Predicting Aedes aegypti infestation using landscape and thermal features

Our plain-English summary of Predicting Aedes aegypti infestation using landscape and thermal features, by C. Lorenz, M.C. Castro, P.M.P. Trindade, et al (Scientific Reports, 2020).

The study

• Authors: C. Lorenz, M.C. Castro, P.M.P. Trindade, M.L. Nogueira, M. de Oliveira Lage, J.A. Quintanilha, M.C. Parra, M.R. Dibo, E.A. Fávaro, M.M. Guirado, F. Chiaravalloti-Neto
• Journal: Scientific Reports
• Published: 2020;10(1):21688
• Cited by: 20 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

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.

In plain English

This study is about mosquitoes, not about the health effects of asbestos. Asbestos appears only as one type of roofing identified in satellite images. The authors trapped adult female Aedes aegypti mosquitoes at up to 60 households in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, with monitoring from 2016 to 2019. Satellite images were used to measure land cover within 30 m of each trap, including asbestos roofs, and to measure surface temperature. A statistical model was built from 2016 to 2018 data and checked against summer 2019 data. Average catches per trap were 4.66 in summer, 3.59 in fall, 1.40 in winter and 3.69 in spring. In the model, more asbestos roof and exposed soil cover was associated with more female mosquitoes. Green areas, pavement and winter were associated with fewer. The authors concluded that winter was most strongly associated with reduced infestation. In their view, these land-cover features reflect local socio-economic conditions and also differ in heat and water-ponding characteristics.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Entomological field survey with satellite (remote sensing) land-cover and surface-temperature data, analysed with a multilevel (mixed) negative binomial model
• Who was studied: Adult female Aedes aegypti mosquitoes trapped at up to 60 households in Vila Toninho, São José do Rio Preto, São Paulo, Brazil (about 5,600 inhabitants, with poorer socio-economic indicators than city averages). Monitoring ran from 2016 to 2019: 2016 to 2018 data built the model and summer 2019 data validated it.
• Exposure: No human asbestos exposure was studied. The explanatory variables were percentage land cover within 30 m of each trap: pavement, tile roof, asbestos roof, roof slab, green area, exposed soil, and water plus shadow. Season and satellite-derived surface temperature were also tested.
• Main results:
  • Mean mosquitoes captured per trap were 4.66 in summer, 3.59 in fall, 1.40 in winter and 3.69 in spring. Winter had the lowest abundance (p less than 0.001).
  • In the final model, asbestos roof cover and exposed soil cover were positively associated with the number of female mosquitoes.
  • Green area, paving and winter were negatively associated with mosquito abundance.
  • Table 1 printed the following values for asbestos roof: estimate 0.0270723, standard error 0.0159589, p-value 0.0018139, exponentiated estimate 1.0732909.
  • Table 1 printed the following values for winter: estimate −1.0052431, exponentiated estimate 0.3659557.
  • Surface temperature near traps was positively associated with mosquito numbers. It was dropped from the model because season provided suitable predictive power.
  • The model fitted better than a null model: AIC 1,047.42 for the model versus 1208.24 for the null model.
  • On the summer 2019 validation data, relative RMSE was 0.1367 and relative MAE was 0.0724. The correlation between observed and predicted counts was R = 0.74.
• The authors' conclusion: The authors concluded that reductions in mosquito infestation were most strongly associated with winter. Green areas and pavement were negatively associated with adult females, and asbestos roofs and exposed soil were positively associated. In the authors' view, these variables reflect local socio-economic conditions and differ in thermal and water-ponding characteristics.
• Limitations the authors state:
  • The thermal satellite images were not fine enough to detect house-to-house variability.
  • The 30 m buffer assumed as the average flight radius of an adult female mosquito may be an underestimate.
  • Asbestos roofs may not indicate low socioeconomic level in all countries.

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