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

Abstract

Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

Building International Capacity for Citizen Scientist Engagement in Mosquito Surveillance and Mitigation: The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper

“…The usefulness of Mosquito Habitat Mapper data in An. stephensi surveillance [55,82] is an outcome unforeseen when the app was initially developed.…”

“…To support current computer vision/A.I. research by scientists at the University of South Florida [82], photographs of larvae submitted by citizen scientists from Benin, Kenya, Senegal, and Madagascar were subjected to optical expert validation. Anopheles stephensi is an invasive species in Africa and is a competent vector for malaria parasites.…”

“…These two factors make it useful to include taxon identification in the app even if expert optical validation of identified larvae is ultimately required. An operational computer vision system for discerning the genus and species of larvae, currently in development, is expected to eliminate the need for manual optical validation [82].…”

“…While counts of mosquito larvae provide insight into the ubiquity of specific taxa, our primary research focus seeks to identify the kinds of mosquitoes associated with different larval habitat types [26] and how habitats are distributed with respect to land cover types [75]. These data have also been used to identify the presence of harmful invasive species in new contexts [82].…”

Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics

“…In a 2021 study (Spiesman et al, 2021) (Carney et al, 2022;Minakshi et al, 2020), crop pests (Kasinathan et al, 2021), beetles (Venegas et al, 2021), and hornets (Jeong et al, 2020), as well as ants and their movements (Wu et al, 2020). In the realm of investigating bee mimicry using deep neural networks, we are aware of one recent work in 2019, which looks at M € ullerian mimicry among bumble bees across spatial scales (Ezray et al, 2019).…”