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, 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.

Figure 1

Municipality of São José do Rio Preto, state of São Paulo, Brazil. Vila Toninho neighborhood (study area) is highlighted in red. Map data: Google, Maxar Technologies.

Figure 2

Vila Toninho neighborhood. Left: WorldView 3 image showing seven different landcover categories. Right: Landsat 8 TIRS showing surface temperature.

Figure 3

Distribution of Ae. aegypti adult female mosquitoes by season in Vila Toninho from 2016 to 2018.

Figure 4

Scatterplots of each independent variable tested with the number of mosquitoes considering each season. Landcover categories (in %): WAT + SHA water and shadow, EXP.SOIL exposed soil, CERAMIC T ceramic tile, ASBESTOS R asbestos roof, GREEN green areas (trees and grass), AIR TEMP average season temperature (ºC), WINTER binary variable indicating if is winter season or not, SURF TEMP surface temperature (TIRS Landsat 8, ºC), RAIN average season rainfall (mm).

Figure 5

QQ-plot of the selected multilevel negative binomial model. n.s non-significant. The KS test indicates that the points are not far from the reference line (non-significant p-value). Similarly, the Outlier test did not reveal any significant presence of outliers in the data. It tests if the residues (expected/observed) have normal distribution and if there is any discrepant point.

Figure 6

Number of Ae. aegypti adult females observed and predicted per trap using the negative binomial model pooled by season (correlation coefficient R = 0.74).