Mapping the Urban Environments of Aedes aegypti Using Drone Technology

Author:

Valdez-Delgado Kenia Mayela1ORCID,Garcia-Salazar Octavio2ORCID,Moo-Llanes David A.1ORCID,Izcapa-Treviño Cecilia3,Cruz-Pliego Miguel A.3,Domínguez-Posadas Gustavo Y.3,Armendáriz-Valdez Moisés O.3,Correa-Morales Fabián4ORCID,Cisneros-Vázquez Luis Alberto1,Ordóñez-González José Genaro1,Fernández-Salas Ildefonso5,Danis-Lozano Rogelio1

Affiliation:

1. Centro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, Mexico

2. Centro de Investigación e Innovación en Ingeniería Aeronáutica (CIIIA), Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León (UANL), Apodaca 65582, Nuevo León, Mexico

3. Centro Nacional de Prevención de Desastres (CENAPRED), Secretaría de Seguridad y Protección Ciudadana, Gobierno de México, Av. Antonio Delfín Madrigal 665 Pedregal de Santo Domingo, Ciudad de México 04360, Mexico

4. Centro Nacional de Programas Preventivos y Control de Enfermedades (CENAPRECE), Secretaría de Salud, Gobierno de México, Benjamin Franklin 132, Ciudad de Mexico 11800, Mexico

5. Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León (UANL), Ave. Pedro de Alba s/n cruz con Ave. Manuel L. Barragán, San Nicolás de los Garza 66455, Nuevo León, Mexico

Abstract

Aedes aegypti is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is fundamental to define the most affordable methodology to understand these landscape variables in urban environments. The proposed methodology integrated drone technologies and traditional entomological surveillance to strengthen our knowledge about areas suitable for Ae. aegypti infestation. We included an analysis using the vegetation indexes, NDVI and NDVIRe, and their association with Ae. aegypti larvae and adults in houses from the El Vergel neighborhood Tapachula, Chiapas, Mexico. We used drone technology to obtain high-resolution photos and performed multispectral orthomosaic constructions for the data of vegetation indexes with a kernel density analysis. A negative binomial regression was performed to determine the association between the numbers of Ae. aegypti larvae and adults with the kernel density based on NDVI and NDVIRe. Medium and high values of kernel density of NDVIRe (both p-value < 0.05) and NDVI (both p-value < 0.05) were associated with a higher amount of mosquito adults per houses. The density of Ae. aegypti larvae per house did not show an association with medium and high values of NDVIRe (both p-value > 0.05) and NDVI (both p-value > 0.05). The vegetation indexes, NDVI and NDVIRe, have potential as precise predictors of Ae. aegypti adult mosquito circulation in urban environments. Drone technology can be used to map and obtain landscape characteristics associated with mosquito abundance in urban environments.

Funder

Mexican government

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

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