Global mosquito observations dashboard (GMOD)
Anyone around the world can use their smartphone to help scientists track the most dangerous animal alive — the mosquito! Our team especially needs photos of invasive mosquitoes that breed in artificial containers: malaria vector Anopheles stephensi, as well as Aedes aegypti and Aedes albopictus, the culprits of dengue, Zika, and yellow fever outbreaks.
This dashboard integrates mosquito observation data from various citizen science platforms. Volunteers can contribute by downloading one of three mobile apps:
- GLOBE Observer to monitor larval mosquitoes, breeding habitats, and land cover via photos; supported by NASA through the GLOBE Program.
- Mosquito Alert to monitor adult mosquitoes and breeding habitats via photos, as well as bites; an expert-validated system coordinated by several research institutions in Spain (CEAB-CSIC, CREAF, ICREAUPF).
- iNaturalist to monitor adult and larval mosquitoes via photos; a social platform for sharing biodiversity observations, and a joint initiative of the California Academy of Sciences and National Geographic Society, used by thousands of organizations and researchers. iNaturalist projects: Africa: mosquitoesInAfrica.org; Americas: mosquitoAI.org
This dashboard was created to support real-time monitoring worldwide, and to reuse images to train machine learning algorithms to predict the species of a mosquito based on a photo: see our free web-based tools available at mosquitoID.org.
Due to the global health threat posed by mosquito-borne diseases, there is a nearly universal need for increased surveillance and habitat mitigation worldwide. Mosquito populations are traditionally tracked through specimens found in traps, and identified by expert entomologists or DNA analysis. However, these surveillance methods take time and are hard to scale. Broad availability and access to mobile devices has enabled the activation of citizen scientists as a cost-effective solution to improve the spatial and temporal coverage of mosquito surveillance in communities.
This dashboard was developed by Johnny Uelmen (USF) and Connor Mapes (USF/TWC), and funded by the National Science Foundation under Grant No. IIS-2014547 to PI Ryan Carney (USF) and Co-PIs Sriram Chellappan (USF), Russanne Low (IGES), and Anne Bowser (TWC).