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Front Med (Lausanne)

Title:		Use of trained scent dogs for detection of COVID-19 and evidence of cost-saving
Author(s):		Mutesa L; Misbah G; Remera E; Ebbers H; Schalke E; Tuyisenge P; Sindayiheba R; Igiraneza C; Uwimana J; Mbabazi D; Kayonga E; Twagiramungu M; Mugwaneza D; Ishema L; Butera Y; Musanabaganwa C; Rwagasore E; Twele F; Meller S; Tuyishime A; Rutayisire R; Murindahabi MM; Wilson LA; Bigirimana N; Volk HA; Ndahindwa V; Kayijuka B; Mills EJ; Muvunyi CM; Nsanzimana S;
Address:		"Center for Human Genetics, Inc., College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda. Rwanda National Joint Task Force COVID-19, Kigali, Rwanda. Kynoscience UG, Praxis und Wissenschaft, Horstel, Germany. K9 Department, Rwanda National Police, Kigali, Rwanda. Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hannover, Germany. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada"
Journal Title:		Front Med (Lausanne)
Year:		2022
Volume:		20221201
Issue:
Page Number:		1006315 -
DOI:		10.3389/fmed.2022.1006315
ISSN/ISBN:		2296-858X (Print) 2296-858X (Electronic) 2296-858X (Linking)
Abstract:		"BACKGROUND: One of the lessons learned from the coronavirus disease 2019 (COVID-19) pandemic is the importance of early, flexible, and rapidly deployable disease detection methods. Currently, diagnosis of COVID-19 requires the collection of oro/nasopharyngal swabs, nasal turbinate, anterior nares and saliva but as the pandemic continues, disease detection methods that can identify infected individuals earlier and more quickly will be crucial for slowing the spread of the virus. Previous studies have indicated that dogs can be trained to identify volatile organic compounds (VOCs) produced during respiratory infections. We sought to determine whether this approach could be applied for detection of COVID-19 in Rwanda and measured its cost-saving. METHODS: Over a period of 5 months, four dogs were trained to detect VOCs in sweat samples collected from human subjects confirmed positive or negative for COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) testing. Dogs were trained using a detection dog training system (DDTS) and in vivo diagnosis. Samples were collected from 5,253 participants using a cotton pad swiped in the underarm to collect sweat samples. Statistical analysis was conducted using R statistical software. FINDINGS: From August to September 2021 during the Delta wave, the sensitivity of the dogs' COVID-19 detection ranged from 75.0 to 89.9% for the lowest- and highest-performing dogs, respectively. Specificity ranged from 96.1 to 98.4%, respectively. In the second phase coinciding with the Omicron wave (January-March 2022), the sensitivity decreased substantially from 36.6 to 41.5%, while specificity remained above 95% for all four dogs. The sensitivity and specificity by any positive sample detected by at least one dog was 83.9, 95% CI: 75.8-90.2 and 94.9%; 95% CI: 93.9-95.8, respectively. The use of scent detection dogs was also found to be cost-saving compared to antigen rapid diagnostic tests, based on a marginal cost of approximately $14,000 USD for testing of the 5,253 samples which makes 2.67 USD per sample. Testing turnaround time was also faster with the scent detection dogs, at 3 h compared to 11 h with routine diagnostic testing. CONCLUSION: The findings from this study indicate that trained dogs can accurately identify respiratory secretion samples from asymptomatic and symptomatic COVID-19 patients timely and cost-effectively. Our findings recommend further uptake of this approach for COVID-19 detection"
Keywords:		Covid-19 Rt-pcr SARS-CoV-2 cost-saving scent dogs volatile organic compounds (VOCs);
Notes:		"PubMed-not-MEDLINEMutesa, Leon Misbah, Gashegu Remera, Eric Ebbers, Hans Schalke, Esther Tuyisenge, Patrick Sindayiheba, Reuben Igiraneza, Clement Uwimana, Jeanine Mbabazi, Diane Kayonga, Epimaque Twagiramungu, Michel Mugwaneza, Denyse Ishema, Leandre Butera, Yvan Musanabaganwa, Clarisse Rwagasore, Edson Twele, Friederike Meller, Sebastian Tuyishime, Albert Rutayisire, Robert Murindahabi, Marilyn Milumbu Wilson, Lindsay A Bigirimana, Noella Volk, Holger A Ndahindwa, Vedaste Kayijuka, Benoit Mills, Edward J Muvunyi, Claude Mambo Nsanzimana, Sabin eng Switzerland 2022/12/20 Front Med (Lausanne). 2022 Dec 1; 9:1006315. doi: 10.3389/fmed.2022.1006315. eCollection 2022"