Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Guest, Claire; Dewhirst, Sarah Y; Lindsay, Steve W; Allen, David J; Aziz, Sophie; Baerenbold, Oliver; Bradley, John; Chabildas, Unnati; Chen-Hussey, Vanessa; Clifford, Samuel; Cottis, Luke; Dennehy, Jessica; Foley, Erin; Gezan, Salvador A; Gibson, Tim; Greaves, Courtenay K; Kleinschmidt, Immo; Lambert, Sébastien; Last, Anna; Morant, Steve; Parker, Josephine EA; Pickett, John; Quilty, Billy J; Rooney, Ann; Shah, Manil; Somerville, Mark; Squires, Chelci; Walker, Martin; Logan, James G; Jones, Robert; Assis, Ana; Borthwick, Ewan; Caton, Laura; Edwards, Rachel; Heal, Janette; Hill, David; Jahan, Nazifa; Johnson, Cecelia; Kaye, Angela; Kirkpatrick, Emily; Kisha, Sarah; Ledeatte Williams, Zaena; Moar, Robert; Owonibi, Tolulope; Purcell, Benjamin; Rixson, Christopher; Spencer, Freya; Stefanidis, Anastasios; Stewart, Sophie; Tytheridge, Scott; Wakley, Sian; Wildman, Shanice; Aziz, Catherine; Care, Helen; Curtis, Emily; Dowse, Claire; Makepeace, Alan; Oultram, Sally-Anne; Smith, Jayde; Shenton,...

Abstract

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people.

Citation

Guest, C., Dewhirst, S. Y., Lindsay, S. W., Allen, D. J., Aziz, S., Baerenbold, O., Bradley, J., Chabildas, U., Chen-Hussey, V., Clifford, S., Cottis, L., Dennehy, J., Foley, E., Gezan, S. A., Gibson, T., Greaves, C. K., Kleinschmidt, I., Lambert, S., Last, A., Morant, S., …Davis, J. (2022). Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study. Journal of Travel Medicine, 29(3), Article taac043. https://doi.org/10.1093/jtm/taac043

• Journal Article Type: Article
• Acceptance Date: Feb 5, 2022
• Online Publication Date: Mar 24, 2022
• Publication Date: 2022
• Deposit Date: Jun 28, 2022
• Publicly Available Date: Jun 28, 2022
• Journal: Journal of Travel Medicine
• Print ISSN: 1195-1982
• Electronic ISSN: 1708-8305
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 3
• Article Number: taac043
• DOI: https://doi.org/10.1093/jtm/taac043
• Public URL: https://durham-repository.worktribe.com/output/1201198