Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes

Area of Science:

• Environmental Science
• Public Health
• Infectious Diseases

Background:

• Mosquito-borne diseases cause significant global morbidity and mortality annually.
• Accurate mosquito surveillance and identification are crucial but challenging due to cost, time, and expertise requirements.
• Existing citizen science platforms lack data standardization, hindering global integration.

Purpose of the Study:

• To harmonize disparate data from citizen science platforms for improved mosquito surveillance.
• To develop a scalable, integrated framework leveraging citizen science and artificial intelligence.
• To enhance the identification of invasive and vector mosquito species.

Main Methods:

• Utilized Open Geospatial Consortium standards to integrate data from three mobile apps (Mosquito Alert, iNaturalist, GLOBE Observer).
• Launched coordinated media campaigns to boost citizen science observations.
• Developed a suite of artificial intelligence algorithms using pooled image data for species identification.

Main Results:

• Successfully harmonized data streams, enabling interoperability across platforms.
• Generated unprecedented numbers and types of citizen science observations.
• Captured the first images of targeted invasive and vector mosquito species.
• Developed AI tools for automated taxonomic and anatomical identification.

Conclusions:

• A next-generation surveillance framework integrating citizen science and AI has been established.
• This framework facilitates global collaboration and enhances mosquito control efforts.
• The combined approach offers a powerful tool to combat mosquito-borne diseases worldwide.