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Article ; Online: Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2.

Dong, Qunfeng / Gao, Xiang

JAMIA open

2020 Volume 3, Issue 4, Page(s) 496–499

Abstract: Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome ... the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate ... of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https://github.com ...

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.
Language	English
Publishing date	2020-11-23
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooaa049
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Dong, Qunfeng / Gao, Xiang

medRxiv

Abstract: Accurately estimating the seroprevalence of antibodies to SARS-CoV-2 requires the use ... of appropriate methods. Bayesian statistics provides a natural framework for considering the variabilities ... We present a full Bayesian approach for this purpose, and we demonstrate the utility of our approach using ...

Abstract	Accurately estimating the seroprevalence of antibodies to SARS-CoV-2 requires the use of appropriate methods. Bayesian statistics provides a natural framework for considering the variabilities of specificity and sensitivity of the antibody tests, as well as for incorporating prior knowledge of viral infection prevalence. We present a full Bayesian approach for this purpose, and we demonstrate the utility of our approach using a recently published large-scale dataset from the U.S. CDC.
Keywords	covid19
Language	English
Publishing date	2020-08-25
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.08.23.20180497
Database	COVID19

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Article ; Online: Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Dong, Q. / Gao, X.

Abstract	Accurately estimating the seroprevalence of antibodies to SARS-CoV-2 requires the use of appropriate methods. Bayesian statistics provides a natural framework for considering the variabilities of specificity and sensitivity of the antibody tests, as well as for incorporating prior knowledge of viral infection prevalence. We present a full Bayesian approach for this purpose, and we demonstrate the utility of our approach using a recently published large-scale dataset from the U.S. CDC.
Keywords	covid19
Publisher	MedRxiv; WHO
Document type	Article ; Online
DOI	10.1101/2020.08.23.20180497
Database	COVID19

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Article: Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Dong, Qunfeng Gao Xiang

JAMIA Open

Abstract: Accurate estimations of the seroprevalence of antibodies to SARS-CoV-2 need to properly consider ... in a population may also be important for adjusting the estimation of seroprevalence For this purpose, we have ... of the antibody tests, as well as the prior probability distribution of seroprevalence We have demonstrated ...

Abstract	Accurate estimations of the seroprevalence of antibodies to SARS-CoV-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 U S 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
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #894604
Database	COVID19

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Article ; Online: Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Dong, Qunfeng / Gao, Xiang

JAMIA Open ; ISSN 2574-2531

2020

Abstract: Abstract Accurate estimations of the seroprevalence of antibodies to SARS-CoV-2 need to properly ... of viral infection in a population may also be important for adjusting the estimation of seroprevalence ... of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have ...

Abstract	Abstract Accurate estimations of the seroprevalence of antibodies to SARS-CoV-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 U.S. 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. Lay summary To estimate the extent of the viral infection, we have developed a statistical method that can incorporate the variabilities of specificity and sensitivity of the antibody tests. Our computer code is freely available at https://github.com/qunfengdong/AntibodyTest.
Keywords	covid19
Language	English
Publisher	Oxford University Press (OUP)
Publishing country	uk
Document type	Article ; Online
DOI	10.1093/jamiaopen/ooaa049
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Towards Bayesian Evaluation of Seroprevalence Studies

Jana Furstova / Zuzana Kratka / Tomas Furst / Jan Strojil / Ondrej Vencalek

Medical Sciences Forum, Vol 4, Iss 11, p

2021 Volume 11

Abstract: ... In this contribution, we show how to use the framework of Bayesian inference to produce a reasonable estimate ... of seroprevalence from studies that use a single binary test. Bayes’ Theorem sometimes produces results that seem ... Bayes’ Theorem represents a mathematical formalization of the common sense. What we know ...

Abstract	Bayes’ Theorem represents a mathematical formalization of the common sense. What we know about the world today is what we knew yesterday plus what the data told us. The lack of understanding of this concept is the source of many errors and wrong judgements in the current COVID-19 pandemic. In this contribution, we show how to use the framework of Bayesian inference to produce a reasonable estimate of seroprevalence from studies that use a single binary test. Bayes’ Theorem sometimes produces results that seem counter-intuitive at first sight. It is important to realize that the reality may be different from its image represented by test results. The extent to which these two worlds differ depends on the performance of the test (i.e., its sensitivity and specificity), and the prevalence of the tested condition.
Keywords	Bayesian ; seroprevalence ; antibodies ; false positive ; SARS-CoV-2 ; COVID-19 ; Medicine ; R
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Two-phase Bayesian latent class analysis to assess diagnostic test performance in the absence of a gold standard: COVID-19 serological assays as a proof of concept.

Camirand Lemyre, Felix / Honfo, Sewanou Hermann / Caya, Chelsea / Cheng, Matthew P / Colwill, Karen / Corsini, Rachel / Gingras, Anne-Claude / Jassem, Agatha / Krajden, Mel / Márquez, Ana Citlali / Mazer, Bruce D / McLennan, Meghan / Renaud, Christian / Yansouni, Cedric P / Papenburg, Jesse / Lewin, Antoine

Vox sanguinis

2023 Volume 118, Issue 12, Page(s) 1069–1077

Abstract: ... from May to July 2020 and tested for anti-SARS-CoV-2 antibodies using seven serological assays (five commercial and ... we aimed to demonstrate how Bayesian latent class models (BLCMs) could be used to estimate SARS-CoV-2 ... two non-commercial).: Results: SARS-CoV-2 seroprevalence was estimated at 0.71% (95% credible ...

Abstract	Background and objectives: In this proof-of-concept study, which included blood donor samples, we aimed to demonstrate how Bayesian latent class models (BLCMs) could be used to estimate SARS-CoV-2 seroprevalence in the absence of a gold standard assay under a two-phase sampling design. Materials and methods: To this end, 6810 plasma samples from blood donors who resided in Québec (Canada) were collected from May to July 2020 and tested for anti-SARS-CoV-2 antibodies using seven serological assays (five commercial and two non-commercial). Results: SARS-CoV-2 seroprevalence was estimated at 0.71% (95% credible interval [CrI] = 0.53%-0.92%). The cPass assay had the lowest sensitivity estimate (88.7%; 95% CrI = 80.6%-94.7%), while the Héma-Québec assay had the highest (98.7%; 95% CrI = 97.0%-99.6%). Conclusion: The estimated low seroprevalence (which indicates a relatively limited spread of SARS-CoV-2 in Quebec) might change rapidly-and this tool, developed using blood donors, could enable a rapid update of the prevalence estimate in the absence of a gold standard. Further, the present analysis illustrates how a two-stage BLCM sampling design, along with blood donor samples, can be used to estimate the performance of new diagnostic tests and inform public health decisions regarding a new or emerging disease for which a perfect reference standard does not exist.
MeSH term(s)	Humans ; COVID-19/diagnosis ; COVID-19/epidemiology ; SARS-CoV-2 ; Latent Class Analysis ; Bayes Theorem ; Seroepidemiologic Studies ; Sensitivity and Specificity ; Antibodies, Viral ; Diagnostic Tests, Routine ; COVID-19 Testing
Chemical Substances	Antibodies, Viral
Language	English
Publishing date	2023-10-18
Publishing country	England
Document type	Journal Article
ZDB-ID	80313-3
ISSN	1423-0410 ; 0042-9007
ISSN (online)	1423-0410
ISSN	0042-9007
DOI	10.1111/vox.13545
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Article ; Online: Seroprevalence of SARS-CoV-2 on health professionals via Bayesian estimation: a Brazilian case study before and after vaccines.

Maior, Caio B S / Lins, Isis D / Raupp, Leonardo S / Moura, Márcio C / Felipe, Felipe / Santana, João M M / Fernandes, Mariana P / Araújo, Alice V / Gomes, Ana L V

Acta tropica

2022 Volume 233, Page(s) 106551

Abstract: ... accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g ... information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city ... specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis ...

Abstract	The increasing number of COVID-19 infections brought by the current pandemic has encouraged the scientific community to analyze the seroprevalence in populations to support health policies. In this context, accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g., specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis using IgA and IgG antibody levels through multiple scenarios regarding data availability from different information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city: no data available, data only related to the test performance, data from other regions. The study population comprises 432 subjects with more than 620 collections analyzed via IgA/IgG ELISA tests. We conducted the study in pre- and post-vaccination campaigns started in Brazil. We discuss the importance of aggregating available data from various sources to create informative prior knowledge. Considering prior information from the USA and Europe, the pre-vaccine seroprevalence means are 8.04% and 10.09% for IgG and 7.40% and 9.11% for IgA. For the post-vaccination campaign and considering local informative prior, the median is 84.83% for IgG, which confirms a sharp increase in the seroprevalence after vaccination. Additionally, stratification considering differences in sex, age (younger than 30 years, between 30 and 49 years, and older than 49 years), and presence of comorbidities are provided for all scenarios.
MeSH term(s)	Adult ; Antibodies, Viral ; Bayes Theorem ; Brazil/epidemiology ; COVID-19/epidemiology ; COVID-19/prevention & control ; Humans ; Immunoglobulin A ; Immunoglobulin G ; SARS-CoV-2 ; Seroepidemiologic Studies ; Vaccines
Chemical Substances	Antibodies, Viral ; Immunoglobulin A ; Immunoglobulin G ; Vaccines
Language	English
Publishing date	2022-06-09
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	210415-5
ISSN	1873-6254 ; 0001-706X
ISSN (online)	1873-6254
ISSN	0001-706X
DOI	10.1016/j.actatropica.2022.106551
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Article ; Online: Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.

Chitwood, Melanie H / Russi, Marcus / Gunasekera, Kenneth / Havumaki, Joshua / Klaassen, Fayette / Pitzer, Virginia E / Salomon, Joshua A / Swartwood, Nicole A / Warren, Joshua L / Weinberger, Daniel M / Cohen, Ted / Menzies, Nicolas A

PLoS computational biology

2022 Volume 18, Issue 8, Page(s) e1010465

Abstract: ... We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county ... ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 ... of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent ...

Abstract	Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.
MeSH term(s)	Bayes Theorem ; COVID-19/epidemiology ; Epidemics ; Humans ; SARS-CoV-2 ; Seroepidemiologic Studies ; United States/epidemiology
Language	English
Publishing date	2022-08-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010465
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Article ; Online: Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests.

Kline, David / Li, Zehang / Chu, Yue / Wakefield, Jon / Miller, William C / Norris Turner, Abigail / Clark, Samuel J

Proceedings of the National Academy of Sciences of the United States of America

2021 Volume 118, Issue 26

Abstract: ... that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized ... poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe ... Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 ...

Abstract	Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States-Indiana and Connecticut-have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.
MeSH term(s)	Adolescent ; Adult ; Aged ; Bayes Theorem ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Serological Testing ; Female ; Humans ; Male ; Middle Aged ; Ohio/epidemiology ; Prevalence ; SARS-CoV-2 ; Seroepidemiologic Studies
Language	English
Publishing date	2021-06-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2023947118
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