Using Citizen Science and AI to Track Deadly and Invasive Mosquitoes
The spread of mosquito-borne diseases poses an urgent threat to the Nation’s and the world’s health and welfare. Many of these diseases (West Nile disease, dengue fever, malaria, Zika) have become endemic, and outbreaks have been estimated to result annually in 2.7 million deaths worldwide. The majority of known mosquito-borne diseases are transmitted by three common mosquito genera, namely Aedes, Anopheles, and Culex. Because there are no vaccines or cures available for many of these diseases, surveillance is critical in deploying countermeasures, such as more targeted insecticide treatment and public information campaigns, to eliminate breeding habitats and mitigate disease outbreaks.
The University of South Florida is partnering with IGES and the Woodrow Wilson Center on NSF-sponsored research that is investigating deep learning techniques for automated classification of mosquito species from citizen scientist smartphone images using iNaturalist and NASA’s GLOBE Mosquito Habitat Mapper. The mosquito images, along with details about their habitat, are essential to train the AI’s algorithm. Over time, this collection of data will better inform mosquito habitat and disease prediction maps with unprecedented detail.