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.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 19 (COVID-19), is a single-stranded positive-sense ribonucleic acid (RNA) virus that typically undergoes one to two single nucleotide mutations per month. COVID-19 continues to spread globally, with case fatality and test positivity rates often linked to locally circulating strains of SARS-CoV-2. Furthermore, mutations in this virus, in particular those occurring in the spike protein (involved in the virus binding to the host epithelial cells) have potential implications in current vaccination efforts. In Rwanda, more than twenty thousand cases have been confirmed as of March 14th 2021, with a case fatality rate of 1.4% and test positivity rate of 2.3% while the recovery rate is at 91.9%. Rwanda started its genomic surveillance efforts, taking advantage of pre-existing research projects and partnerships, to ensure early detection of SARS-CoV-2 variants and to potentially contain the spread of variants of concern (VOC). As a result of this initiative, we here present 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in the country from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the newly emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the VOCs, B.1.1.7 and B.1.351, among incoming travelers tested at Kigali International Airport. We also discuss the potential impact of COVID-19 control measures established in the country to control the spread of the virus. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations during the time frame of interest. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences are currently available or that no longer report positive cases. Our results point to the importance of systematically screening all incoming travelers, regardless of the origin of their travels, as well as regional collaborations for durable response to COVID-19.
