Surveillance and control of mosquito-borne diseases through automated species identification and spatiotemporal modeling

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 state of Florida is a domestic epicenter for mosquito-borne diseases, with a devastating Zika outbreak in 2018 and locally transmitted cases of dengue fever in 2019 and 2020. 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, real-time surveillance is critical in deploying countermeasures, such as more targeted insecticide treatment and public information campaigns, to eliminate breeding habitats and mitigate disease outbreaks. This award supports research to develop a platform for large-scale automated identification of mosquito genera and species via smartphone images. The platform will enable citizens to upload smartphone images to contribute to real-time data data on mosquito populations worldwide.

The project will investigate deep learning techniques for automated classification of mosquito species from smartphone images. Mosquito identification is a challenging problem, as species differences are not obvious to the untrained eye. Identification techniques will be based on segmentation of different anatomical features of mosquitoes. The project will result in validated algorithms for automated classification of species at scale. The algorithms will be embedded in a platform for crowd-sourced input of geographically-tagged images of mosquitoes and dead birds. These data will be leveraged to detect introductions of invasive mosquitoes, generate mosquito distribution maps, and produce real-time risk maps to enable early detection of disease outbreaks. The identification methods are expected to be useful for the classification of other insect species and to further investigations in mosquito ecology and evolutionary biology with the goal of improving public health.

NSF project details

Modeling
Surveillance

Oct 2020 — Sep 2024

$916,000

Ethiopia
Mauritius

Anopheles stephensi