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

Evangelos Kontos, Aria Samimi, View ORCID ProfileRenate W. Hakze-van der Honing, Jan Priem, View ORCID ProfileAurore Avarguès-Weber, View ORCID ProfileAlexander Haverkamp, View ORCID ProfileMarcel Dicke, Jose L Gonzales, View ORCID ProfileWim H.M. van der Poel
doi: https://doi.org/10.1101/2021.10.18.464814
Evangelos Kontos
1InsectSense, Wageningen, The Netherlands
4Laboratory of Entomology, Wageningen University, The Netherlands
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Aria Samimi
1InsectSense, Wageningen, The Netherlands
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Renate W. Hakze-van der Honing
2Wageningen Bioveterinary Research, Lelystad, The Netherlands
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  • ORCID record for Renate W. Hakze-van der Honing
Jan Priem
2Wageningen Bioveterinary Research, Lelystad, The Netherlands
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Aurore Avarguès-Weber
3Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, 118 Route de Narbonne, 31062 Toulouse, France
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Alexander Haverkamp
4Laboratory of Entomology, Wageningen University, The Netherlands
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Marcel Dicke
4Laboratory of Entomology, Wageningen University, The Netherlands
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Jose L Gonzales
2Wageningen Bioveterinary Research, Lelystad, The Netherlands
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Wim H.M. van der Poel
2Wageningen Bioveterinary Research, Lelystad, The Netherlands
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  • ORCID record for Wim H.M. van der Poel
  • For correspondence: wim.vanderpoel@wur.nl
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Abstract

The 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 18, 2021.
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Bees can be trained to identify SARS-CoV-2 infected samples
Evangelos Kontos, Aria Samimi, Renate W. Hakze-van der Honing, Jan Priem, Aurore Avarguès-Weber, Alexander Haverkamp, Marcel Dicke, Jose L Gonzales, Wim H.M. van der Poel
bioRxiv 2021.10.18.464814; doi: https://doi.org/10.1101/2021.10.18.464814
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Bees can be trained to identify SARS-CoV-2 infected samples
Evangelos Kontos, Aria Samimi, Renate W. Hakze-van der Honing, Jan Priem, Aurore Avarguès-Weber, Alexander Haverkamp, Marcel Dicke, Jose L Gonzales, Wim H.M. van der Poel
bioRxiv 2021.10.18.464814; doi: https://doi.org/10.1101/2021.10.18.464814

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