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Harmonisation of Transmissible Disease Interpretation in the EU (HOTLINE)

EFSA supporting publications(2019)

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EFSA Supporting PublicationsVolume 16, Issue 7 1678E External scientific reportOpen Access Harmonisation Of Transmissible disease Interpretation in the EU (HOTLINE) Polychronis Kostoulas, Polychronis Kostoulas School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this authorArmando Giovannini, Armando Giovannini Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale"- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis, Teramo, 64100 ItalySearch for more papers by this authorAna Alba, Ana Alba IRTA., Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, SpainSearch for more papers by this authorArianna Comin, Arianna Comin Dept. of Disease Control and Epidemiology, Swedish National Veterinary Institute, Uppsala, SwedenSearch for more papers by this authorEleftherios Meletis, Eleftherios Meletis School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this authorSimona Iannetti, Simona Iannetti Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale"- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis, Teramo, 64100 ItalySearch for more papers by this authorSebastian Napp, Sebastian Napp IRTA., Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, SpainSearch for more papers by this authorAnn Lindberg, Ann Lindberg Dept. of Disease Control and Epidemiology, Swedish National Veterinary Institute, Uppsala, SwedenSearch for more papers by this authorNikolaos Solomakos, Nikolaos Solomakos School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this author Polychronis Kostoulas, Polychronis Kostoulas School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this authorArmando Giovannini, Armando Giovannini Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale"- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis, Teramo, 64100 ItalySearch for more papers by this authorAna Alba, Ana Alba IRTA., Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, SpainSearch for more papers by this authorArianna Comin, Arianna Comin Dept. of Disease Control and Epidemiology, Swedish National Veterinary Institute, Uppsala, SwedenSearch for more papers by this authorEleftherios Meletis, Eleftherios Meletis School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this authorSimona Iannetti, Simona Iannetti Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale"- National Reference Centre for Veterinary Epidemiology, Programming, Information and Risk Analysis, Teramo, 64100 ItalySearch for more papers by this authorSebastian Napp, Sebastian Napp IRTA., Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, SpainSearch for more papers by this authorAnn Lindberg, Ann Lindberg Dept. of Disease Control and Epidemiology, Swedish National Veterinary Institute, Uppsala, SwedenSearch for more papers by this authorNikolaos Solomakos, Nikolaos Solomakos School of Health Sciences, University of Thessaly, 43100 GreeceSearch for more papers by this author First published: 18 July 2019 https://doi.org/10.2903/sp.efsa.2019.EN-1678 Question number: EFSA-Q-2019-00244 Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Countries typically collect disease data in a way that is best suited for their specific needs. Therefore, differences exist in the sampling schemes and the diagnostic methods, which produce non-comparable data and, subsequently, non-comparable estimates of the prevalence of disease. The objective of the HOTLINE project was to make disease information comparable and interpretable across different sampling and testing settings. To serve this objective a series of Bayesian tools were developed and applied. Reporting guidelines aimed at promoting a more consistent approach to communication of animal health surveillance activities and their outputs, including what information must be reported to make true prevalence estimation feasible, have also been created. For tutorial purposes, an interactive web application was created to carry out Bayesian analysis of hierarchically structured prevalence data. E-lectures and training material for all models and methods are available through the free training session of our webpage with step by step explanations. Finally, a mailing list and LinkedIn group have been established to sustain a fruitful communication in the development and deployment of such methods. References Allaire JJ, Xie Y,McPherson J. Luraschi J, Ushey K, Atkins A.,Wickham H.Cheng J.Chang W. Iannone R 2018. rmarkdown: Dynamic Documents for R. R package version 1.11. URL https://rmarkdown.rstudio.com Branscum,A. J., Gardner,I. A., & Johnson,W. O. 2004. Bayesian modelling of animal-and herd-level prevalences. Preventive veterinary medicine, 66(1-4), 101- 112. Bobb JF, Dominici F, and Peng RD. 2011. A Bayesian Model Averaging Approach for Estimating the Relative Risk of Mortality Associated with Heat Waves in 105 U.S. Cities. Biometrics. 67(4): 1605- 1616. Comin A, Häsler B and Hoinville L, Peyre M, Dórea F, Schauer B, Snow L, Stärk KDC, Lindberg A and Brouwer A, van Schaik G, Staubach C, Schultz K, Bisdorff B, Goutard F, Pinto-Ferreira J, Conraths F, Cameron A, Martinez-Avilés M, Sanchez-Vizcaino J, Varan V, Traon D, Pinto J, Rushton J, Ripperger J and Pfeiffer D.U. 2016. RISKSUR Tools: taking animal health surveillance into the future through interdisciplinary integration of scientific evidence. Proceedings of the Annual meeting of the Society of Veterinary Epidemiology and Preventive Medicine, Elsinore, Denmark, March 16-18 2016: 243- 254. Cowles,M. K. 2013. Applied Bayesian statistics: with R and OpenBUGS examples (Vol. 98). Springer Science & Business Media. Chang W. and Borges Ribeiro B 2018. shiny dashboard: Create Dashboards with 'Shiny'. R package version 0.7.1. https://CRAN.R-project.org/package=shinydashboard Chang,W.Cheng,J. Allaire,JJ,Xie Y,McPherson J 2018. shiny: Web Application Framework for R. R package version 1.2.0. https://CRAN.R-project.org/package=shiny European Food Safety Authority (EFSA), 2015. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2015. EFSA Journal, 14(12). Garrett,RG. 2018. rgr: Applied Geochemistry EDA. R package version 1.1.15. https://CRAN.R-project.org/package=rgr Gupta RC, Albanese RA, Penn JW and White TJ. 1997. Bayesian estimation of relative risk in biomedical research. Environmetrics, 8: 133- 143. Hanson,T., Johnson,W. O., & Gardner I. A. 2003. Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard. Journal of agricultural, biological, and environmental statistics, 8(2), 223. Hora SC and Kelley GD. 1983. Bayesian inference on the odds and risk ratios. Communications in statistics. Theory and methods. 12 (6): 681- 692. Kostoulas,P. 2018. PriorGen: Generates Prior Distributions for Proportions. R package version 1.1.2. https://CRAN.R-project.org/package=PriorGen Liapi M, Leontides L, Kostoulas P, Botsaris G, Iacovou Y, Rees C, Naseby DC, 2011. Bayesian estimation of the true prevalence of Mycobacterium avium subsp. paratuberculosis infection in Cypriot dairy sheep and goat flocks. Small Rumin Res, 95, 174- 178. R Core Team 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ Rogan,W. J., & Gladen,B. (1978). Estimating prevalence from the results of a screening test. American journal of epidemiology, 107(1), 71- 76. Schauer B, Martínez-Avilés M, Comin A, Rodríguez Prieto V, Dórea F, Häsler B, Bisdorff B, Schulz K, Conraths FJ and Staubach C, 2014. Surveillance is a "public good" - but how public is it? Presented at the EPIZONE 8th Annual Meeting in Session 8: Epidemiology; 23 - 25 September 2014, DGI-Byen, Copenhagen, Denmark. Soetaert K. and P.M.J. Herman 2009. A Practical Guide to Ecological Modelling. Using R as a Simulation Platform. Springer, 372 pp. Soetaert K. 2009. rootSolve: Nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations. R-package version 1.6 Sturtz,S., Ligges,U., and Gelman,A. 2005. R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software, 12(3), 1- 16. Wickham H 2007. Reshaping Data with the reshape Package. Journal of Statistical Software, 21(12), 1- 20. URL http://www.jstatsoft.org/v21/i12/. Wickham,H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. Wickham,H François,R. Henry L. Müller K. 2018. dplyr: A Grammar of Data Manipulation. R package version 0.7.8.https://CRAN.R-project.org/package=dplyr Xie,Y.Allaire JJ.Grolemund G 2018. R Markdown: The Definitive Guide. Chapman and Hall/CRC. ISBN 9781138359338. https://bookdown.org/yihui/rmarkdown Yihui Xie, Joe Cheng and Xianying Tan 2018. DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.5. https://CRAN.R-project.org/package=DT Supporting Information Filename Description efs31678e-sup-0001-Annex_A.pdfPDF document, 84 KB efs31678e-sup-0002-Annex_B.pdfPDF document, 280 KB efs31678e-sup-0003-Annex_C.pdfPDF document, 739.3 KB efs31678e-sup-0004-Annex_D.xlsxMS Excel, 6.5 KB Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. 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