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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 E A; Pickett, John; Quilty, Billy J; Rooney, Ann; Shah, Manil; Somerville, Mark; Squires, Chelci; Walker, Martin; Logan, James G.
  • Guest C; Medical Detection Dogs, Milton Keynes, UK.
  • Dewhirst SY; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Lindsay SW; Department of Biosciences, Durham University, Durham, UK.
  • Allen DJ; Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
  • Aziz S; Medical Detection Dogs, Milton Keynes, UK.
  • Baerenbold O; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
  • Bradley J; MRC International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.
  • Chabildas U; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Chen-Hussey V; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Clifford S; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
  • Cottis L; Hampden Veterinary Hospital, Anchor Ln, Aylesbury, UK.
  • Dennehy J; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Foley E; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Gezan SA; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Gibson T; RoboScientific Ltd, Ely, UK.
  • Greaves CK; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Kleinschmidt I; MRC International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.
  • Lambert S; Royal Veterinary College, University of London, Hatfield, UK.
  • Last A; Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
  • Morant S; Medical Detection Dogs, Milton Keynes, UK.
  • Parker JEA; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Pickett J; Cardiff University Main Building, Cardiff, UK.
  • Quilty BJ; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
  • Rooney A; Lomond Veterinary Clinic, Helensburgh, UK.
  • Shah M; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Somerville M; Medical Detection Dogs, Milton Keynes, UK.
  • Squires C; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
  • Walker M; Royal Veterinary College, University of London, Hatfield, UK.
  • Logan JG; Arctech Innovation, The Cube, Londoneast-uk Business and Technical Park, Dagenham, UK.
J Travel Med ; 29(3)2022 05 31.
Article in English | MEDLINE | ID: covidwho-1758787
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).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Dogs / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Animals / Humans Language: English Journal subject: Communicable Diseases / Public Health Year: 2022 Document Type: Article Affiliation country: Jtm

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Dogs / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Animals / Humans Language: English Journal subject: Communicable Diseases / Public Health Year: 2022 Document Type: Article Affiliation country: Jtm