Antibodies to SARS/CoV-2 in arbitrarily-selected Atlanta residents
Zou, J.; Bretin, A.; Gewirtz, A.
Show abstract
We quantitated anti-SARS/CoV-2 IgG and IgM by ELISA in self-collected blood samples (n=142) in arbitrarily-selected metro Atlanta residents, primarily acquaintances of the authors lab members from 4/17-4/27, 2020. Archived serum (n=34), serum from nucleic acid test (NAT)-positive subjects (n=4), and samples collected from NAT-positive community members (n=4) served to validate the assay. The range of anti-SARS/CoV-2 antibodies in archived and NAT-positive sera indicated need to compromise sensitivity or specificity. Accordingly, we set a cutoff of 4 SD above the mean for IgG and 3 SD above the mean for IgM to indicate that an individual had been exposed, and developed some degree of immunity, to SARS/CoV-2. The IgG cutoff clearly compromised sensitivity but offered high specificity, both of which were harder to gauge for IgM. Based on these cutoffs, excluding subjects whose participation resulted from self-suspected SARS/CoV-2 infection, we found 7.1% positivity for anti-SARS/CoV-2 IgG (3 of 127 subjects) or IgM (6 of 127). While we do not claim this small immune survey is broadly representative of metro Atlanta, and we have greater confidence in the IgG results, which had only 2.4% positivity, it nonetheless demonstrates that persons with antibodies to SARS/CoV-2, whove not suspected theyd been exposed to this virus, can readily be found in various Atlanta area neighborhoods (9 positives were in 8 zip codes). Accordingly, these results support the notion that dissemination of the virus is more widespread than testing would indicate but also suggests that most persons in metro Atlanta remain vulnerable to this virus. More generally, these results support the general utility of sero-surveillance to guide public policy but also highlight the difficulty of discerning if individuals have immunity to SARS/CoV-2.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SARS-CoV-2 Serologic Assays in Control and Unknown Populations Demonstrate the Necessity of Virus Neutralization Testing. 92%
- Cumulative incidence of SARS-CoV-2 infections among adults in Georgia, USA, August-December 2020 92%
- Performance characteristics of a rapid SARS-CoV-2 antigen detection assay at a public plaza testing site in San Francisco 91%
Similar papers in this journal
- Seroprevalence of SARS-CoV-2 Antibodies in Seattle, Washington—October 2019–April 2020 92%
- Differential antibody production by symptomatology in SARS-CoV-2 convalescent individuals 92%
- Performance of the TaqMan™ COVID-19 Pooling Kit for detection of SARS-CoV-2 in Asymptomatic and Symptomatic populations at an Institution of Higher Education 91%
Similar papers in this journal
- Antigen-based testing but not real-time PCR correlates with SARS-CoV-2 virus culture 92%
- Performance of Repeat BinaxNOW SARS-CoV-2 Antigen Testing in a Community Setting, Wisconsin, November-December 2020 92%
- The New Normal: Delayed Peak SARS-CoV-2 Viral Loads Relative to Symptom Onset and Implications for COVID-19 Testing Programs 92%
Similar papers in this journal
- Durable antibody responses in staff at two long-term care facilities, during and post SARS-CoV-2 outbreaks 93%
- Evaluation of the INDICAID™ COVID-19 Rapid Antigen Test in symptomatic populations and asymptomatic community testing 91%
- Multi-site clinical validation of Isothermal Amplification based SARS-COV-2 detection assays using different sampling strategies 90%
Similar papers in this journal
- Simian Immunodeficiency Virus and Storage Buffer: Field-friendly preservation methods for RNA viral detection in primate feces 90%
- SARS-CoV-2 antibody prevalence among industrial livestock operation workers and nearby community residents, North Carolina, USA, 2021-2022 90%
- The Serological Sciences Network (SeroNet) for COVID-19: Depth and Breadth of Serology Assays and Plans for Assay Harmonization 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.