Artificial Intelligence and Citizen Science as a Tool for Global Mosquito Surveillance: Madagascar Case Study

<i>Anopheles stephensi</i> is an invasive and deadly malaria vector with the ability to use artificial containers as larval habitats. This ability is unique for malaria vectors in Africa and requires distinct surveillance strategies for early detection and rapid response. In this study, we trained a variety of artificial intelligence (AI) image recognition algorithms, using thousands of smartphone photos of laboratory-authenticated <i>An. stephensi</i> and seven endemic mosquito species, to develop a citizen science-friendly tool for <i>An. stephensi</i> detection. In Antananarivo, Madagascar, citizen science observations of >132 <i>Anopheles</i> spp. larvae from multiple artificial containers-including one closeup photo of a larva, from a tire-were submitted via NASA's GLOBE Observer app in March 2020 and discovered years later. Given that genetic testing was no longer possible, this photo was used as a proof-of-concept to determine whether the AI species identification could be used on citizen science-generated images. The tire larva was classified as <i>An. stephensi</i> by all 11 species models, which yielded high accuracy and confidence (up to 99.34%) and included a false positive rate of <1%. Furthermore, explainable AI (XAI) heat maps led to the discovery of dark spots in abdominal segment VI corresponding to testes, corroborating a separate classification of the tire larva as male by the sex model. All available evidence suggests that AI image identification would have flagged this larva as a suspect <i>An. stephensi</i>, which could have been submitted to a molecular laboratory for further confirmation. Results demonstrate the power of integrating citizen science and AI-for which we provide free online tools-as a low-cost signal for malaria programs to confirm and respond to, and as complementary surveillance to fill the critical knowledge gaps in the distribution of invasive <i>An. stephensi</i> across Africa and beyond.

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Publication Details

Journal
Insects
Published
2025-10-28
DOI
https://doi.org/10.3390/insects16111098
Primary Topic
Malaria Research and Control
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence and Citizen Science as a Tool for Global Mosquito Surveillance: Madagascar Case Study

Tanvir Bhuiyan, Sriram Chellappan, Johnny A. Uelmen, Sarah Zohdy et al.
Insects
Malaria Research and Control
article

Artificial Intelligence and Citizen Science as a Tool for Global Mosquito Surveillance: Madagascar Case Study

Tanvir Bhuiyan, Sriram Chellappan, Johnny A. Uelmen, Sarah Zohdy, Russanne Low, Lala Sahondra Rafarasoa, Ryan M. Carney, Pradeep Annamalai Subramani, Farhat Binte Azam, Tovo Mbolatiana Andrianjafy, Madison M. Ogbondah, Valéry Riantsoa, Mamisoa A. Ramahazomanana, Ranto N. Rasolofo, Karlene Gehrisch
article en

Abstract

<i>Anopheles stephensi</i> is an invasive and deadly malaria vector with the ability to use artificial containers as larval habitats. This ability is unique for malaria vectors in Africa and requires distinct surveillance strategies for early detection and rapid response. In this study, we trained a variety of artificial intelligence (AI) image recognition algorithms, using thousands of smartphone photos of laboratory-authenticated <i>An. stephensi</i> and seven endemic mosquito species, to develop a citizen science-friendly tool for <i>An. stephensi</i> detection. In Antananarivo, Madagascar, citizen science observations of >132 <i>Anopheles</i> spp. larvae from multiple artificial containers-including one closeup photo of a larva, from a tire-were submitted via NASA's GLOBE Observer app in March 2020 and discovered years later. Given that genetic testing was no longer possible, this photo was used as a proof-of-concept to determine whether the AI species identification could be used on citizen science-generated images. The tire larva was classified as <i>An. stephensi</i> by all 11 species models, which yielded high accuracy and confidence (up to 99.34%) and included a false positive rate of <1%. Furthermore, explainable AI (XAI) heat maps led to the discovery of dark spots in abdominal segment VI corresponding to testes, corroborating a separate classification of the tire larva as male by the sex model. All available evidence suggests that AI image identification would have flagged this larva as a suspect <i>An. stephensi</i>, which could have been submitted to a molecular laboratory for further confirmation. Results demonstrate the power of integrating citizen science and AI-for which we provide free online tools-as a low-cost signal for malaria programs to confirm and respond to, and as complementary surveillance to fill the critical knowledge gaps in the distribution of invasive <i>An. stephensi</i> across Africa and beyond.

InsectsVol. 16(11)
Centers for Disease Control and Prevention (US), University of Antananarivo (MG), University of South Florida (US), Centers for Disease Control and Prevention (UG), Institute for Global Environmental Strategies (US), University of Wisconsin Health (US)
Openalex Percentile: Top 59%
Malaria Research and Control
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