Spatiotemporal trends in Anopheles funestus breeding habitats
Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322400709X |
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| author | Grace R. Aduvukha Elfatih M. Abdel-Rahman Bester Tawona Mudereri Onisimo Mutanga John Odindi Henri E.Z. Tonnang |
| author_facet | Grace R. Aduvukha Elfatih M. Abdel-Rahman Bester Tawona Mudereri Onisimo Mutanga John Odindi Henri E.Z. Tonnang |
| author_sort | Grace R. Aduvukha |
| collection | DOAJ |
| description | Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone region of western Kenya. Employing a two-step methodological approach, we integrated multi-criteria decision analysis (MCDA) and rule-based fuzzy logic analysis to evaluate the spatiotemporal similarity or divergence of these habitats. The analysis spanned a five-year interval, 2008, 2013, and 2018 with 2013 serving as the base year for both hindcast and forecast predictions. The MCDA utilized categorical land use/land cover (LULC) and edaphic variables to identify potential breeding habitats, while climatic and topographic variables and spectral indices were analysed using fuzzy logic to assess the similarity or divergence of these habitats over time. Validation of the MCDA and fuzzy logic models was performed using a flight buffer distance based on adult An. funestus presence points (n = 136), supplemented by a limited number of larval breeding locations (n = 12) respectively. Our findings identified 147 potential An. funestus larval breeding habitats across the study area. The fuzzy logic analysis predicted a high degree of similarity (85.03%) in potential breeding habitats between the study years compared to the base year, with a divergence of 14.97%. This study demonstrates the feasibility of using semi-automated methods to detect both permanent and impermanent An. funestus breeding habitats under conditions of limited data. The methodologies developed provide a timely, cost-effective tool for enhanced surveillance and management of An funestus mosquito larval breeding, offering valuable insights for stakeholders involved in malaria vector monitoring and control. |
| format | Article |
| id | doaj-art-0f74440200ad49b69a9b520e36c562cc |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-0f74440200ad49b69a9b520e36c562cc2025-08-20T03:01:13ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-0113610435110.1016/j.jag.2024.104351Spatiotemporal trends in Anopheles funestus breeding habitatsGrace R. Aduvukha0Elfatih M. Abdel-Rahman1Bester Tawona Mudereri2Onisimo Mutanga3John Odindi4Henri E.Z. Tonnang5International Centre of Insect Physiology and Ecology, P. O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa; Corresponding author.International Centre of Insect Physiology and Ecology, P. O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaInternational Centre of Insect Physiology and Ecology, P. O. Box 30772, Nairobi 00100, Kenya; International Potato Center (CIP), P.O. Box 1269, Kigali, Rwanda; School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Private Bag 3, Wits, 2050, South AfricaSchool of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaSchool of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaInternational Centre of Insect Physiology and Ecology, P. O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaEffective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone region of western Kenya. Employing a two-step methodological approach, we integrated multi-criteria decision analysis (MCDA) and rule-based fuzzy logic analysis to evaluate the spatiotemporal similarity or divergence of these habitats. The analysis spanned a five-year interval, 2008, 2013, and 2018 with 2013 serving as the base year for both hindcast and forecast predictions. The MCDA utilized categorical land use/land cover (LULC) and edaphic variables to identify potential breeding habitats, while climatic and topographic variables and spectral indices were analysed using fuzzy logic to assess the similarity or divergence of these habitats over time. Validation of the MCDA and fuzzy logic models was performed using a flight buffer distance based on adult An. funestus presence points (n = 136), supplemented by a limited number of larval breeding locations (n = 12) respectively. Our findings identified 147 potential An. funestus larval breeding habitats across the study area. The fuzzy logic analysis predicted a high degree of similarity (85.03%) in potential breeding habitats between the study years compared to the base year, with a divergence of 14.97%. This study demonstrates the feasibility of using semi-automated methods to detect both permanent and impermanent An. funestus breeding habitats under conditions of limited data. The methodologies developed provide a timely, cost-effective tool for enhanced surveillance and management of An funestus mosquito larval breeding, offering valuable insights for stakeholders involved in malaria vector monitoring and control.http://www.sciencedirect.com/science/article/pii/S156984322400709XAnophelesEarth observationLarval breedingLarval source managementModellingRemote sensing |
| spellingShingle | Grace R. Aduvukha Elfatih M. Abdel-Rahman Bester Tawona Mudereri Onisimo Mutanga John Odindi Henri E.Z. Tonnang Spatiotemporal trends in Anopheles funestus breeding habitats International Journal of Applied Earth Observations and Geoinformation Anopheles Earth observation Larval breeding Larval source management Modelling Remote sensing |
| title | Spatiotemporal trends in Anopheles funestus breeding habitats |
| title_full | Spatiotemporal trends in Anopheles funestus breeding habitats |
| title_fullStr | Spatiotemporal trends in Anopheles funestus breeding habitats |
| title_full_unstemmed | Spatiotemporal trends in Anopheles funestus breeding habitats |
| title_short | Spatiotemporal trends in Anopheles funestus breeding habitats |
| title_sort | spatiotemporal trends in anopheles funestus breeding habitats |
| topic | Anopheles Earth observation Larval breeding Larval source management Modelling Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S156984322400709X |
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