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@Article{LorenzCTNLQPDFGC:2020:PrAeAe,
               author = "Lorenz, Camila and Castro, Marcia C. and Trindade, Patricia 
                         Michele Pereira and Nogueira, Maur{\'{\i}}cio L. and Lage, 
                         Mariana de Oliveira and Quintanilha, Jos{\'e} A. and Parra, Maisa 
                         C. and Dibo, Margareth R. and F{\'a}varo, Eliane A. and Guirado, 
                         Marluci M. and Chiaravalloti Neto, Francisco",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Harvard T.H. Chan 
                         School of Public Health} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Faculdade de Medicina de S{\~a}o Jos{\'e} 
                         do Rio Preto} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Faculdade de Medicina 
                         de S{\~a}o Jos{\'e} do Rio Preto} and Entomology Laboratory, 
                         Endemics Control Superintendence and {Faculdade de Medicina de 
                         S{\~a}o Jos{\'e} do Rio Preto} and {} and {Universidade de 
                         S{\~a}o Paulo (USP)}",
                title = "Predicting Aedes aegypti infestation using landscape and thermal 
                         features",
              journal = "Scientific Reports",
                 year = "2020",
               volume = "10",
               number = "1",
                pages = "e21688",
                month = "Dec.",
             abstract = "Identifying Aedes aegypti breeding hotspots in urban areas is 
                         crucial for the design of efective vector control strategies. 
                         Remote sensing techniques ofer 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 S{\~a}o Jos{\'e} do Rio Preto, 
                         S{\~a}o 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 
                         ftted to the data, which showed that the winter season has the 
                         greatest infuence 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 signifcant potential for optimizing vector 
                         control strategies, future mosquito suppression, and outbreak 
                         prediction.",
                  doi = "10.1038/s41598-020-78755-8",
                  url = "http://dx.doi.org/10.1038/s41598-020-78755-8",
                 issn = "2045-2322",
             language = "en",
           targetfile = "lorenz_predicting.pdf",
        urlaccessdate = "14 abr. 2021"
}


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