Antibody phenotypes and probabilistic seroprevalence estimates during the emergence 1 of SARS-CoV-2 in Sweden 2
Serological studies are critical for understanding pathogen-specific immune responses and 30 informing public health measures 1,2 . Here, we evaluate tandem IgM, IgG and IgA responses in 31 a cohort of individuals PCR+ for SARS-CoV-2 RNA ( n= 105) representing different categories 32 of disease severity, including mild and asymptomatic infections. All PCR+ individuals 33 surveyed were IgG-positive against the virus spike (S) glycoprotein. Elevated Ab levels were 34 associated with hospitalization, with IgA titers, increased circulating IL-6 and strong 35 neutralizing responses indicative of intensive care status. Additional studies of healthy blood 36 donors ( n =1,000) and pregnant women ( n =900), sampled weekly during the initial outbreak in 37 Stockholm, Sweden (weeks 14-25, 2020), demonstrated that anti-viral IgG titers differed over 38 1,000-fold between seroconverters, highlighting the need for careful evaluation of assay cut- 39 offs for individual measurements and accurate estimates of seroprevalence (SP). To provide a 40 solution to this, we developed probabilistic machine learning approaches to assign likelihood 41 of past infection without setting an assay cut-off, allowing for more quantitative individual and 42 population-level Ab measures. Using these tools, that considered responses against both S and 43 RBD, we report SARS-CoV-2 S-specific IgG in 6.8% of blood donors and pregnant women 44 two months after the peak of spring COVID-19 deaths, with the SP curve and country death 45 rate following similar trajectories. 46