Predicting Aedes aegypti infestation using landscape and thermal features.

Area of Science:

• Environmental Science
• Medical Entomology
• Remote Sensing

Background:

• Effective vector control strategies for Aedes aegypti require identification of urban breeding hotspots.
• Remote sensing offers valuable tools for mapping habitat suitability and understanding mosquito populations.

Purpose of the Study:

• To evaluate the association between urban landscape features, thermal data, and Aedes aegypti infestations.
• To build and validate a predictive model for mosquito abundance using remote sensing data.

Main Methods:

• Entomological surveys were conducted monthly from 2016-2019 in São José do Rio Preto, Brazil.
• WorldView-3 satellite imagery and Landsat-8 TIRS data were used to extract land cover and land surface temperature.
• A multilevel negative binomial model was fitted using 2016-2018 data and validated with 2019 summer data.

Main Results:

• The winter season showed the greatest influence on decreasing mosquito abundance.
• Green areas and pavements were negatively associated with adult female Aedes aegypti.
• Higher cover of asbestos roofs and exposed soil positively correlated with mosquito presence.

Conclusions:

• Urban landscape features, identified via remote sensing, are significantly associated with Aedes aegypti abundance.
• These findings highlight the potential of remote sensing for optimizing vector control and predicting outbreaks.
• Socio-economic factors influence landscape features that also provide favorable mosquito breeding conditions.