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Are Seroprevalence Estimates for Severe Acute Respiratory Syndrome Coronavirus 2 Biased?

Our take —

This perspective piece provides evidence that the true prevalence of antibody response against SARS-CoV-2 in the general population may be significantly underestimated, implying more people have been exposed to SARS-CoV-2 than current testing demonstrates due to sub-optimal serologic assay sensitivity in diagnostic testing. Assays require optimization to detect lower antibody titers from patients with mild infections or to account for significant waning of the immune response over time. Modeling assumptions made in this simulation study may not reflect the true test, transmission and immune response in the population.

Study design


Study population and setting

FDA-approved serologic assays used to monitor seroprevalence of antibody against SARS-CoV-2 often use samples from patients with severe symptoms and recent infections as positive controls in assay validation and optimization. Samples from these patients have high antibody levels compared to subjects with mild or no symptoms and subjects with less recent infections, where antibody levels are expected to decline over time. The use of these samples in assay validation may result in significant spectrum bias, which overestimates assay sensitivity in the general population leading to underestimates of the actual number of people who have been exposed to SARS-CoV-2. This modeling study aimed to quantify the amount of bias introduced in estimating SARS-CoV-2 seroprevalence in the general population due to potentially inappropriate assay sensitivity validation. The authors modeled the potential impacts of varying assay validation sample donor characteristics (proportion with differing symptom severities and/or sample collection time post-infection) on the sensitivity of measuring SARS-CoV-2 seroprevalence in simulations of the general population. Assay specificity was held constant. Simulated study populations were created by differing in the relative proportions of symptom severities (severe, mild, asymptomatic) as well as different times post infection for assaying seroprevalence.

Summary of Main Findings

These modeling analyses imply that current assays lack appropriate sensitivity and thus underestimate the true seroprevalence of antibodies against SARS-CoV-2 in the general population. Using a simulated model set where 95% of all infections were asymptomatic or mild and harbor lower antibody levels than assay validation samples, the authors demonstrated that the true assay sensitivity in the general population of current assays may be as low as 54%. Additionally, most samples used for assay validation are collected within 60 days of infection. Since evidence indicates antibody levels decline over time this also implies that as time since infection increases, the sensitivity of the assays will be reduced, further contributing to an underestimate of the seroprevalence in the general population.

Study Strengths

With respect to sensitivity analysis related to symptom severity, the composition of one simulated data set is 95% asymptomatic carriers and patients with mild symptoms, which aligns closely with the true clinical outcomes of SARS-CoV-2 infection.


Several assumptions were made in this analysis, including that assay sensitivity would be greatest for recent severe infections, as well as the transmission model and immune response kinetics, which may not completely represent the true assay, transmission and immune response in the population.

Value added

This modeling simulation provides evidence that serum samples from individuals more representative of the disease spectrum within the general population should be used in SARS-CoV-2 assay validation. The article also highlights the potential benefit of the creation of a reference serum bank for universal SARS-CoV-2 serologic assay validation.

This review was posted on: 20 November 2020