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J Travel Med

Title:		Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study
Author(s):		Guest C; Dewhirst SY; Lindsay SW; Allen DJ; Aziz S; Baerenbold O; Bradley J; Chabildas U; Chen-Hussey V; Clifford S; Cottis L; Dennehy J; Foley E; Gezan SA; Gibson T; Greaves CK; Kleinschmidt I; Lambert S; Last A; Morant S; Parker JEA; Pickett J; Quilty BJ; Rooney A; Shah M; Somerville M; Squires C; Walker M; Logan JG; Team CDR;
Address:		"Medical Detection Dogs, Milton Keynes, UK. Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK. Department of Biosciences, Durham University, Durham, UK. Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK. MRC International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK. Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. Hampden Veterinary Hospital, Anchor Ln, Aylesbury, UK. RoboScientific Ltd, Ely, UK. Royal Veterinary College, University of London, Hatfield, UK. Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. Cardiff University Main Building, Cardiff, UK. Lomond Veterinary Clinic, Helensburgh, UK. Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK"
Journal Title:		J Travel Med
Year:		2022
Volume:		29
Issue:		3
Page Number:		-
DOI:		10.1093/jtm/taac043
ISSN/ISBN:		1708-8305 (Electronic) 1195-1982 (Print) 1195-1982 (Linking)
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)"
Keywords:		Animals Asymptomatic Infections *COVID-19/diagnosis *Dogs Humans Mass Screening SARS-CoV-2 Sensitivity and Specificity Volatile Organic Compounds/analysis Covid-19 infection control public health rapid screening;
Notes:		"MedlineGuest, 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, Sebastien Last, Anna Morant, Steve Parker, Josephine E A Pickett, John Quilty, Billy J Rooney, Ann Shah, Manil Somerville, Mark Squires, Chelci Walker, Martin Logan, James G eng MR/R010161/1/MRC_/Medical Research Council/United Kingdom Observational Study England 2022/03/25 J Travel Med. 2022 May 31; 29(3):taac043. doi: 10.1093/jtm/taac043"