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Publication . Preprint . Article . 2021

Bees can be trained to identify SARS-CoV-2 infected samples

Evangelos Kontos; Aria Samimi; Renate W. Hakze-van der Honing; J. Priem; Aurore Avarguès-Weber; Alexander Haverkamp; Marcel Dicke; +2 Authors
Open Access
Published: 28 Nov 2021
Publisher: HAL CCSD

AbstractThe COVID-19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the Volatile Organic Compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARS-CoV-2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink’s odours and could therefore be part of a wider SARS-CoV-2 diagnostic system. We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention. We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subject’s health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee-based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods.

Subjects by Vocabulary

Microsoft Academic Graph classification: Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Diagnostic test Artificial intelligence business.industry business Diagnostic system Biology Diagnostic evaluation 2019-20 coronavirus outbreak Machine learning computer.software_genre computer Memory retention


[SDV]Life Sciences [q-bio], EPS, PE&RC, Virology & Molecular Biology, Epidemiology, Bio-informatics & Animal models, Laboratory of Entomology, Conditioning, Covid-19, Detection, Honeybees, Olfaction, SARS-CoV2, Virologie & Moleculaire Biologie, Epidemiologie, Bioinformatica & Diermodellen, Laboratorium voor Entomologie, Epidemiology, Bio-informatics & Animal models, Epidemiologie, Bioinformatica & Diermodellen, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology

Funded by
EC| One Health EJP
One Health EJP
Promoting One Health in Europe through joint actions on foodborne zoonoses, antimicrobial resistance and emerging microbiological hazards.
  • Funder: European Commission (EC)
  • Project Code: 773830
  • Funding stream: H2020 | COFUND-EJP
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Article . 2022
Providers: NARCIS