Predicting Aedes aegypti infestation using landscape and thermal features

Abstract Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features,...

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Autores principales:	Camila Lorenz, Marcia C. Castro, Patricia M. P. Trindade, Maurício L. Nogueira, Mariana de Oliveira Lage, José A. Quintanilha, Maisa C. Parra, Margareth R. Dibo, Eliane A. Fávaro, Marluci M. Guirado, Francisco Chiaravalloti-Neto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ddeaaf50e9d34f9baeb9fc32bd704e0a
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spelling	oai:doaj.org-article:ddeaaf50e9d34f9baeb9fc32bd704e0a2021-12-02T12:33:06ZPredicting Aedes aegypti infestation using landscape and thermal features10.1038/s41598-020-78755-82045-2322https://doaj.org/article/ddeaaf50e9d34f9baeb9fc32bd704e0a2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78755-8https://doaj.org/toc/2045-2322Abstract Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.Camila LorenzMarcia C. CastroPatricia M. P. TrindadeMaurício L. NogueiraMariana de Oliveira LageJosé A. QuintanilhaMaisa C. ParraMargareth R. DiboEliane A. FávaroMarluci M. GuiradoFrancisco Chiaravalloti-NetoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Camila Lorenz Marcia C. Castro Patricia M. P. Trindade Maurício L. Nogueira Mariana de Oliveira Lage José A. Quintanilha Maisa C. Parra Margareth R. Dibo Eliane A. Fávaro Marluci M. Guirado Francisco Chiaravalloti-Neto Predicting Aedes aegypti infestation using landscape and thermal features
description	Abstract Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.
format	article
author	Camila Lorenz Marcia C. Castro Patricia M. P. Trindade Maurício L. Nogueira Mariana de Oliveira Lage José A. Quintanilha Maisa C. Parra Margareth R. Dibo Eliane A. Fávaro Marluci M. Guirado Francisco Chiaravalloti-Neto
author_facet	Camila Lorenz Marcia C. Castro Patricia M. P. Trindade Maurício L. Nogueira Mariana de Oliveira Lage José A. Quintanilha Maisa C. Parra Margareth R. Dibo Eliane A. Fávaro Marluci M. Guirado Francisco Chiaravalloti-Neto
author_sort	Camila Lorenz
title	Predicting Aedes aegypti infestation using landscape and thermal features
title_short	Predicting Aedes aegypti infestation using landscape and thermal features
title_full	Predicting Aedes aegypti infestation using landscape and thermal features
title_fullStr	Predicting Aedes aegypti infestation using landscape and thermal features
title_full_unstemmed	Predicting Aedes aegypti infestation using landscape and thermal features
title_sort	predicting aedes aegypti infestation using landscape and thermal features
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ddeaaf50e9d34f9baeb9fc32bd704e0a
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Predicting Aedes aegypti infestation using landscape and thermal features

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