A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India

Abstract

Serological assays used to estimate SARS-CoV-2 seroprevalence often rely on manufacturer cut-offs established based on severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India from January to May, 2021. Samples were tested for SARS-CoV-2 IgG antibodies to the spike (S) and nucelocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cut-offs and using a mixture model based on measured IgG. Using manufacturer cut-offs, there was a five-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence 64.9% (95% Credible Interval [CrI], 63.8-66.0) and 51.5% (95% CrI, 50.2-52.9) respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cut-offs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. SARS-CoV-2 seroprevalence estimates using alternative targets must consider heterogeneity in seroresponse to ensure seroprevalence is not underestimated and correlates not misinterpreted.