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Preprint in English | medRxiv | ID: ppmedrxiv-21252532


Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Waning antibody levels lead to reduced sensitivity of serological diagnostic tests over time. This undermines the utility of serological surveillance as the SARS-CoV-2 pandemic progresses into its second year. Here we develop a multiplex serological test for measuring antibodies of three isotypes (IgG, IgM, IgA) to five SARS-CoV-2 antigens (Spike (S), receptor binding domain (RBD), Nucleocapsid (N), Spike subunit 2, Membrane-Envelope fusion) and the Spike proteins of four seasonal coronaviruses. We measure antibody responses in several cohorts of French and Irish hospitalized patients and healthcare workers followed for up to eleven months after symptom onset. The data are analysed with a mathematical model of antibody kinetics to quantify the duration of antibody responses accounting for inter-individual variation. One year after symptoms, we estimate that 36% (95% range: 11%, 94%) of anti-S IgG remains, 31% (9%, 89%) anti-RBD IgG remains, and 7% (1%, 31%) anti-N IgG remains. Antibodies of the IgM isotype waned more rapidly, with 9% (2%, 32%) anti-RBD IgM remaining after one year. Antibodies of the IgA isotype also waned rapidly, with 10% (3%, 38%) anti-RBD IgA remaining after one year. Quantitative measurements of antibody responses were used to train machine learning algorithms for classification of previous infection and estimation of time since infection. The resulting diagnostic test classified previous infections with 99% specificity and 98% (95% confidence interval: 94%, 99%) sensitivity, with no evidence for declining sensitivity over the time scale considered. The diagnostic test also provided accurate classification of time since infection into intervals of 0 - 3 months, 3 - 6 months, and 6 - 12 months. Finally, we present a computational method for serological reconstruction of past SARS-CoV-2 transmission using the data from this test when applied to samples from a single cross-sectional sero-prevalence survey.

Preprint in English | medRxiv | ID: ppmedrxiv-20219030


Although the COVID-19 pandemic peaked in March/April 2020 in France, the prevalence of infection is barely known. Herein, we assessed using high-throughput methods the serological response against the SARS-CoV-2 virus of 1847 participants working in one institution in Paris. In May-July 2020, 11% (95% CI: 9.7-12.6) of serums were positive for IgG against the SARS-CoV-2 N and S proteins and 9.5% (CI:8.2-11.0) were pseudo-neutralizer. The prevalence of immunization was 11.6% (CI:10.2-13.2) considering positivity in at least one assays. In 5% (CI:3.9-7.1) of RT-qPCR positive individuals, no systemic IgGs were detected. Among immune individuals, 21% had been asymptomatic. Anosmia and ageusia occurred in 52% of the IgG-positive individuals and in 3% of the negative ones. In contrast, 30% of the anosmia-ageusia cases were seronegative suggesting that the true prevalence of infection may reach 16.6%. In sera obtained 4-8 weeks after the first sampling anti-N and anti-S IgG titers and pseudo-neutralization activity declined by 31%, 17% and 53%, respectively with half-life of 35, 87 and 28 days, respectively. The population studied is representative of active workers in Paris. The short lifespan of the serological systemic responses suggests an underestimation the true prevalence of infection.

Preprint in English | medRxiv | ID: ppmedrxiv-20093963


BackgroundInfection with SARS-CoV-2 induces an antibody response targeting multiple antigens that changes over time. This complexity presents challenges and opportunities for serological diagnostics. MethodsA multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples from patients in French hospitals with RT-qPCR confirmed SARS-CoV-2 infection (n = 259), and negative control serum samples collected before the start of the SARS-CoV-2 epidemic (n = 335). A random forests algorithm was trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection. A mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the potential sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low transmission settings. ResultsIgG antibody responses to trimeric Spike protein identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 91.6% sensitivity (95% confidence interval (CI); 87.5%, 94.5%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% sensitivity (95% CI; 96.5%, 99.6%) and 99.3% specificity (95% CI; 97.6%, 99.8%). Informed by prior data from other coronaviruses, we estimate that one year following infection a monoplex assay with optimal anti-Stri IgG cutoff has 88.7% sensitivity (95% CI: 63.4%, 97.4%), and that a multiplex assay can increase sensitivity to 96.4% (95% CI: 80.9%, 100.0%). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 1%, below the false positivity rate of many other assays. ConclusionSerological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected greater than six months ago, and measuring seroprevalence in serological surveys in very low transmission settings.