Predicting Aedes aegypti infestation using landscape and thermal features

Camila Lorenz,Marcia C Castro,José A Quintanilha,Patricia M P Trindade,Marluci M Guirado,Maurício L Nogueira,Maisa C Parra,Mariana De Oliveira Lage,Francisco Chiaravalloti-Neto,Margareth R Dibo,Eliane A Fávaro

doi:10.1038/s41598-020-78755-8

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.

Highlights

Genera, including Anopheles[12,15] and Aedes[5,14,16,17]
We considered the relative root mean square error (RMSE) (Rel_RMSE) and relative mean absolute error (MAE) (Rel_MAE) as the error divided by the average of the observed response
We used remotely sensed temperature data and land-cover classification to identify features associated with adult female Ae. aegypti mosquitos in an urban neighborhood of São José do Rio Preto, São Paulo, Brazil

Summary

Introduction

Genera, including Anopheles[12,15] and Aedes[5,14,16,17]. Specific land use and cover types can favor the proliferation of mosquitoes. Studies of urban micro-climate show that temperature can vary significantly over relatively short distances[23], which are likely to impact mosquito populations[17] and their capacity as vectors of disease[24,25,26]. The Landsat-8 Thermal Infrared Sensor (TIRS) provides images at a relatively fine temporal (every two weeks) and spatial (30 m) scales. These datasets offer opportunities to improve the accuracy and precision of mosquito infestation prediction models. Most of the existing studies that have focused on the Aedes genus of mosquitos have employed satellite-derived surface temperature d ata[17,27,28], which may differ from air temperature by several degrees[27], especially during the day. We sought to demonstrate the application of remote sensing technology for the prediction of mosquito infestation in the Vila Toninho neighborhood of São José do Rio Preto, São Paulo, Brazil (Fig. 1)

Methods

Results

Discussion

Conclusion