Validation of a rapid, saliva-based, and ultra-sensitive SARS-CoV-2 screening system for pandemic-scale infection surveillance

Robert E. Dewhurst, Tatjana Heinrich, Paul Watt, Paul Ostergaard, Jose M. Marimon, Mariana Moreira, Philip E. Houldsworth, Jack D. Rudrum, David Wood, Sulev Kõks

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Without any realistic prospect of comprehensive global vaccine coverage and lasting immunity, control of pandemics such as COVID-19 will require implementation of large-scale, rapid identification and isolation of infectious individuals to limit further transmission. Here, we describe an automated, high-throughput integrated screening platform, incorporating saliva-based loop-mediated isothermal amplification (LAMP) technology, that is designed for population-scale sensitive detection of infectious carriers of SARS-CoV-2 RNA. Central to this surveillance system is the “Sentinel” testing instrument, which is capable of reporting results within 25 min of saliva sample collection with a throughput of up to 3840 results per hour. It incorporates continuous flow loading of samples at random intervals to cost-effectively adjust for fluctuations in testing demand. Independent validation of our saliva-based RT-LAMP technology on an automated LAMP instrument coined the “Sentinel”, found 98.7% sensitivity, 97.6% specificity, and 98% accuracy against a RT-PCR comparator assay, confirming its suitability for surveillance screening. This Sentinel surveillance system offers a feasible and scalable approach to complement vaccination, to curb the spread of COVID-19 variants, and control future pandemics to save lives.

Original languageEnglish
Article number5936
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - Dec 2022

Fingerprint

Dive into the research topics of 'Validation of a rapid, saliva-based, and ultra-sensitive SARS-CoV-2 screening system for pandemic-scale infection surveillance'. Together they form a unique fingerprint.

Cite this