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Validating saliva as a biological sample for cost-effective, rapid and routine screening for SARS-CoV-2
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-22269889
ABSTRACT
PurposeCompared to nasopharyngeal/oropharyngeal swabs, non-invasive saliva samples have enormous potential for scalability and routine population screening of SARS-CoV-2. In this study, we are investigating the efficacy of saliva samples relative to nasopharyngeal/oropharyngeal swabs for use as a direct source for the RT-PCR based SARS-CoV-2 detection. MethodsPaired nasopharyngeal/oropharyngeal swabs and saliva samples were collected from suspected positive SARS-CoV-2 patients and tested using RT-PCR. Generalised linear models were used to investigate factors that explain result agreement. Further, we used simulations to evaluate the effectiveness of saliva-based screening in restricting the spread of infection in a large campus such as an educational institution. ResultsWe observed 75.4% overall result agreement. Prospective positive samples stored for three or more days showed a drastic reduction in the probability of result agreement. We observed 83% result agreement and 74.5% test sensitivity in samples processed and tested within two days of collection. Our simulations suggest that a test with 75% sensitivity, but high daily capacity can be very effective in limiting the size of infection clusters in a workspace. Guided by these results, we successfully implemented a saliva-based screening in the Bangalore Life Sciences Cluster (BLiSC) campus. ConclusionThese results suggest that saliva may be a viable sample source for SARS-CoV-2 surveillance if samples are processed immediately. We strongly recommend the implementation of saliva-based screening strategies for large workplaces and in schools, as well as for population-level screening and routine surveillance as we learn to live with the SARS-CoV-2 virus.
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Full text:
1
Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Diagnostic_studies
/
Experimental_studies
/
Observational_studies
/
Prognostic_studies
Language:
En
Year:
2022
Document type:
Preprint