PLoS One. 2021 Sep 23; 16 (9): e0257743. doi: 10.1371 / journal.pone.0257743. Electronic collection 2021.

ABSTRACT

BACKGROUND: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) seroprevalence studies fill the void left by case detection, to estimate the true burden of the COVID-19 pandemic. Although several anti-SARS-CoV-2 immunoassays are available, there is no gold standard.

METHODS: This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April and September 2020. Each sample was tested by four tests: Abbott SARS-Cov-2 IgG test, targeting the nucleocapsid (Abbott-NP) and three internal IgG ELISA assays (targeting spike glycoprotein, receptor binding domain and core). Seroprevalence rates were compared using several composite benchmarks and a series of Bayesian latent class models.

RESULT: We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive on all tests. None of the individual tests resulted in a monotonic increase in seroprevalence over time. In contrast, using the results of all tests, the Bayesian latent class model with informative priors predicts the seroprevalence of 0.7% (95% credibility interval (95% ICr); 0.4, 1 , 0%) in April / May at 0.7% (95% CrI 0.5, 1.1%) in June / July at 0.9% (95% CrI 0.5, 1.3) in August / September. The characteristics of the assay have varied over time. Overall, Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP test increased 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 a 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 declining IgG antibody signals.

PMID: 34555095 | DOI: 10.1371 / journal.pone.0257743