Reliable estimation of SARS-CoV-2 anti-spike protein IgG titers from single dilution optical density values in serologic surveys

Background

As the COVID-19 pandemic evolves, there is a need for reliable and scalable seroepidemiology methods to estimate incidence, monitor the dynamics of population-level immunity, and guide mitigation and immunization policies. Our aim was to evaluate the reliability of normalized ELISA optical density (nOD) at a single dilution as a predictor of SARS-CoV-2 immunoglobulin titers derived from serial dilutions.

Methods

We conducted serial serological surveys of a community-based cohort from the city of Salvador, Brazil after two sequential COVID-19 epidemic waves. Anti-SARS-CoV-2 spike protein immunoglobulin G (anti-S IgG) ELISA (Euroimmun AG) was performed with serial 3-fold dilutions of sera from 54 of the 1101 cohort participants. We estimated interpolated ELISA titers, used parametric models to fit the relationship between nOD at a single 1:100 dilution and interpolated titers, and assessed the correlation between changes in nOD and changes in titers.

Results

The relationship between nOD at a single 1:100 dilution and interpolated titers fit a log-log curve, with a residual standard error of 0.304. We derived a conversion table of nOD to interpolated titer values. Additionally, there was a high correlation between changes in nOD and changes in interpolated titers between paired serial samples (r = 0.836, ρ = 0.873). Changes in nOD reliably predicted increases and decreases in titers, with 98.1% agreement (κ = 95.9%).

Conclusion

Single nOD measurements can reliably estimate SARS-CoV-2 antibody titers, significantly reducing time, labor, and resource needs when conducting large-scale serological surveys to ascertain population-level changes in exposure and immunity.

Highlights

• Optical density at a single dilution reliably estimates SARS-CoV-2 antibody titers

• Serial optical density measurements accurately identify changes in serostatus

• Using single optical density values can significantly reduce resource use in serosurveys