Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: navigating the absence of a gold standard

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creativework.keywords - en
SARS CoV 2
Nucleocapsids
Glycoproteins
Virus testing
Enzyme-linked immunoassays
Antibodies
COVID 19
Blood donors
dc.contributor.author
Saeed, Sahar
O’Brien, Sheila F.
Abe, Kento
Yi, Qi-Long
Rathod, Bhavisha
Wang, Jenny
Fazel-Zarandi, Mahya
Tuite, Ashleigh
Fisman, David
Wood, Heidi
Colwill, Karen
Gingras, Anne-Claude
Drews, Steven J.
dc.date.accessioned
2024-01-11T16:27:44Z
dc.date.available
2024-01-11T16:27:44Z
dc.date.issued
2021-09-23
dc.description.abstract - en
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. METHODS: This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. RESULT: We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. DISCUSSION: Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.
dc.identifier.doi
https://doi.org/10.1371/journal.pone.0257743
dc.identifier.issn
1932-6203
dc.identifier.pubmedID
34555095
dc.identifier.uri
https://open-science.canada.ca/handle/123456789/1713
dc.language.iso
en
dc.publisher
PLoS ONE
dc.rights - en
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights - fr
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights.openaccesslevel - en
Gold
dc.rights.openaccesslevel - fr
Or
dc.rights.uri - en
https://creativecommons.org/licenses/by/4.0/
dc.rights.uri - fr
https://creativecommons.org/licenses/by/4.0/deed.fr
dc.subject - en
Health
dc.subject - fr
Santé
dc.subject.en - en
Health
dc.subject.fr - fr
Santé
dc.title - en
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: navigating the absence of a gold standard
dc.type - en
Article
dc.type - fr
Article
local.acceptedmanuscript.articlenum
e0257743
local.article.journalissue
9
local.article.journaltitle
PLoS ONE
local.article.journalvolume
16
local.pagination
1-13
local.peerreview - en
Yes
local.peerreview - fr
Oui
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