Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study
Author:
Guest Claire1, Dewhirst Sarah Y2ORCID, Lindsay Steve W34, Allen David J56, Aziz Sophie1, Baerenbold Oliver78, Bradley John98, Chabildas Unnati2, Chen-Hussey Vanessa2, Clifford Samuel1011ORCID, Cottis Luke12, Dennehy Jessica2, Foley Erin2, Gezan Salvador A2, Gibson Tim13, Greaves Courtenay K2, Kleinschmidt Immo98, Lambert Sébastien14, Last Anna156, Morant Steve1, Parker Josephine E A2, Pickett John16, Quilty Billy J1011, Rooney Ann17, Shah Manil2, Somerville Mark1, Squires Chelci2, Walker Martin14, Logan James G2186, 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 Fiona, Hutchins Harry, Mart Robert, Cartwright Jo-anne, Forsey Miranda, Goodsell Kerry, Kittridge Lauren, Nicholson Anne, Ramos Angelo, Ritches Joanne, Setty Niranjan, Vertue Mark, Bergstrom Malin, Chaudhary Zain, De Wilton Angus, Gaskell Kate, Houlihan Catherine, Jones Imogen, Margaritis Marios, Miralhes Patricia, Owens Leah, Rampling Tommy, Rickman Hannah, Boffito Marta, Fernandez Candida, Cotterell Bryony, Guerdette Anne-Marie, Tsaknis George, Turns Margaret, Walsh Joanne, Frankland Lisa, West Raha, Holland Maureen, Keenan Natalie, Wassall Helen, Young Megan, Rangeley Jade, Saalmink Gwendolyn, Adlakha Sanjay, Buckley Philip, Allsop Lynne, Smith Susan, Sowter Donna, Campbell Alison, Jones Julie, Laird Steve, O’Toole Sarah, Ryan Courteney, Evans Jessica, Rand James, Schumacher Natasha, Hazelton Tracey, Dodgson Andrew, Glasgow Susannah, Kadiu Denise, Lopuszansky Orianne, Oommen Anu, Prabhu Joshi, Pursell Molly, Turner Jane, Walton Hollie, Andrews Robert, Cruickshank Irena, Thompson Catherine, Wainwright Tania, Roebuck Alun, Lawrence Tara, Netherton Kimberley, Hewitt Claire, Shephardson Sarah, Crasto Winston Andrew, Lake Judith, Musanhu Rosemary, Walker Rebecca, Burns Karen, Higham Andrew, Le Bas Julie, Mackenzie Nicola, Thatcher Hilary, Beadle Shannen, Buckley Sarah, Castle Gail, Fletcher Aimee, Holbrook Sara, Kane Patricia, Lindley Kate, Lowry Tracey, Lupton Stephanie, Oddy Sharon, Slater Lynda, Sylvester Martin, Agwuh Kenneth, Maxwell Veronica, Ryder Stephen, Topham Kirsty, Egbuniwe Obi, Matthews Rebecca, Arenas-Pinto Alejandro, Prymas Paulina, Severn Abigail, Shaw Amber, Begum Safia, Lenton Daniel, Scriven James, Leeman Lucy, Rudge Karen, Storr Emma, Alvarez Ana, Forster Kate, Hind Daniel, Cook Natalie, Peeling Rosanna, Carey Peter, Wilson Anne, Davis Jane,
Affiliation:
1. Medical Detection Dogs , Milton Keynes, UK 2. Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park , Dagenham, UK 3. Department of Biosciences , , Durham, UK 4. Durham University , , Durham, UK 5. Department of Infection Biology , , London, UK 6. Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine , , London, UK 7. Department of Medical Statistics , , London, UK 8. London School of Hygiene and Tropical Medicine , , London, UK 9. MRC International Statistics and Epidemiology Group , , London, UK 10. Department of Infectious Disease Epidemiology , , London, UK 11. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine , , London, UK 12. Hampden Veterinary Hospital , Anchor Ln, Aylesbury, UK 13. RoboScientific Ltd , Ely, UK 14. Royal Veterinary College, University of London , Hatfield, UK 15. Clinical Research Department , , London, UK 16. Cardiff University Main Building , Cardiff, UK 17. Lomond Veterinary Clinic , Helensburgh, UK 18. Department of Disease Control , , London, UK
Abstract
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.
Trial Registration NCT04509713 (clinicaltrials.gov).
Funder
Department of Health and Social Care, UK Government Durham University COVID-19 Response Fund NIHR Clinical Research Network Support
Publisher
Oxford University Press (OUP)
Cited by
14 articles.
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