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Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis

INTRODUCTION: Diagnostic test evaluation for African swine fever (ASF) in field settings like Vietnam is critical to understanding test application in intended populations for surveillance and control strategies. Bayesian latent class analysis (BLCA) uses the results of multiple imperfect tests appl...

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Autores principales: Schambow, Rachel, Giménez-Lirola, Luis G., Hanh, Vu Duc, Huong, Lai Thi Lan, Lan, Nguyen Thi, Trang, Pham Hong, Luc, Do Duc, Bo, Ha Xuan, Chuong, Vo Dinh, Rauh, Rolf, Nelson, William, Mora-Díaz, Juan Carlos, Rovira, Albert, Culhane, Marie R., Perez, Andres M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995851/
https://www.ncbi.nlm.nih.gov/pubmed/36908521
http://dx.doi.org/10.3389/fvets.2023.1079918
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author Schambow, Rachel
Giménez-Lirola, Luis G.
Hanh, Vu Duc
Huong, Lai Thi Lan
Lan, Nguyen Thi
Trang, Pham Hong
Luc, Do Duc
Bo, Ha Xuan
Chuong, Vo Dinh
Rauh, Rolf
Nelson, William
Mora-Díaz, Juan Carlos
Rovira, Albert
Culhane, Marie R.
Perez, Andres M.
author_facet Schambow, Rachel
Giménez-Lirola, Luis G.
Hanh, Vu Duc
Huong, Lai Thi Lan
Lan, Nguyen Thi
Trang, Pham Hong
Luc, Do Duc
Bo, Ha Xuan
Chuong, Vo Dinh
Rauh, Rolf
Nelson, William
Mora-Díaz, Juan Carlos
Rovira, Albert
Culhane, Marie R.
Perez, Andres M.
author_sort Schambow, Rachel
collection PubMed
description INTRODUCTION: Diagnostic test evaluation for African swine fever (ASF) in field settings like Vietnam is critical to understanding test application in intended populations for surveillance and control strategies. Bayesian latent class analysis (BLCA) uses the results of multiple imperfect tests applied to an individual of unknown disease status to estimate the diagnostic sensitivity and specificity of each test, forgoing the need for a reference test. METHODS: Here, we estimated and compared the diagnostic sensitivity and specificity of a novel indirect ELISA (iELISA) for ASF virus p30 antibody (Innoceleris LLC.) and the VetAlert™ ASF virus DNA Test Kit (qPCR, Tetracore Inc.) in field samples from Vietnam by assuming that disease status 1) is known and 2) is unknown using a BLCA model. In this cross-sectional study, 398 paired, individual swine serum/oral fluid (OF) samples were collected from 30 acutely ASF-affected farms, 37 chronically ASF-affected farms, and 20 ASF-unaffected farms in Vietnam. Samples were tested using both diagnostic assays. Diagnostic sensitivity was calculated assuming samples from ASF-affected farms were true positives and diagnostic sensitivity by assuming samples from unaffected farms were true negatives. ROC curves were plotted and AUC calculated for each test/sample combination. For comparison, a conditionally dependent, four test/sample combination, three population BLCA model was fit. RESULTS: When considering all assumed ASF-affected samples, qPCR sensitivity was higher for serum (65.2%, 95% Confidence Interval [CI] 58.1–71.8) and OF (52%, 95%CI 44.8–59.2) compared to the iELISA (serum: 42.9%, 95%CI 35.9–50.1; OF: 33.3%, 95%CI 26.8–40.4). qPCR-serum had the highest AUC (0.895, 95%CI 0.863–0.928). BLCA estimates were nearly identical to those obtained when assuming disease status and were robust to changes in priors. qPCR sensitivity was considerably higher than ELISA in the acutely-affected population, while ELISA sensitivity was higher in the chronically-affected population. Specificity was nearly perfect for all test/sample types. DISCUSSION: The effect of disease chronicity on sensitivity and specificity could not be well characterized here due to limited data, but future studies should aim to elucidate these trends to understand the best use of virus and antibody detection methods for ASF. Results presented here will help the design of surveillance and control strategies in Vietnam and other countries affected by ASF.
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spelling pubmed-99958512023-03-10 Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis Schambow, Rachel Giménez-Lirola, Luis G. Hanh, Vu Duc Huong, Lai Thi Lan Lan, Nguyen Thi Trang, Pham Hong Luc, Do Duc Bo, Ha Xuan Chuong, Vo Dinh Rauh, Rolf Nelson, William Mora-Díaz, Juan Carlos Rovira, Albert Culhane, Marie R. Perez, Andres M. Front Vet Sci Veterinary Science INTRODUCTION: Diagnostic test evaluation for African swine fever (ASF) in field settings like Vietnam is critical to understanding test application in intended populations for surveillance and control strategies. Bayesian latent class analysis (BLCA) uses the results of multiple imperfect tests applied to an individual of unknown disease status to estimate the diagnostic sensitivity and specificity of each test, forgoing the need for a reference test. METHODS: Here, we estimated and compared the diagnostic sensitivity and specificity of a novel indirect ELISA (iELISA) for ASF virus p30 antibody (Innoceleris LLC.) and the VetAlert™ ASF virus DNA Test Kit (qPCR, Tetracore Inc.) in field samples from Vietnam by assuming that disease status 1) is known and 2) is unknown using a BLCA model. In this cross-sectional study, 398 paired, individual swine serum/oral fluid (OF) samples were collected from 30 acutely ASF-affected farms, 37 chronically ASF-affected farms, and 20 ASF-unaffected farms in Vietnam. Samples were tested using both diagnostic assays. Diagnostic sensitivity was calculated assuming samples from ASF-affected farms were true positives and diagnostic sensitivity by assuming samples from unaffected farms were true negatives. ROC curves were plotted and AUC calculated for each test/sample combination. For comparison, a conditionally dependent, four test/sample combination, three population BLCA model was fit. RESULTS: When considering all assumed ASF-affected samples, qPCR sensitivity was higher for serum (65.2%, 95% Confidence Interval [CI] 58.1–71.8) and OF (52%, 95%CI 44.8–59.2) compared to the iELISA (serum: 42.9%, 95%CI 35.9–50.1; OF: 33.3%, 95%CI 26.8–40.4). qPCR-serum had the highest AUC (0.895, 95%CI 0.863–0.928). BLCA estimates were nearly identical to those obtained when assuming disease status and were robust to changes in priors. qPCR sensitivity was considerably higher than ELISA in the acutely-affected population, while ELISA sensitivity was higher in the chronically-affected population. Specificity was nearly perfect for all test/sample types. DISCUSSION: The effect of disease chronicity on sensitivity and specificity could not be well characterized here due to limited data, but future studies should aim to elucidate these trends to understand the best use of virus and antibody detection methods for ASF. Results presented here will help the design of surveillance and control strategies in Vietnam and other countries affected by ASF. Frontiers Media S.A. 2023-02-23 /pmc/articles/PMC9995851/ /pubmed/36908521 http://dx.doi.org/10.3389/fvets.2023.1079918 Text en Copyright © 2023 Schambow, Giménez-Lirola, Hanh, Huong, Lan, Trang, Luc, Bo, Chuong, Rauh, Nelson, Mora-Díaz, Rovira, Culhane and Perez. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Veterinary Science
Schambow, Rachel
Giménez-Lirola, Luis G.
Hanh, Vu Duc
Huong, Lai Thi Lan
Lan, Nguyen Thi
Trang, Pham Hong
Luc, Do Duc
Bo, Ha Xuan
Chuong, Vo Dinh
Rauh, Rolf
Nelson, William
Mora-Díaz, Juan Carlos
Rovira, Albert
Culhane, Marie R.
Perez, Andres M.
Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title_full Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title_fullStr Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title_full_unstemmed Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title_short Modeling the accuracy of a novel PCR and antibody ELISA for African swine fever virus detection using Bayesian latent class analysis
title_sort modeling the accuracy of a novel pcr and antibody elisa for african swine fever virus detection using bayesian latent class analysis
topic Veterinary Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995851/
https://www.ncbi.nlm.nih.gov/pubmed/36908521
http://dx.doi.org/10.3389/fvets.2023.1079918
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