Validation of Saliva and Self-Administered Nasal Swabs for COVID-19 Testing
Teo, A. K. J.; Choudhury, Y.; Tan, I. B.; Cher, C. Y.; Chew, S. H.; Wan, Z. Y.; Cheng, L. T. E.; Oon, L. L. E.; Tan, M. H.; Chan, K. S.; Hsu, L. Y.
Show abstract
BackgroundActive cases of COVID-19 has primarily been diagnosed via RT-PCR of nasopharyngeal (NP) swabs. Saliva and self-administered nasal (SN) swabs can be collected safely without trained staff. We aimed to test the sensitivity of "naso-oropharyngeal" saliva and SN swabs compared to NP swabs in a large cohort of migrant workers in Singapore. MethodsWe recruited 200 male adult subjects: 45 with acute respiratory infection, 104 asymptomatic close contacts, and 51 confirmed COVID-19 cases. Each subject underwent NP swab, SN swab and saliva collection for RT-PCR testing at 1 to 3 timepoints. We additionally used a direct-from-sample amplicon-based next-generation sequencing (NGS) workflow to establish phylogeny. ResultsOf 200 subjects, 91 and 46 completed second and third rounds of testing, respectively. Of 337 sets of tests, there were 150 (44.5%) positive NP swabs, 127 (37.7%) positive SN swabs, and 209 (62.0%) positive saliva. Test concordance between different sample sites was good, with a kappa statistic of 0.616 for NP and SN swabs, and 0.537 for NP and saliva. In confirmed symptomatic COVID-19 subjects, the likelihood of a positive test from any sample fell beyond 14 days of symptom onset. NGS was conducted on 18 SN and saliva samples, with phylogenetic analyses demonstrating lineages for all samples tested were Clade O (GISAID nomenclature) and lineage B.6 (PANGOLIN nomenclature). ConclusionThis study supports saliva as a sensitive and less intrusive sample for COVID-19 diagnosis and further delineates the role of oropharyngeal secretions in increasing the sensitivity of testing. However, SN swabs were inferior as an alternate sample type. Our study also provides evidence that a straightforward next-generation sequencing workflow can provide direct-from-sample phylogenetic analysis for public health decision-making.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Metagenomic sequencing to detect respiratory viruses in persons under investigation for COVID-19 94%
- Performance of Saliva, Oropharyngeal Swabs, and Nasal Swabs for SARS-CoV-2 Molecular Detection: A Systematic Review and Meta-analysis 93%
- Performance of saliva specimens for the molecular detection of SARS-CoV-2 in the community setting: does sample collection method matter? 93%
Similar papers in this journal
- Detection of SARS-CoV-2 infection in gargle, spit and sputum specimens 95%
- Investigating sensitivity of nasal or throat (ISNOT): A combination of both swabs increases sensitivity of SARS-CoV-2 rapid antigen tests 94%
- Evaluation of the INDICAID™ COVID-19 Rapid Antigen Test in symptomatic populations and asymptomatic community testing 94%
Similar papers in this journal
- Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection 93%
- Screening for SARS-CoV-2 in close contacts of individuals with confirmed infection: performance and operational considerations 93%
- Surface and air contamination with SARS-CoV-2 from hospitalized COVID-19 patients in Toronto, Canada 92%
Similar papers in this journal
- Evaluation of nasopharyngeal swab collection techniques for nucleic acid recovery and participant experience: recommendations for COVID-19 diagnostics 94%
- Respiratory Syncytial virus Strain Evolution and Mutations in Western Australia in the Context of Nirsevimab Prophylaxis 94%
- Impact of Nasopharyngeal Specimen Quality on SARS-CoV-2 Test Sensitivity 92%
Similar papers in this journal
- Comparison between mid-nasal swabs and buccal swabs for SARS-CoV-2 detection in mild COVID-19 patients 94%
- Evaluation of the Panbio™ rapid antigen test for SARS-CoV-2 in primary health care centers and test sites 93%
- SARS-CoV-2 detection by nasal strips: a superior tool for surveillance of pediatric populations 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.