Evidence map›Paper›PMID 33666169›Full record

ArticleeLife2021

Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys.

Daniel B Larremore, Bailey K Fosdick, Kate M Bubar, Sam Zhang, Stephen M Kissler, C Jessica E Metcalf, Caroline O Buckee, Yonatan H Grad

Open access · goldAbstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

63 citing papers in PubMed, 99 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Daniel B LarremoreDepartment of Computer Science, University of Colorado Boulder, Boulder, United States.ORCID https://orcid.org/0000-0001-5273-5234
Bailey K FosdickDepartment of Statistics, Colorado State University, Fort Collins, United States.
Kate M BubarDepartment of Applied Mathematics, University of Colorado Boulder, Boulder, United States.
Sam ZhangDepartment of Applied Mathematics, University of Colorado Boulder, Boulder, United States.
Stephen M KisslerDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, United States.ORCID http://orcid.org/0000-0003-3062-7800
C Jessica E MetcalfDepartment of Ecology and Evolutionary Biology and the Woodrow Wilson School, Princeton University, Princeton, United States.ORCID http://orcid.org/0000-0003-3166-7521
Caroline O BuckeeDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States.ORCID https://orcid.org/0000-0002-8386-5899
Yonatan H GradDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, United States.ORCID https://orcid.org/0000-0001-5646-1314
Boston University · USUniversity of Colorado Boulder · USColorado State University · USWoodrow Wilson International Center for Scholars · US

Funding

Casual, Statistical and Mathematical Modeling with Serologic DataU01CA261277 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM, LIPSITCH, MARC · 2020 to 2023
$2.8M
NCI NIH HHS 1U01CA261277-01NCI NIH HHS U01 CA261277
6 · The paper itself

Abstract

Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies have been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.

Indexed as

AdolescentAdultAgedAge FactorsBayes TheoremChildChild, PreschoolCOVID-19COVID-19 Serological TestingHumansInfantInfant, NewbornMiddle AgedPandemicsSARS-CoV-2Seroepidemiologic StudiesantibodyCOVID-19epidemiologyglobal healthinfectious diseasemicrobiologymodelingnoneSARS-CoV-2serologyuncertainty

Identifiers

PMID33666169
PMCPMC7979159
OpenAlexW3017144323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.