Disease-associated antibody phenotypes and probabilistic seroprevalence estimates during the emergence of SARS-CoV-2
Serological studies are critical for understanding pathogen-specific immune responses and informing public health measures (1,2). By developing highly sensitive and specific trimeric spike (S)-based antibody tests, we report IgM, IgG and IgA responses to SARS-CoV-2 in COVID-19 patients (n=105) representing different categories of disease severity. All patients surveyed were IgG positive against S. Elevated anti-SARS-CoV-2 antibody levels were associated with hospitalization, with IgA titers, increased circulating IL-6 and strong neutralizing responses indicative of intensive care status. Antibody-positive blood donors and pregnant women sampled during the pandemic in Stockholm, Sweden (weeks 14-25), displayed on average lower titers and weaker neutralizing responses compared to patients; however, inter-individual anti-viral IgG titers differed up to 1,000-fold. To provide more accurate estimates of seroprevalence, given the frequency of weak responders and the limitations associated with the dichotomization of a continuous variable (3,4), we used a Bayesian approach to assign likelihood of past infection without setting an assay cut-off. Analysis of blood donors (n=1,000) and pregnant women (n=900) sampled weekly demonstrated SARS-CoV-2-specific IgG in 7.2% (95% Bayesian CI [5.1-9.5]) of individuals two months after the peak of spring 2020 COVID-19 deaths. Seroprevalence in these otherwise healthy cohorts increased steeply before beginning to level-off, following the same trajectory as the Stockholm region deaths over this time period.