Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset
Abstract
Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3,214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in faeces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
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All data are available in Source data 1.
Article and author information
Author details
Funding
H2020 Marie Skłodowska-Curie Actions (707840)
- Benny Borremans
Defense Advanced Research Projects Agency (PREEMPT D18AC00031)
- Amandine Gamble
- James O Lloyd-Smith
UCLA AIDS Institute and Charity Treks
- Amandine Gamble
- James O Lloyd-Smith
National Science Foundation (DEB-1557022)
- K C Prager
- James O Lloyd-Smith
US Department of Defense Strategic Environmental Research and Development Program (RC‐2635)
- K C Prager
- James O Lloyd-Smith
Cooperative Ecosystem Studies Unit (Cooperative Agreement W9132T1920006)
- K C Prager
- James O Lloyd-Smith
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Borremans et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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