COVID-19 antibody seroprevalence in Santa Clara County, California

Abstract

Background: Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

Methods: On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

Results: The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

Conclusion: The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Keywords: COVID-19; infection fatality rate; seroprevalence.

Figure 1

Flow diagram of participants who filled out the survey and registered, visited a site for testing and were associated with a tested specimen. We were able to associate 3328 individuals with complete survey, site and lab-result data. ‘Individuals with completed registrations’ refers to individuals who completed the initial online survey and were able to select a test site and time. ‘No-shows’ refers to participants who filled out the survey and obtained a site registration but for whom we do not have a record of attendance onsite. ‘Unverifiable IDs’ refers to records from the site data with duplicate participant identifications (IDs) for which we cannot verify which individual attended the site (this may be due to participants bringing incorrect IDs and/or technical errors in the REDCap ID assignment process). ‘Samples not collected or not tested’ includes at least 10 individuals who visited the site but did not consent to participate, as well as several children who may have decided not to have their fingers pricked after completing intake onsite. This also includes specimens that were lost before they could be tested in the lab. ‘Unusable or unmatched survey data’ includes individuals with invalid zip codes, participant IDs from the lab results that could not be matched back to the survey responses and participants who withdrew from the study. Unmatched participant IDs may be due to participants stating an incorrect participant ID at the test site or site data collectors incorrectly recording stated participant IDs. ‘Invalid lab result’ refers to one test for which the on-board control failed and the lab result could not be correctly interpreted.