Canine real-time detection of SARS-CoV-2 infections in the context of a mass screening event Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1136/bmjgh-2022-010276
Introduction Previous research demonstrated that medical scent detection dogs have the ability to distinguish SARS-CoV-2 positive from negative samples with high diagnostic accuracy. To deploy these dogs as a reliable screening method, it is mandatory to examine if canines maintain their high diagnostic accuracy in real-life screening settings. We conducted a study to evaluate the performance of medical scent detection dogs under real-life circumstances. Methods Eight dogs were trained to detect SARS-CoV-2 RT-qPCR-positive samples. Four concerts with a total of 2802 participants were held to evaluate canines’ performance in screening individuals for SARS-CoV-2 infection. Sweat samples were taken from all participants and presented in a line-up setting. In addition, every participant had been tested with a SARS-CoV-2 specific rapid antigen test and a RT-qPCR and they provided information regarding age, sex, vaccination status and medical disease history. The participants’ infection status was unknown at the time of canine testing. Safety measures such as mask wearing and distance keeping were ensured. Results The SARS-CoV-2 detection dogs achieved a diagnostic specificity of 99.93% (95% CI 99.74% to 99.99%) and a sensitivity of 81.58% (95% CI 66.58% to 90.78%), respectively. The overall rate of concordant results was 99.68%. The majority of the study population was vaccinated with varying vaccines and vaccination schemes, while several participants had chronic diseases and were under chronic medication. This did not influence dogs’ decisions. Conclusion Our results demonstrate that SARS-CoV-2 scent detection dogs achieved high diagnostic accuracy in a real-life scenario. The vaccination status, previous SARS-CoV-2 infection, chronic disease and medication of the participants did not influence the performance of the dogs in detecting the acute infection. This indicates that dogs provide a fast and reliable screening option for public events in which high-throughput screening is required.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjgh-2022-010276
- https://gh.bmj.com/content/bmjgh/7/11/e010276.full.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308915026
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308915026Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1136/bmjgh-2022-010276Digital Object Identifier
- Title
-
Canine real-time detection of SARS-CoV-2 infections in the context of a mass screening eventWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-01Full publication date if available
- Authors
-
Nele Alexandra ten Hagen, Friederike Twele, Sebastian Meller, Lisa Wijnen, Claudia Schulz, Clara Schoneberg, Lothar Kreienbrock, Maren von Köckritz‐Blickwede, Albert Osterhaus, Anna-Lena Boeck, Konstantin Boeck, Viktoria Bonda, Veronika Pilchová, Franziska Kaiser, Mariana González-Hernández, Hans Ebbers, Julia Hinsenkamp, Isabell Pink, Nora Drick, Tobias Welte, Michael P. Manns, Thomas Illig, Andreas Puyskens, Andreas Nitsche, Christiane Ernst, Michael Engels, Esther Schalke, Holger A. VolkList of authors in order
- Landing page
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https://doi.org/10.1136/bmjgh-2022-010276Publisher landing page
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https://gh.bmj.com/content/bmjgh/7/11/e010276.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://gh.bmj.com/content/bmjgh/7/11/e010276.full.pdfDirect OA link when available
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Medicine, Vaccination, Context (archaeology), Population, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Disease, Coronavirus disease 2019 (COVID-19), Internal medicine, Immunology, Infectious disease (medical specialty), Biology, Environmental health, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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