Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2.
JAMIA Open
; 3(4): 496-499, 2020 Dec.
Article
in English
| MEDLINE | ID: covidwho-894604
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the US CDC, with our results providing entire probability distributions of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https//github.com/qunfengdong/AntibodyTest.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
Language:
English
Journal:
JAMIA Open
Year:
2020
Document Type:
Article
Affiliation country:
Jamiaopen
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