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A Multiplexed, Next Generation Sequencing Platform for High-Throughput Detection of SARS-CoV-2 (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.10.15.20212712
ABSTRACT
Population scale sweeps of viral pathogens, such as SARS-CoV-2, that incorporate large numbers of asymptomatic or mild symptom patients present unique challenges for public health agencies trying to manage both travel and local spread. Physical distancing is the current major strategy to suppress spread of the disease, but with enormous socio-economic costs. However, modelling and studies in isolated jurisdictions suggest that active population surveillance through systematic molecular diagnostics, combined with contact tracing and focused quarantining can significantly suppress disease spread and has significantly impacted disease transmission rates, the number of infected people, and prevented saturation of the healthcare system. However, reliable systems allowing for parallel testing of 10-100,000s of patients in larger urban environments have not yet been employed. Here we describe COVID-19 screening using Systematic Parallel Analysis of RNA coupled to Sequencing (C19-SPAR-Seq), a scalable, multiplexed, readily automated next generation sequencing (NGS) platform that is capable of analyzing tens of thousands of COVID-19 patient samples in a single instrument run. To address the strict requirements in clinical diagnostics for control of assay parameters and output, we employed a control-based Precision-Recall and predictive Receiver Operator Characteristics (coPR) analysis to assign run-specific quality control metrics. C19-SPAR-Seq coupled to coPR on a trial cohort of over 600 patients performed with a specificity of 100% and sensitivity of 91% on samples with low viral loads and a sensitivity of > 95% on high viral loads associated with disease onset and peak transmissibility. Our study thus establishes the feasibility of employing C19-SPAR-Seq for the large-scale monitoring of SARS-CoV-2 and other pathogens.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
/
Hallucinations
Language:
English
Year:
2020
Document Type:
Preprint
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