Journal of
Veterinary Medicine and Animal Health

  • Abbreviation: J. Vet. Med. Anim. Health
  • Language: English
  • ISSN: 2141-2529
  • DOI: 10.5897/JVMAH
  • Start Year: 2009
  • Published Articles: 348

Full Length Research Paper

Bayesian estimation to test accuracy for influenza A infection via respiratory clinical signs in the absence of a gold standard

Nitipong Homwong*
  • Nitipong Homwong*
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA. 2Kasetsart University, Kamphaeng Saen, Nakhon Pathom, Thailand.
  • Google Scholar
Douglas Marthaler
  • Douglas Marthaler
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA. Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA.
  • Google Scholar
Matteo Convertino
  • Matteo Convertino
  • Division of Environmental Health Sciences and Public Health Informatics Program, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA. Institute on the Environment, University of Minnesota, Saint Paul, Minnesota, USA. Institute for Engineering in Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
  • Google Scholar
Montserrat Torremorell
  • Montserrat Torremorell
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA.
  • Google Scholar
Meggan E. Craft
  • Meggan E. Craft
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA. Institute on the Environment, University of Minnesota, Saint Paul, Minnesota, USA.
  • Google Scholar
Benjamin Hause
  • Benjamin Hause
  • Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, Kansas, USA.
  • Google Scholar
John Deen
  • John Deen
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, USA.
  • Google Scholar


  •  Received: 22 July 2015
  •  Accepted: 31 August 2015
  •  Published: 31 October 2015

References

Allerson M, Davies P, Gramer M, Torremorell M (2013a). Infection Dynamics of Pandemic 2009 H1N1 Influenza Virus in a Two-Site Swine Herd. Transbound. Emerg. Dis. 61(6):490.499.
 
Allerson M, Deen J, Detmer S, Gramer M, Joo HS, Romagosa A, Torremorell M (2013b). The impact of maternally derived immunity on influenza A virus transmission in neonatal pig populations. Vaccine 31:500-505.
Crossref
 
Baadsgaard NP, Jørgensen E (2003). A Bayesian approach to the accuracy of clinical observations. Prev. Vet. Med. 59(4):189-206.
Crossref
 
Black MA, Craig BA (2002). Estimating disease prevalence in the absence of a gold standard. Stat. Med. 21(18):2653-2669.
Crossref
 
Bouwknegt M, Engel B, Herremans M, Widdowson M, Worm H, Koopmans M, Frankena K, de Roda Husman A, De Jong M, Van Der Poel W (2008). Bayesian estimation of hepatitis E virus seroprevalence for populations with different exposure levels to swine in The Netherlands. Epidemiol. Infect. 136(04):567-576.
Crossref
 
Branscum AJ, Gardner IA, Johnson WO (2005). Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling. Prev. Vet. Med. 68(2):145-163.
Crossref
 
Carlin BP, Louis TA (2008). The Bayesian appoach. Bayesian methods for data analysis. Chapman & Hall/CRC Press. New York, USA pp. 15-104.
 
Choi YK, Goyal SM, Joo H (2002). Prevalence of swine influenza virus subtypes on swine farms in the United States. Arch. Virol. 147(6):1209-1220.
Crossref
 
Christensen J, Gardner IA (2000). Herd-level interpretation of test results for epidemiologic studies of animal diseases. Prev. Vet. Med. 45(1):83-106.
Crossref
 
Corzo CA, Culhane M, Juleen K, Stigger-Rosser E, Ducatez MF, Webby RJ, Lowe JF (2013a). Active Surveillance for Influenza A Virus among Swine, Midwestern United States, 2009–2011. Emerg. Infect. Dis. 19(6):954-960.
Crossref
 
Corzo CA, Romagosa A, Dee SA, Gramer MR, Morrison RB, Torremorell M (2013b). Relationship between airborne detection of influenza A virus and the number of infected pigs. Vet. J. 196:171-175.
Crossref
 
Cowling DW, Gardner IA, Johnson WO (1999). Comparison of methods for estimation of individual-level prevalence based on pooled samples. Prev. Vet. Med. 39(3):211-225.
Crossref
 
Davies P (2006). Principle of diagnostic testing of animal populations. In. AASV Ann. Meeting Proc. pp. 345-350.
 
Deblanc C, Robert F, Pinard T, Gorin S, Quéguiner S, Gautier-Bouchardon A, Ferré S, Garraud J, Cariolet R, Brack M (2012). Pre-infection of pigs with Mycoplasma hyopneumoniae induces oxidative stress that influences outcomes of a subsequent infection with a swine influenza virus of H1N1 subtype. Vet. Microbiol. 162:643-651.
Crossref
 
Dendukuri N, Joseph L (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 57(1):158-167.
Crossref
 
Donovan TS (2008). Influenza isolate selection methodology for timely autogenous vaccine use. In. the American Association of Swine. Vet. Ann. Meeting pp. 557-561.
 
Dykhuis HC, Painter T, Fangman T, Holtkamp D (2012). Assessing production parameters and economic impact of swine influenza, PRRS and Mycoplasma hyopneumoniae on finishing pigs in a large production system. In. the American Association of Swine. Vet. Ann. Meeting pp. 75-76.
 
Enøe C, Georgiadis MP, Johnson WO (2000). Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown. Prev. Vet. Med. 45(1–2):61-81.
Crossref
 
Fablet C, Marois-Créhan C, Simon G, Grasland B, Jestin A, Kobisch M, Madec F, Rose N (2012). Infectious agents associated with respiratory diseases in 125 farrow-to-finish pig herds: a cross-sectional study. Vet. Microbiol. 157(1):152-163.
Crossref
 
Gardner IA, Stryhn H, Lind P, Collins MT (2000). Conditional dependence between tests affects the diagnosis and surveillance of animal diseases. Prev. Vet. Med. 45(1):107-122.
Crossref
 
Gelman A, Rubin DB (1992). Inference from iterative simulation using multiple sequences. Stat. Sci. pp. 457-472.
Crossref
 
Geurden T, Berkvens D, Casaert S, Vercruysse J, Claerebout E (2008). A Bayesian evaluation of three diagnostic assays for the detection of Giardia duodenalis in symptomatic and asymptomatic dogs. Vet. Parasitol. 157(1):14-20.
Crossref
 
Geweke J (1991). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Federal Reserve Bank of Minneapolis, Research Department.
 
Goodell CK, Prickett J, Kittawornrat A, Zhou F, Rauh R, Nelson W, O'Connell C, Burrell A, Wang C, Yoon K-J (2013). Probability of detecting influenza A virus subtypes H1N1 and H3N2 in individual pig nasal swabs and pen-based oral fluid specimens over time. Vet. Microbiol. 166(3):450-460.
Crossref
 
Greiner M, Gardner I (2000a). Epidemiologic issues in the validation of veterinary diagnostic tests. Prev. Vet. Med. 45(1):3-22.
Crossref
 
Greiner M, Gardner IA (2000b). Application of diagnostic tests in veterinary epidemiologic studies. Prev. Vet. Med. 45(1):43-59.
Crossref
 
Hadley W (2009). ggplot2: Elegant graphics for data analysis. Springer. New York.
 
Holtkamp D, Rotto H, Garcia R (2007). The economic cost of major health challenges in large US swine production systems. In. the American Association of Swine Veterinarians Annual Meeting. Orlando, FL, USA pp. 85-89.
 
Johnson WO, Gastwirth JL, Pearson LM (2001). Screening without a "gold standard": the Hui-Walter paradigm revisited. Am. J. Epidemiol. 153(9):921-924.
Crossref
 
Joseph L, Gyorkos TW, Coupal L (1995). Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am. J. Epidemiol. 141(3):263-272.
 
Lunn D, Jackson C, Spiegelhalter DJ, Best N, Thomas A (2012). Model checking and comaprison. The BUGS book: A practical introduction to Bayesian analysis. CRC Press. Boca Raton, FL.
 
Marin X (2013). ggmcmc: Graphical tools for analyzing Markov Chain Monte Carlo simulations from Bayesian inference. Available at: http://xavier-fim.net/packages/ggmcmc.
 
Mohan R, Saif Y, Erickson G, Gustafson G, Easterday B (1981). Serologic and epidemiologic evidence of infection in turkeys with an agent related to the swine influenza virus. Avian Dis. pp. 11-16.
Crossref
 
Nérette P, Stryhn H, Dohoo I, Hammell L (2008). Using pseudogold standards and latent-class analysis in combination to evaluate the accuracy of three diagnostic tests. Prev. Vet. Med. 85(3):207-225.
Crossref
 
Olsen C, Carey S, Hinshaw L, Karasin A (2000). Virologic and serologic surveillance for human, swine and avian influenza virus infections among pigs in the north-central United States. Arch. Virol. 145(7):1399-1419.
Crossref
 
Paul S, Toft N, Agerholm JS, Christoffersen AB, Agger JF (2013). Bayesian estimation of sensitivity and specificity of Coxiella burnetii antibody ELISA tests in bovine blood and milk. Prev. Vet. Med. 109(3):258-263.
Crossref
 
Plummer M (2013). JAGS Version 3.4.0 user manual.
 
Plummer M (2015). rjags: Bayesian graphical Models using MCMC. R package version 3-14. Available at: http://CRAN.R-project.org/package=rjags.
 
Plummer M, Best N, Cowles K, Vines K (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News 6(1):7-11.
 
Poljak Z, Dewey CE, Martin SW, Christensen J, Carman S, Friendship RM (2008a). Prevalence of and risk factors for influenza in southern Ontario swine herds in 2001 and 2003. Can. J. Vet. Res. 72(1):7.
 
Poljak Z, Friendship RM, Carman S, McNab WB, Dewey CE (2008b). Investigation of exposure to swine influenza viruses in Ontario (Canada) finisher herds in 2004 and 2005. Prev. Vet. Med. 83(1):24-40.
Crossref
 
Praud A, Gimenez O, Zanella G, Dufour B, Pozzi N, Antras V, Meyer L, Garin-Bastuji B (2012). Estimation of sensitivity and specificity of five serological tests for the diagnosis of porcine brucellosis. Prev. Vet. Med. 104(1):94-100.
Crossref
 
R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing.