with field data were processed to generate four ecological indicators: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST). These indicators were integrated into a cartographic risk model to predict and map mosquito proliferation hotspots and potential disease transmission zones. A total of 8,735 mosquito larvae were collected from accessible breeding sites, representing five genera and ten species. The most abundant species was <i>Culex pipiens</i> (55.31%, <i>n</i> = 4,831), followed by <i>Ochlerotatus caspius</i> (8.77%, <i>n</i> = 272), while <i>Culex theileri</i> was the least abundant (0.13%, <i>n</i> = 4). Five of the identified species are known disease vectors in Egypt. The summer risk model estimated 231.82 km<sup>2</sup> (2.12% of the study area) as high-risk zones, with approximately 85% of these areas classified as agricultural land. In contrast, the winter model indicated a significant decrease in risk, with only 75.23 km<sup>2</sup> (0.69%) identified as potential mosquito breeding sites. This study highlights the importance of remote sensing and spatial modeling in mosquito surveillance and vector-borne disease risk assessment. The findings can support targeted vector control strategies to mitigate public health risks in the region.</p>

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Ecological risk assessment and spatial modeling of mosquito breeding habitats in Beni Sweif Governorate, Egypt

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Mohamed Sowilem,
  • Ahmed El-Zeiny,
  • Hala Effat,
  • Kamel Mansour

摘要

Mosquito-borne diseases pose a significant public health threat, particularly in regions where environmental conditions favor mosquito proliferation. Identifying high-risk breeding habitats is essential for effective vector control and disease prevention. This study employs multi-temporal Landsat satellite imagery combined with spatial analyses to assess the ecological risk of mosquito breeding habitats in Beni Sweif Governorate, Egypt. Two field surveys, conducted in September 2020 and January 2021, were used to locate breeding sites and collect mosquito larvae following standard WHO protocols. To characterize the environmental factors influencing mosquito distribution, Landsat images synchronized with field data were processed to generate four ecological indicators: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST). These indicators were integrated into a cartographic risk model to predict and map mosquito proliferation hotspots and potential disease transmission zones. A total of 8,735 mosquito larvae were collected from accessible breeding sites, representing five genera and ten species. The most abundant species was Culex pipiens (55.31%, n = 4,831), followed by Ochlerotatus caspius (8.77%, n = 272), while Culex theileri was the least abundant (0.13%, n = 4). Five of the identified species are known disease vectors in Egypt. The summer risk model estimated 231.82 km2 (2.12% of the study area) as high-risk zones, with approximately 85% of these areas classified as agricultural land. In contrast, the winter model indicated a significant decrease in risk, with only 75.23 km2 (0.69%) identified as potential mosquito breeding sites. This study highlights the importance of remote sensing and spatial modeling in mosquito surveillance and vector-borne disease risk assessment. The findings can support targeted vector control strategies to mitigate public health risks in the region.