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PCR data accurately predict infectious virus: a characterization of SARS-CoV-2 in non-human primates

Snedden, C. E.; Lloyd-Smith, J. O.

2023-06-23 microbiology
10.1101/2023.06.23.546114 bioRxiv
Show abstract

Researchers and clinicians often rely on molecular assays like PCR to identify and monitor viral infections instead of the resource-prohibitive gold standard of viral culture. However, it remains unclear when (if ever) PCR measurements of viral load are reliable indicators of replicating or infectious virus. Here, we compare total RNA, subgenomic RNA, and viral culture results from 24 studies of SARS-CoV-2 in non-human primates using bespoke statistical models. On out-of-sample data, our best models predict subgenomic RNA from total RNA with 91% accuracy, and they predict culture positivity with 85% accuracy. Total RNA and subgenomic RNA showed equivalent performance as predictors of culture positivity. Multiple cofactors, including exposure conditions and host traits, influence culture predictions for total RNA quantities spanning twelve orders of magnitude. Our model framework can be adapted to compare any assays, in any host species, and for any virus, to support laboratory analyses, medical decisions, and public health guidelines.

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"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.