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

Abstract

Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers' cutoffs established on the basis of 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 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-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 of 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 cutoffs. 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. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.

Keywords: COVID-19; India; SARS-CoV-2; coronavirus disease 2019; mixture models; seroprevalence; serosurveys; severe acute respiratory syndrome coronavirus 2.

Figure 1

Study locations and daily reported coronavirus disease 2019 (COVID-19) case counts in Chennai, India, from January 9 to May 13, 2021. A) Population density (gray inset) and locations of Greater Chennai Corporation sampling sites (red circles) (38). B) Daily reported numbers of COVID-19 cases within the serosurvey sampling window (shaded gray area).

Figure 2

Illustration of a mixture model’s distribution and fit to anti–spike (S) protein immunoglobulin G (IgG) (A) and anti–nucleocapsid (N) protein IgG (B) data, Chennai, India, January–May 2021. The columns represent the distribution of SARS-CoV-2 IgG levels in the data, and the dashed vertical lines represent the manufacturer’s cutoffs. The solid, dashed, and dotted curves represent the distribution of IgG values in the overall population and the seronegative and seropositive compartments, respectively, with shaded bands reflecting the 95% credible interval (CrI) for each distribution. In panel A, the gray column represents the proportion of samples that were below the lower limit of quantitation (LLOQ), while the black point (T-shaped bars, 95% CrI) represents the model-estimated probability of being below the LLOQ. In panel B, the thick solid curve represents the observed density function for anti-N IgG in the population. AU, arbitrary units; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Figure 3

Associations of sampling time and age group with log immunoglobulin G (IgG) levels among SARS-CoV-2–seropositive individuals, Chennai, India, January–May 2021. A) Anti–spike protein IgG; B) anti–nucleocapsid protein IgG. Circles show the change (Δ) in log IgG level; bars show the 95% credible interval (CrI). SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.