A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India Article Swipe
Matt D. T. Hitchings
,
Eshan U. Patel
,
Rifa Khan
,
Aylur K. Srikrishnan
,
Mark G. Anderson
,
Karan Sanjay Kumar
,
Amy Wesolowski
,
Hussain Syed Iqbal
,
Mary A. Rodgers
,
Shruti H. Mehta
,
Gavin Cloherty
,
Derek A. T. Cummings
,
Sunil S. Solomon
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/aje/kwad103
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/aje/kwad103
Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers’ cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.
Related Topics
Concepts
Seroprevalence
Medicine
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Coronavirus disease 2019 (COVID-19)
2019-20 coronavirus outbreak
Virology
Environmental health
Statistics
Demography
Outbreak
Internal medicine
Mathematics
Immunology
Antibody
Serology
Infectious disease (medical specialty)
Disease
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/aje/kwad103
- https://academic.oup.com/aje/advance-article-pdf/doi/10.1093/aje/kwad103/50058139/kwad103.pdf
- OA Status
- hybrid
- Cited By
- 11
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366603553
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366603553Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/aje/kwad103Digital Object Identifier
- Title
-
A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, IndiaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-21Full publication date if available
- Authors
-
Matt D. T. Hitchings, Eshan U. Patel, Rifa Khan, Aylur K. Srikrishnan, Mark G. Anderson, Karan Sanjay Kumar, Amy Wesolowski, Hussain Syed Iqbal, Mary A. Rodgers, Shruti H. Mehta, Gavin Cloherty, Derek A. T. Cummings, Sunil S. SolomonList of authors in order
- Landing page
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https://doi.org/10.1093/aje/kwad103Publisher landing page
- PDF URL
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https://academic.oup.com/aje/advance-article-pdf/doi/10.1093/aje/kwad103/50058139/kwad103.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/aje/advance-article-pdf/doi/10.1093/aje/kwad103/50058139/kwad103.pdfDirect OA link when available
- Concepts
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Seroprevalence, Medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19), 2019-20 coronavirus outbreak, Virology, Environmental health, Statistics, Demography, Outbreak, Internal medicine, Mathematics, Immunology, Antibody, Serology, Infectious disease (medical specialty), Disease, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.due | 176 |
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| abstract_inverted_index.was | 83, 95, 145, 154 |
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| abstract_inverted_index.CrI: | 120 |
| abstract_inverted_index.This | 93 |
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| abstract_inverted_index.each | 91 |
| abstract_inverted_index.from | 39 |
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| abstract_inverted_index.4,677 | 34 |
| abstract_inverted_index.50.2, | 121 |
| abstract_inverted_index.51.5% | 118 |
| abstract_inverted_index.63.8, | 115 |
| abstract_inverted_index.64.9% | 110 |
| abstract_inverted_index.66.0) | 116 |
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| abstract_inverted_index.basis | 24 |
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| abstract_inverted_index.model | 73 |
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| abstract_inverted_index.spike | 55 |
| abstract_inverted_index.there | 82 |
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| abstract_inverted_index.5-fold | 85 |
| abstract_inverted_index.52.9), | 122 |
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| abstract_inverted_index.anti-N | 106, 134 |
| abstract_inverted_index.anti-S | 104, 132, 170 |
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| abstract_inverted_index.inclusion | 179 |
| abstract_inverted_index.proteins. | 60 |
| abstract_inverted_index.scale-up, | 163 |
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| abstract_inverted_index.relationships | 130 |
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| abstract_inverted_index.seroprevalence, | 63 |
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| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I33213144 |
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