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Figure 1.  Estimates of Seroprevalence to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies and Timeline of Specimen Collection
Estimates of Seroprevalence to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies and Timeline of Specimen Collection

A, Estimates are shown with 95% CIs for 10 geographic sites from which residual clinical specimens were collected. Seroprevalence estimate is shown at the midpoint of the specimen collection date range. B, Timeline with specimen collection dates for each site.

Figure 2.  Strata-Specific Estimates of Seroprevalence to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies in 10 Geographic Sites
Strata-Specific Estimates of Seroprevalence to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies in 10 Geographic Sites

A, Estimates of seroprevalence to SARS-CoV-2 antibodies by sex, from highest to lowest overall seroprevalence. B, Strata-specific estimates of seroprevalence to SARS-CoV-2 antibodies by age group, from highest to lowest overall seroprevalence.

Table 1.  Number of Residual Clinical Specimens From Commercial Laboratories Tested for Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 in 10 Geographic Sites, by Sex, Age Group, and Location, With Dates of Specimen Collection for Each Site
Number of Residual Clinical Specimens From Commercial Laboratories Tested for Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 in 10 Geographic Sites, by Sex, Age Group, and Location, With Dates of Specimen Collection for Each Site
Table 2.  Sex-Specific, Age-Specific, and Age- and Sex-Standardized Seroprevalence Estimates, Adjusted for Assay Performance Characteristics, for Specimens Collected in 10 Geographic Sitesa,b
Sex-Specific, Age-Specific, and Age- and Sex-Standardized Seroprevalence Estimates, Adjusted for Assay Performance Characteristics, for Specimens Collected in 10 Geographic Sitesa,b
Table 3.  Estimated Number of Infections Based on Seroprevalence Estimates and Comparison With the Number of Reported Cases as of the Last Date of Specimen Collection for 10 Sites
Estimated Number of Infections Based on Seroprevalence Estimates and Comparison With the Number of Reported Cases as of the Last Date of Specimen Collection for 10 Sites
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    4 Comments for this article
    EXPAND ALL
    silver lining: death rates from Covid-19 largely affected by estimated infections
    Withania Somnifera, PhD | University
    If all assumptions, estimates and other limitations of this study are accepted, then if the extent of infection is about 10x than reported, this would mean that the death rate from Covid-19 is about 10x smaller than reported. This would appear to make this pandemic less lethal than reported in the media including the JH database.
    CONFLICT OF INTEREST: None Reported
    Unbiased Sampling for Prevalence/Incidence
    Tor Tosteson, ScD | Dartmouth
    The population incidence rate of COVID-19 is clearly a key parameter for predicting and managing the pandemic. However, this article uses data subject to selection bias to estimate such rated. Random samples may overcome these.

    Sample surveys such as the CDC National Health Interview Survey may offer an mechanism for doing this. Response bias may be encountered as well, but it should be at least attempted.
    CONFLICT OF INTEREST: None Reported
    Estimates of case fatality rates from study data
    DAVID KELLER, MD, MS | Internal Medicine
    Case fatality rate (CFR), defined as the number of deaths due to an infectious disease divided by the number of cases of the disease, is of great interest for estimating an infected individual's unadjusted risk of death from the disease, and for comparison with the unadjusted risk of death from other diseases (e.g., influenza).  CFR can only be calculated using final data, collected after all the cases have been resolved (either died or recovered).  The number of cases of the disease in a population is best calculated using the seroprevalence of convalescent antibodies.

    This study found the number of
    cases of COVID-19, as calculated from seroprevalence data, was 6 to 24 times larger than the number calculated from reported results of direct disease testing.  If accurate, then the true CFR for COVID-19 is 6 to 24 times smaller than the CFR derived from the reported number of cases from direct disease testing.  If the CFR is indeed an order of magnitude smaller than previously suspected, then the cost-to-benefit ratio of mitigation efforts is an order of magnitude higher than previously suspected.

    I divided the number of infections estimated for each of the 4 complete states examined in this study by the current total number of deaths reported for these states by CDC, to calculate an updated rough estimate of the CFR for each state.  The results were CFRs of 0.5%, 0.7%, 1.3% and 2.5% for UT, MO, LA and CT respectively.  Note that CT is one of a handful of states which practiced widespread transfer of patients with active COVID-19 infections into nursing homes.  Other differences between the calculated CFRs of these states may be due to differences in age or other population factors which determine the severity of COVID-19 disease, or differences in severity between local strains of the virus.  

    Ongoing calculations of CFR, based on increasingly accurate data, should improve over time, and converge for similar populations and virus strains.  

    CONFLICT OF INTEREST: None Reported
    READ MORE
    Differences between CDC and Santa Clara Seroprevalence Data
    Eran Bendavid, MD MS | Stanford University
    We are impressed with this large-scale seroprevalence study [1] and concur with the primary findings that "the number of infections was much greater than the number of reported cases." This is similar to our conclusion from the Santa Clara Seroprevalence Study [2]. The authors mention our study and note that in our study "seroprevalence estimates were largely driven by estimates of test performance characteristics."
    The Santa Clara seroprevalence estimates noted by the authors (2.5%-4.2%) are outdated, and were revised.[2] Our best seroprevalence estimate is 2.8% (95CI 1.3-4.7%), and not driven by test performance characteristics. In our revision we established the
    test sensitivity and specificity with precision [2]. 
    Part of the difference between our and the CDC estimates for the Bay area may reflect chance, as confidence intervals overlap. However, several other explanations may be more relevant. First, the CDC study obtained samples from commercial laboratories drawing from the greater Bay area. In comparison to the rest of the Bay area, Santa Clara County had the earliest identified case, and was recognized for early viral spread, plausibly leading to higher prevalence [3]. 
    Second, the distribution of samples in the CDC study appears skewed towards Contra Costa County (Appendix eFigure 1H), with many samples from the zip codes surrounding Danville and Walnut Creek, relatively affluent cities with low representation of Hispanic populations where we identified the highest prevalence. Another study done on April 25-April 28 on an area of San Francisco with predominantly Hispanic population found a seroprevalence of 6.1% [4]. Third, minority populations are at higher risk of infection, but may be less likely to seek health care and lab testing and thus sampled.
    Fourth, there is increasing evidence that many people fail to develop detectable antibodies after infection, and that, among those with antibodies, the titers decrease briskly. This may explain paradoxically decreasing seroprevalence in the same location over time [5]. This pattern was also seen in the authors’ data for Utah and Minnesota (www.cdc.gov/coronavirus/2019-ncov/cases-updates/commercial-lab-surveys.html). Finally, the authors quote our weighted prevalence estimates, with weights chosen to match county demographics by race, age, sex, and zip-code distribution. Our unweighted estimate - analogous to the study’s data - was 1.5% (weighting the authors’ data to represent regional demographics may have increased their estimate).
    We support the use of seroprevalence estimates for assessing epidemic stage. The differences above are noted in the spirit of enthusiasm for improving future seroprevalence studies.

    Eran Bendavid, John Ioannidis, Jay Bhattacharya, Neeraj Sood

    1. Havers et al. Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020. JAMA Intern Med. Published online July 21, 2020.
    2. Bendavid E et al. COVID-19 Antibody Seroprevalence in Santa Clara County, California. medRxiv. doi:10.1101/2020.04.14.20062463
    3. Feuer BLJ Will. CDC officials confirm 7th US case of coronavirus, in California man who traveled to China. CNBC. Accessed July 22, 2020. https://www.cnbc.com/2020/01/31/california-and-cdc-officials-confirm-7th-case-of-coronavirus-in-the-us.html
    4. Chamie G et al. SARS-CoV-2 Community Transmission During Shelter-in-Place in San Francisco. medRxiv. doi:10.1101/2020.06.15.20132233
    5. Ioannidis J. The infection fatality rate of COVID-19 inferred from seroprevalence data. medRxiv. doi:10.1101/2020.05.13.20101253
    CONFLICT OF INTEREST: We are authors of a study referenced in the paper on which we comment
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    Original Investigation
    July 21, 2020

    Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020

    Author Affiliations
    • 1CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia
    • 2Florida Department of Health, Tallahassee
    • 3New York State Department of Health, Albany
    • 4Division of State and Local Readiness, Centers for Disease Control and Prevention, Atlanta, Georgia
    • 5Utah Department of Health, Salt Lake City
    • 6Louisiana Department of Health, New Orleans
    • 7California Department of Health, Richmond
    • 8Washington State Department of Health, Tumwater
    • 9Minnesota Department of Health, St Paul
    • 10Florida Department of Health, Tallahassee
    • 11Louisiana Department of Health, New Orleans
    • 12Connecticut Department of Public Health, Hartford
    • 13Missouri Department of Health and Senior Services, Jefferson City
    • 14Pennsylvania Department of Health, Harrisburg
    • 15Washington State Department of Health, Tumwater
    • 16Missouri Department of Health and Senior Services, Jefferson City
    • 17Minnesota Department of Health, St Paul
    JAMA Intern Med. Published online July 21, 2020. doi:10.1001/jamainternmed.2020.4130
    Key Points

    Question  What proportion of persons in 10 US sites had detectable antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from March 23 to May 12, 2020?

    Findings  In this cross-sectional study of 16 025 residual clinical specimens, estimates of the proportion of persons with detectable SARS-CoV-2 antibodies ranged from 1.0% in the San Francisco Bay area (collected April 23-27) to 6.9% of persons in New York City (collected March 23-April 1). Six to 24 times more infections were estimated per site with seroprevalence than with coronavirus disease 2019 (COVID-19) case report data.

    Meaning  For most sites, it is likely that greater than 10 times more SARS-CoV-2 infections occurred than the number of reported COVID-19 cases; most persons in each site, however, likely had no detectable SARS-CoV-2 antibodies.

    Abstract

    Importance  Reported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection likely underestimate the prevalence of infection in affected communities. Large-scale seroprevalence studies provide better estimates of the proportion of the population previously infected.

    Objective  To estimate prevalence of SARS-CoV-2 antibodies in convenience samples from several geographic sites in the US.

    Design, Setting, and Participants  This cross-sectional study performed serologic testing on a convenience sample of residual sera obtained from persons of all ages. The serum was collected from March 23 through May 12, 2020, for routine clinical testing by 2 commercial laboratory companies. Sites of collection were San Francisco Bay area, California; Connecticut; south Florida; Louisiana; Minneapolis-St Paul-St Cloud metro area, Minnesota; Missouri; New York City metro area, New York; Philadelphia metro area, Pennsylvania; Utah; and western Washington State.

    Exposures  Infection with SARS-CoV-2.

    Main Outcomes and Measures  The presence of antibodies to SARS-CoV-2 spike protein was estimated using an enzyme-linked immunosorbent assay, and estimates were standardized to the site populations by age and sex. Estimates were adjusted for test performance characteristics (96.0% sensitivity and 99.3% specificity). The number of infections in each site was estimated by extrapolating seroprevalence to site populations; estimated infections were compared with the number of reported coronavirus disease 2019 (COVID-19) cases as of last specimen collection date.

    Results  Serum samples were tested from 16 025 persons, 8853 (55.2%) of whom were women; 1205 (7.5%) were 18 years or younger and 5845 (36.2%) were 65 years or older. Most specimens from each site had no evidence of antibodies to SARS-CoV-2. Adjusted estimates of the proportion of persons seroreactive to the SARS-CoV-2 spike protein antibodies ranged from 1.0% in the San Francisco Bay area (collected April 23-27) to 6.9% of persons in New York City (collected March 23-April 1). The estimated number of infections ranged from 6 to 24 times the number of reported cases; for 7 sites (Connecticut, Florida, Louisiana, Missouri, New York City metro area, Utah, and western Washington State), an estimated greater than 10 times more SARS-CoV-2 infections occurred than the number of reported cases.

    Conclusions and Relevance  During March to early May 2020, most persons in 10 diverse geographic sites in the US had not been infected with SARS-CoV-2 virus. The estimated number of infections, however, was much greater than the number of reported cases in all sites. The findings may reflect the number of persons who had mild or no illness or who did not seek medical care or undergo testing but who still may have contributed to ongoing virus transmission in the population.

    Introduction

    The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the US was reported in Washington State on January 20, 2020. The first US case linked to community transmission was reported in California on February 26, 2020, followed by subsequent cases resulting from community transmission reported in Washington on February 28 and New York on March 3.1-5 Since January 2020, states have been recommended to report all laboratory-confirmed cases to the Centers for Disease Control and Prevention (CDC).6 Reported cases, however, likely represent only a fraction of SARS-CoV-2 infections, as an unknown proportion of cases are mild or asymptomatic, or they are otherwise not diagnosed or ascertained through passive public health reporting.7-9 Furthermore, viral testing has been limited in many sites and was often reserved for severely ill patients early in the US outbreak, and testing availability has changed rapidly. Each of these issues could confound estimates of incident cases and epidemic dynamics that use only case-based reporting data.

    Detection of antibodies to SARS-CoV-2 in a person’s blood likely indicates that they were infected at some point since the start of the pandemic. Thus, serologic assays can be used to provide population-based estimates of infection that include people who had mild or asymptomatic infection or who were never tested despite having symptoms.

    We used convenience samples of residual clinical specimens obtained from 2 commercial diagnostic laboratories to conduct a serologic survey. Our goal was to estimate the seroprevalence in the population—that is, the proportion of the population with evidence of previous infection with SARS-CoV-2, by age group, in 10 geographically diverse US sites with known community transmission.

    Methods

    We obtained convenience samples of deidentified residual patient sera collected for routine screening (eg, cholesterol screening) or clinical management by 2 commercial clinical laboratories (Lab A and Lab B) from 10 sites. For Lab A, data on the breakdown between inpatients and outpatients were not available. For Lab B, almost all the samples were from outpatients. The samples were collected during discrete periods from March 23 through May 12 (Table 1; Figure 1). Sites and dates of collection included western Washington State (WA) (defined broadly; March 23-April 1); the New York City metro area (NY) (defined broadly; predominantly Manhattan, Bronx, Brooklyn, Queens, and Nassau counties; March 23-April 1); south Florida (FL) (restricted to Miami-Dade, Broward, Palm Beach, and Martin counties; April 6-10); Philadelphia metro statistical area counties and Lancaster and Cumberland counties (PA) (April 13-25); San Francisco Bay area, including San Jose (CA) (April 23-27); Minneapolis-St Paul-St Cloud combined statistical areas (MN) (April 30-May 12); and all of Missouri (MO) (April 20-26), Utah (UT) (April 20-May 3), Connecticut (CT) (April 26-May 3), and Louisiana (LA) (April 1-8) (eFigure 1 in the Supplement). Age or age group, patient sex, and collection date were available for all specimens; we aimed to have at least 300 specimens per age group. Specimens from all sites were deduplicated through laboratory records, except for specimens provided by Lab B from WA and NY. Both laboratories provided specimens from NY and WA; Lab A also provided specimens from CA, FL, and LA, and Lab B provided specimens from CT, MO, PA, MN, and UT. The zip code of patient residence was known for all Lab A specimens and for Lab B specimens from CT, MO, PA, MN, and UT, but not for Lab B specimens from NY and WA. Based on information from Lab B, which indicated that the majority of specimens from its facilities in WA and NY were drawn from the areas of greatest population density in western WA and NY metro areas, respectively, we assumed that Lab B specimens from WA and NY were from a similar geographic distribution to those received from Lab A for those sites. For individual specimens, no information on the reason for specimen collection was available.

    After reviewing the protocol, CDC human subjects research officials determined that the testing represented nonresearch activity in the setting of a public health response to the coronavirus disease 2019 (COVID-19) pandemic and exempted it from further review. Informed consent was waived as deidentified data were used. On June 26, 2020, data from 6 sites (CT, FL, MO, NY, UT, and WA) were released on CDC’s website,18 and an early version of the non–peer-reviewed manuscript was posted on a preprint server.19

    Laboratory Methods

    Sera were tested at CDC in a 2-step process—a screening assay followed by a confirmatory assay for presumptive reactive specimens identified through screening. The CDC developed and validated an enzyme-linked immunosorbent assay (ELISA) that was used as the confirmatory assay, as has been previously described.20 A specimen was considered reactive if, on confirmatory testing, at a background corrected optical density of 0.4 and at a serum dilution of 1:100, it had a signal to threshold ratio greater than 1. The screening assay was similar to the confirmatory assay. Sera were screened at a 1:100 dilution using a qualitative pan immunoglobulin (Ig) ELISA against the prefusion stabilized ectodomain of the SARS-CoV-2 spike protein.21 However, a greater coating concentration of spike protein was used, only 1 dilution was tested for each serum sample (1:100), and different optical density cutoffs were first used to identify presumptive reactive specimens, which were then referred for confirmatory testing.20 Using the above definition of reactivity, specificity was 99.3% (95% CI, 98.3%-99.9%) and sensitivity was 96.0% (95% CI, 90.0%-98.9%).20 Results of testing against sera from polymerase chain reaction–confirmed infections with other coronaviruses indicate that antibodies to commonly circulating human coronaviruses exhibited some cross-reactivity, but the level of cross-reactivity was below the limits of detection for this assay.20

    Analysis

    We calculated seroprevalence as the proportion of specimens that were confirmed reactive, stratified by sex and age group (0 to ≤18 years, 19 to ≤49 years, 50 to ≤64 years, and ≥65 years). We calculated age-standardized and sex-standardized seroprevalence estimates using weights derived from US census county-level population projections for the most sampled counties for CA, FL, MN, NY, PA, and WA, and from US census state-level data for CT, LA, MO, and UT. We estimated 95% CIs by generating 10 000 bootstrapped samples with replacement (eMethods in the Supplement). We conducted additional analyses with bootstrapping to account for assay test performance, using sensitivity and specificity parameters described above. We defined estimates that were age-standardized and sex-standardized and adjusted for test characteristics as fully adjusted estimates. To assess for potential differences in populations using different laboratories, we compared seroprevalence in specimens from Lab A with those from Lab B for NY and WA, the 2 sites where both laboratories collected specimens.

    To estimate the degree of underascertainment of reported cases for all sites, we assumed that the presence of SARS-CoV-2 antibodies represented infections that occurred prior to the last date of specimen collection. We applied the estimated age-adjusted and sex-adjusted seroprevalence estimates to the respective populations to estimate total infections. We then divided these numbers by the cumulative case counts reported to health departments22 as of the last date of specimen collection for each site. Because antibodies may take an average of 10 to 14 days to be detectable after infection,23-25 and collection periods were 6 to 14 days in length, we accounted for a lag in the development of antibodies in a scenario analysis using the cumulative number of reported cases as of 7 days prior to the start of specimen collection. The specimen collection period in relationship to reported cases is shown for each site in eFigure 2 in the Supplement, and the number of cases reported daily in the US is shown in eFigure 3 in the Supplement.

    R (version 3.6.1) and Rstudio (version 1.2.1335) (R Foundation for Statistical Computing) were used to perform statistical analyses. Two-sided P values less than .05 were considered statistically significant.

    Results

    We tested 16 025 residual sera specimens from 10 sites collected from March 23 through May 12, with discrete collection periods for each site (Table 1). A total of 6320 (39.4%) specimens were from Lab A, and 9705 (60.6%) specimens were from Lab B. Of all specimens, 8853 (55.2%) were from women. The age group of 0-18 years comprised the smallest number of specimens (n = 1205, 7.5%), with the age group of 65 years and older comprising the largest number (n = 5845, 36.5%). Laboratory catchment areas as determined by the number of specimens were predominantly major cities and their metro areas, including some suburban or exurban counties for CA, FL, MN, NY, PA, and WA. Laboratories receiving specimens from the entire state (CT, LA, MO, and UT) received specimens from areas in numbers approximately proportionate to state population density (eFigure 1 in the Supplement).

    Table 2 shows the seroprevalence estimates by sex and age as well as fully adjusted estimates. Seroprevalence ranged from 1.0% (95% CI, 0.3%-2.4%) in CA to 6.9% (95% CI, 5.0%-8.9%) in NY. Seroprevalence estimates fell within this range for the remaining 8 sites. There was no clear association between seroprevalence by age and sex across sites (Figure 2). In NY, there was a significant difference in fully adjusted seroprevalence between specimens obtained from Lab A (11.5%) and Lab B (5.7%) (P < .01). In WA, there was no difference in fully adjusted seroprevalence between specimens obtained from Lab A or Lab B (1.9% vs 1.5%; P = .47) (eTable 1 in the Supplement).

    Table 3 shows estimates of the number of SARS-CoV-2 infections suggested by seroprevalence estimates in each site and compares these with the number of reported cases as of the last date of specimen collection (eFigure 2 in the Supplement). Our estimate for underascertainment was lowest in CT, where the estimation of 176 012 infections was 6.0 (range, 4.3-7.8) times greater than the 29 287 reported cases as of May 3, 2020, and highest for MO, where the estimation of 161 936 infections was 23.8 (range, 14.8-34.7) times greater than the 6794 reported cases as of April 25, 2020. Estimated numbers of infections for 7 sites—CT, FL, LA, MO, NY, UT, and WA—were at least 10 times greater than the number of reported cases.

    Estimates of underascertainment using the date 7 days prior to start of specimen collection are shown in eTable 2 in the Supplement. Using these earlier dates, our point estimate for underascertainment was lowest in CT, where the number of estimated infections was 8.9 times greater than the number of cases reported as of April 19, 2020, and highest in NY, where the estimation of 641 778 infections was more than 1000 times greater than the 545 cases reported as of March 16, 2020. These estimates do not account for delays in reporting results, which may have been longer earlier in the pandemic.

    Discussion

    Our study estimated seroprevalence of antibodies to SARS-CoV-2 in 10 diverse geographic sites in the US, with discrete collection periods from late March through mid-May 2020. Seroprevalence estimates varied from 1.0% in the San Francisco Bay area in late April to 6.9% in the New York City metro area in late March. Our results for each site suggest that the number of infections was much greater than the number of reported cases throughout the study period; these infections likely include asymptomatic and mild infections for which health care was not sought, as well as symptomatic infections in persons who either did not seek care or in whom SARS-CoV-2 viral testing was not performed. It is possible that false-positive ELISA results could lead us to overestimate seroprevalence and infections. The estimates are the first reported from these 10 sites, from which specimens are to be collected at a variety of time points.26

    The results of several US seroprevalence studies have been released, including those conducted in Santa Clara County (California), Idaho, Los Angeles (California), and New York.27-31 As of early July 2020, 3 of these 4 studies had only been posted as preprints without peer review.27-30 Studies have used different assays and participant selection methods. The Santa Clara County study, conducted April 3 and 4, 2020, approximately 5 weeks after the first case of community transmission of COVID-19 was detected in the San Francisco Bay area, estimated a seroprevalence rate of 2.5% to 4.2%.27 The authors noted that seroprevalence estimates were largely driven by estimates of test performance characteristics, which is to be expected, particularly in a low-prevalence setting. A study in New York City conducted between late February and mid-April 2020 showed seroprevalence estimates of 2.2% and 10.1% for the weeks ending March 29 and April 5, respectively30; the NY specimen collection period in our study was March 23 to April 1. Another study in New York state used sera collected from April 19 to April 28, approximately 8 weeks after community transmission was first identified in New York City. A seroprevalence of 22.7% was estimated.31 The higher seroprevalence found in this study compared with our results may reflect a different study population and specimen collection occurring several weeks later, during a period when SARS-CoV-2 was circulating widely in New York City.

    At present, the relationship between detectable antibodies to SARS-CoV-2 and protective immunity against future infection is not known.25 Extrapolating these estimates to make assumptions about population immunity should not be done until more is known about the correlations between the presence, titer, and duration of antibodies and protection against this novel, emerging disease.

    The timing of the development of SARS-CoV-2–specific antibodies is variable; it is unknown when infection occurred for individuals in this study. Although humoral response kinetics to SARS-CoV-2 infection are not well understood, reactive IgA, IgM, and IgG antibodies have been detected as soon as 1 day after symptom onset.32 In other studies, neutralizing antibodies were detected 10 to 15 days after symptom onset; the median time to development of total antibody, IgM, and IgG has been estimated as 11, 12, and 14 days, respectively.23,25

    We compared the number of estimated cases in the population based on our seroprevalence estimates with the reported cases as of the last day of specimen collection. From this comparison, we estimated that there were from 6 times as many SARS-CoV-2 infections as reported cases in CT to 24 times the number of infections as reported cases in MO. Specimen collection in CT started later than other sites, and lower underascertainment estimates for CT may reflect increasing availability of testing as the pandemic progressed. These estimates of underascertainment are conservative; they would be higher if an earlier date had been used to take into account infected persons who had not yet developed detectable antibodies at the time of specimen collection. Our seroprevalence estimates are more likely to reflect infections that occurred a minimum of 1 to 2 weeks prior to the specimen collection.

    Limitations

    Our study has limitations that are associated with both the samples and with the tests used. The specimens were collected for clinical purposes from persons seeking health care and were shared with the CDC with minimal accompanying data. No data on recent symptomatic illness, underlying conditions, or possible COVID-19 exposures were available. It is possible that specimens were drawn from patients seeking care for suspected COVID-19 symptoms, potentially biasing results, particularly in settings such as NY where disease incidence was higher. Lab B sampled sera from metabolic panels taken at routine outpatient visits; Lab A sampled randomly with respect to clinical test type and admission status. Residual clinical specimens from screening or routine care are more likely to come from persons who require monitoring for chronic medical conditions despite the ongoing pandemic. These persons may not be representative of the general population, including in their health care seeking and social distancing behavior, immune response to infection, and disease exposure risk. Representativeness may vary by age group as well. Therefore, our seroprevalence estimates should be confirmed and extended by other studies, including serosurveys that use targeted sampling frames to enroll more representative populations.33 For Lab B samples from NY and WA, it is possible that more than 1 specimen was from the same individual, as samples were not deduplicated. Given the large numbers of specimens from each site and that the potential for duplication should be unbiased with respect to SARS-CoV-2 infection, the influence on seroprevalence estimates is likely minimal. In addition, although the overall sample size was large, in some sites, there were few specimens from persons 18 years or younger, which limited our ability to estimate seroprevalence among children. Furthermore, at this stage in the pandemic, infections may not be evenly distributed even within these geographic sites. Thus, seroprevalence estimates for large geographic sites may not be accurate if the majority of samples come from specific areas with higher infection rates. We also had limited geographic data on a subset of specimens from Lab B for NY and WA, which may have been drawn from a larger geographic site than those from Lab A with zip code–level data. The inclusion of some specimens from other sites in WA and NY states, especially sites of lower seroprevalence around NY, may lead to inaccurate seroprevalence estimates for these areas and may explain the differences in the seroprevalence estimates between Lab A and Lab B for NY. Finally, the representation of specific geographic pockets may not be the same between the 2 commercial laboratories, and underlying patient populations may differ between the laboratories; therefore, combining results from Lab A and Lab B is problematic. Follow-up serosurveys will include zip code data for all specimens.

    It is possible that the ELISA may exhibit cross-reactivity with antibodies to other common human coronaviruses; therefore, some results may represent a false-positive result for SARS-CoV-2, potentially leading to overestimation of the actual seroprevalence. The assay used has high specificity for SARS-CoV-2, and cross-reactivity with common coronaviruses generated results below the cutoff used for this assay.20 However, even with a highly specific test, the effect of false-positive test results may be more marked in lower prevalence settings, including CA, FL, and WA. We did consider the performance characteristics of the ELISA when making seroprevalence estimates. Although the assay has high sensitivity (96%), it is not 100% sensitive and thus will not detect all persons with antibodies. Finally, several early reports indicate that not all persons with SARS-CoV-2 infection mount an antibody response, and antibody titers may be lower in those with milder disease; furthermore, levels of IgG and neutralizing antibodies decrease in some persons within 2 to 3 months after infection.25,33-35 For these reasons, seroprevalence estimates may underestimate the proportion of persons with prior infection in any population.

    Tracking population seroprevalence for SARS-CoV-2 infection serially, in a variety of specific geographic sites, should inform models of transmission dynamics and policy decisions regarding the effects of social distancing and other preventive measures. To inform understanding of the epidemiology of COVID-19, the CDC plans to conduct repeated sampling in these and other geographic sites around the US on an ongoing basis.26

    Conclusions

    In conclusion, the seroprevalence estimates we report suggest that at the time of specimen collection from March to early May 2020, a large majority of persons in 10 diverse geographic sites in the US had not been infected with SARS-CoV-2. The estimated number of infections, however, was much greater than the number of reported cases in all sites. This finding may reflect persons who had mild or no illness or who did not seek medical care or undergo testing but who still may have contributed to ongoing virus transmission in the population. Because persons often do not know if they are infected with SARS-CoV-2, the public should continue to take steps to help prevent the spread of COVID-19, such as wearing cloth face coverings when outside the home, remaining 6 feet apart from other people, washing hands frequently, and staying home when sick.

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    Article Information

    Accepted for Publication: July 7, 2020.

    Corresponding Author: Fiona P. Havers, MD, MHS, CDC COVID-19 Response Team, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS H24-6, Atlanta, GA 30329 (fhavers@cdc.gov).

    Published Online: July 21, 2020. doi:10.1001/jamainternmed.2020.4130

    Author Contributions: Drs Havers and Lim had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: Havers, Reed, Lim, Hall, Fry, Owen, Blackmore, Blog, Lindquist, Turabelidze, Wiesman, Schiffer, Thornburg.

    Acquisition, analysis, or interpretation of data: Havers, Reed, Lim, Montgomery, Klena, Cannon, Chiang, Gibbons, Krapiunaya, Morales-Betoulle, Roguski, Rasheed, Freeman, Lester, Mills, Carroll, Owen, Johnson, Semenova, Blog, Chai, Dunn, Hand, Jain, Lynfield, Pritchard, Sokol, Sosa, Watkins, Williams, Yendell, Schiffer, Thornburg.

    Drafting of the manuscript: Havers, Lim, Klena, Fry, Thornburg.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Havers, Lim, Gibbons, Roguski, Schiffer.

    Obtained funding: Havers.

    Administrative, technical, or material support: Havers, Montgomery, Klena, Cannon, Chiang, Morales-Betoulle, Mills, Carroll, Owen, Johnson, Semenova, Blackmore, Blog, Chai, Dunn, Jain, Lindquist, Pritchard, Sokol, Sosa, Turabelidze, Watkins, Wiesman, Williams, Schiffer, Thornburg.

    Study supervision: Havers, Reed, Montgomery, Hall, Fry, Johnson, Blog, Pritchard, Schiffer, Thornburg.

    Conflict of Interest Disclosures: Dr Wiesman reported receiving grants from US Department of Health and Human Services during the conduct of the study. No other disclosures were reported.

    Funding/Support: This work was supported by the Centers for Disease Control and Prevention, Atlanta, Georgia.

    Role of the Funder/Sponsor: The Centers for Disease Control and Prevention was involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; and decision to submit the manuscript for publication.

    Group Information: The author members of the CDC COVID-19 Response Team are Drs Havers, Reed, Lim, Montgomery, Klena, Hall, Fry, Chiang, Morales-Betoulle, Rasheed, Freeman, Lester, Mills, Carroll, Owen, Johnson, Semenova, and Thornburg; Mses Cannon, Gibbons, Krapiunaya, and Roguski; and Mr Schiffer. The author members of the State Collaborator Group are Drs Blackmore, Blog, Chai, Dunn, Jain, Lindquist, Lynfield, Sosa, Turabelidze, Watkins, Wiesman, Williams, and Yendell; Mses Hand and Sokol; and Mr Pritchard.

    Disclaimer: The findings and conclusions in the article are those of the authors and do not necessarily represent the views of the US Centers for Disease Control and Prevention.

    Additional Contributions: We thank LabCorp and Quest for supplying specimens. From Quest: William A. Meyer III, PhD, Larry A. Hirsch, BS, Taylor Hwang, BS, and Janet M. Rochat, MS. From the New York City Department of Mental Health and Hygiene: Marcelle Layton, MD. From the Centers for Disease Control and Prevention Molecular Pathogenesis and Immunology Research Laboratory team: Bailey Alston, MS, Muyiwa Ategbole, MPH, Shanna Bolcen, MSPH, Darbi Boulay, BS, Peter Browning, BS, Li Cronin, MS, Ebenezer David, PhD, Rita Desai, BS, Monica Epperson, PhD, Yamini Gorantla, PhD, Lily Jia, MS, Han Li, PhD, Pete Maniatis, MS, Jeff Martin, BA, Kimberly Moss, BS, Kristina Ortiz, MS, Palak Patel, MS, So Hee Park, BS, Yunlong Qin, PhD, Evelene Steward-Clark, MS, Heather Tatum, BS, Andrew Vogan, MS, and Briana Zellner, PhD. We also thank Ian E. Fellows, PhD, of Fellows Statistics for input on statistical methodology. These individuals were not compensated directly by CDC for their participation in this specific study.

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