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Article

Bayesian Binary Mixture Models as a Flexible Alternative to Cut-Off Analysis of ELISA Results, a Case Study of Seoul Orthohantavirus

Centre for Infectious Disease Control, Centre for Zoonoses and Environmental Microbiology, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands
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Academic Editors: Rainer G. Ulrich and Gerald Heckel
Viruses 2021, 13(6), 1155; https://doi.org/10.3390/v13061155
Received: 30 April 2021 / Revised: 27 May 2021 / Accepted: 27 May 2021 / Published: 16 June 2021
(This article belongs to the Special Issue Rodent-Borne Viruses)
Serological assays, such as the enzyme-linked immunosorbent assay (ELISA), are popular tools for establishing the seroprevalence of various infectious diseases in humans and animals. In the ELISA, the optical density is measured and gives an indication of the antibody level. However, there is variability in optical density values for individuals that have been exposed to the pathogen of interest, as well as individuals that have not been exposed. In general, the distribution of values that can be expected for these two categories partly overlap. Often, a cut-off value is determined to decide which individuals should be considered seropositive or seronegative. However, the classical cut-off approach based on a putative threshold ignores heterogeneity in immune response in the population and is thus not the optimal solution for the analysis of serological data. A binary mixture model does include this heterogeneity, offers measures of uncertainty and the direct estimation of seroprevalence without the need for correction based on sensitivity and specificity. Furthermore, the probability of being seropositive can be estimated for individual samples, and both continuous and categorical covariates (risk-factors) can be included in the analysis. Using ELISA results from rats tested for the Seoul orthohantavirus, we compared the classical cut-off method with a binary mixture model set in a Bayesian framework. We show that it performs similarly or better than cut-off methods, by comparing with real-time quantitative polymerase chain reaction (RT-qPCR) results. We therefore recommend binary mixture models as an analysis tool over classical cut-off methods. An example code is included to facilitate the practical use of binary mixture models in everyday practice. View Full-Text
Keywords: ELISA; mixture models; serology; SEOV; seoul orthohantavirus; cut-off analysis ELISA; mixture models; serology; SEOV; seoul orthohantavirus; cut-off analysis
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MDPI and ACS Style

Swart, A.; Maas, M.; de Vries, A.; Cuperus, T.; Opsteegh, M. Bayesian Binary Mixture Models as a Flexible Alternative to Cut-Off Analysis of ELISA Results, a Case Study of Seoul Orthohantavirus. Viruses 2021, 13, 1155. https://doi.org/10.3390/v13061155

AMA Style

Swart A, Maas M, de Vries A, Cuperus T, Opsteegh M. Bayesian Binary Mixture Models as a Flexible Alternative to Cut-Off Analysis of ELISA Results, a Case Study of Seoul Orthohantavirus. Viruses. 2021; 13(6):1155. https://doi.org/10.3390/v13061155

Chicago/Turabian Style

Swart, Arno, Miriam Maas, Ankje de Vries, Tryntsje Cuperus, and Marieke Opsteegh. 2021. "Bayesian Binary Mixture Models as a Flexible Alternative to Cut-Off Analysis of ELISA Results, a Case Study of Seoul Orthohantavirus" Viruses 13, no. 6: 1155. https://doi.org/10.3390/v13061155

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