Please use this identifier to cite or link to this item: https://doi.org/10.1111/irv.12452
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dc.titleRethinking thresholds for serological evidence of influenza virus infection
dc.contributor.authorZhao, X
dc.contributor.authorSiegel, K
dc.contributor.authorChen, M.I.-C
dc.contributor.authorCook, A.R
dc.date.accessioned2020-09-01T00:54:39Z
dc.date.available2020-09-01T00:54:39Z
dc.date.issued2017
dc.identifier.citationZhao, X, Siegel, K, Chen, M.I.-C, Cook, A.R (2017). Rethinking thresholds for serological evidence of influenza virus infection. Influenza and other Respiratory Viruses 11 (3) : 202-210. ScholarBank@NUS Repository. https://doi.org/10.1111/irv.12452
dc.identifier.issn17502640
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/173788
dc.description.abstractIntroduction: For pathogens such as influenza that cause many subclinical cases, serologic data can be used to estimate attack rates and the severity of an epidemic in near real time. Current methods for analysing serologic data tend to rely on use of a simple threshold or comparison of titres between pre- and post-epidemic, which may not accurately reflect actual infection rates. Methods: We propose a method for quantifying infection rates using paired sera and bivariate probit models to evaluate the accuracy of thresholds currently used for influenza epidemics with low and high existing herd immunity levels, and a subsequent non-influenza period. Pre- and post-epidemic sera were taken from a cohort of adults in Singapore (n=838). Bivariate probit models with latent titre levels were fit to the joint distribution of haemagglutination-inhibition assay-determined antibody titres using Markov chain Monte Carlo simulation. Results: Estimated attack rates were 15% (95% credible interval: 12%-19%) for the first H1N1 pandemic wave. For a large outbreak due to a new strain, a threshold of 1:20 and a twofold rise (if pared sera is available) would result in a more accurate estimate of incidence. Conclusion: The approach presented here offers the basis for a reconsideration of methods used to assess diagnostic tests by both reconsidering the thresholds used and by analysing serological data with a novel statistical model. © 2017 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.
dc.sourceUnpaywall 20200831
dc.subject2009 H1N1 influenza
dc.subjectadult
dc.subjectaged
dc.subjectantibody titer
dc.subjectArticle
dc.subjectcohort analysis
dc.subjectcontrolled study
dc.subjectdiagnostic accuracy
dc.subjectdiagnostic test accuracy study
dc.subjecthemagglutination inhibition test
dc.subjectherd immunity
dc.subjecthuman
dc.subjectinfection rate
dc.subjectinfluenza
dc.subjectInfluenza virus
dc.subjectlimit of detection
dc.subjectMarkov chain
dc.subjectMonte Carlo method
dc.subjectnonhuman
dc.subjectpredictive value
dc.subjectpriority journal
dc.subjectsensitivity analysis
dc.subjectsensitivity and specificity
dc.subjectserology
dc.subjectSingapore
dc.subjectstatistical model
dc.subjectthreshold limit value
dc.subjectblood
dc.subjectgenetics
dc.subjectimmunology
dc.subjectInfluenza A virus (H1N1)
dc.subjectInfluenza, Human
dc.subjectisolation and purification
dc.subjectprospective study
dc.subjectvirology
dc.subjectvirus antibody
dc.subjectAntibodies, Viral
dc.subjectHemagglutination Inhibition Tests
dc.subjectHumans
dc.subjectInfluenza A Virus, H1N1 Subtype
dc.subjectInfluenza, Human
dc.subjectProspective Studies
dc.subjectSingapore
dc.typeArticle
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1111/irv.12452
dc.description.sourcetitleInfluenza and other Respiratory Viruses
dc.description.volume11
dc.description.issue3
dc.description.page202-210
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