Semi-supervised water tank detection to support vector control of emerging infectious diseases transmitted by Aedes Aegypti

Abstract

The disease transmitting mosquito Aedes Aegypti is an increasing global threat. It breeds in small artificial containers such as rainwater tanks and can be characterized by a short flight range. The resulting high spatial variability of abundance is challenging to model. Therefore, we tested an approach to map water tank density as a spatial proxy for urban Aedes Aegypti habitat suitability. Water tank density mapping was performed by a semi-supervised self-training approach based on open accessible satellite imagery for the city of Rio de Janeiro. We ran a negative binomial generalized linear regression model to evaluate the statistical significance of water tank density for modeling inner-urban Aedes Aegypti distribution measured by an entomological surveillance system between January 2019 and December 2021. Our proposed semi-supervised model outperformed a supervised model for water tank detection with respect to the F1-score by 22%. Water tank density was a significant predictor for the mean eggs per trap rate of Aedes Aegypti. This shows the potential of the proposed indicator to enrich urban entomological surveillance systems to plan more targeted vector control interventions, presumably leading to less infectious rates of dengue, Zika, and chikungunya in the future.

Keywords

• Aedes Aegypti
• Eco-epidemiology
• GeoAI
• Object detection
• Ovitrap
• Rio de Janeiro