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Active animal health surveillance in European Union Member States: gaps and opportunities

  • B. BISDORFF (a1), B. SCHAUER (a2) (a3), N. TAYLOR (a1) (a4), V. RODRÍGUEZ-PRIETO (a5), A. COMIN (a6), A. BROUWER (a7), F. DÓREA (a6), J. DREWE (a1), L. HOINVILLE (a1), A. LINDBERG (a6), M. MARTINEZ AVILÉS (a5), B. MARTÍNEZ-LÓPEZ (a5) (a8), M. PEYRE (a9), J. PINTO FERREIRA (a10), J. RUSHTON (a1), G. VAN SCHAIK (a11), K. D. C. STÄRK (a10), C. STAUBACH (a2), M. VICENTE-RUBIANO (a5), G. WITTEVEEN (a11), D. PFEIFFER (a1) and B. HÄSLER (a1) (a12)...

Animal health surveillance enables the detection and control of animal diseases including zoonoses. Under the EU-FP7 project RISKSUR, a survey was conducted in 11 EU Member States and Switzerland to describe active surveillance components in 2011 managed by the public or private sector and identify gaps and opportunities. Information was collected about hazard, target population, geographical focus, legal obligation, management, surveillance design, risk-based sampling, and multi-hazard surveillance. Two countries were excluded due to incompleteness of data. Most of the 664 components targeted cattle (26·7%), pigs (17·5%) or poultry (16·0%). The most common surveillance objectives were demonstrating freedom from disease (43·8%) and case detection (26·8%). Over half of components applied risk-based sampling (57·1%), but mainly focused on a single population stratum (targeted risk-based) rather than differentiating between risk levels of different strata (stratified risk-based). About a third of components were multi-hazard (37·3%). Both risk-based sampling and multi-hazard surveillance were used more frequently in privately funded components. The study identified several gaps (e.g. lack of systematic documentation, inconsistent application of terminology) and opportunities (e.g. stratified risk-based sampling). The greater flexibility provided by the new EU Animal Health Law means that systematic evaluation of surveillance alternatives will be required to optimize cost-effectiveness.

Corresponding author
*Author for correspondence: Dr B. Schauer, Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Strasse 48, 17487 Greifswald, Germany. (Email:
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† These authors contributed equally to this work.
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2. LJ Hoinville , Proposed terms and concepts for describing and evaluating animal-health surveillance systems. Preventive Veterinary Medicine 2013; 112: 112.

6. ED Van Asselt , Overview of available methods for risk based control within the European Union. Trends in Food Science & Technology 2012; 23: 5158.

8. AR Cameron . The consequences of risk-based surveillance: Developing output-based standards for surveillance to demonstrate freedom from disease. Preventive Veterinary Medicine 2012; 105: 280286.

9. F De Massis , A Petrini , A Giovannini . Reliability evaluation of sampling plan fixed by Council Directive 91/68/EEC for the maintenance of officially brucellosis-free flock status. Journal of Veterinary Medicine. B, Infectious Diseases and Veterinary Public Health 2005; 52: 284290.

10. L Alban , Towards a risk-based surveillance for Trichinella spp. in Danish pig production. Preventive Veterinary Medicine 2008; 87: 340357.

11. PAJ Martin . Current value of historical and ongoing surveillance for disease freedom: surveillance for bovine Johne's disease in Western Australia. Preventive Veterinary Medicine 2008; 84: 291309.

12. M Greiner , A Dekker . On the surveillance for animal diseases in small herds. Preventive Veterinary Medicine 2005; 70: 223234.

13. M Reist , T Jemmi , KDC Stärk . Policy-driven development of cost-effective, risk-based surveillance strategies. Preventive Veterinary Medicine 2012; 105: 176484.

14. L Alban , Towards a standardised surveillance for Trichinella in the European Union. Preventive Veterinary Medicine 2011; 99: 148160.

15. ME Schuppers , Implementing a probabilistic definition of freedom from infection to facilitate trade of livestock: putting theory into praxis for the example of bovine herpes virus-1. Preventive Veterinary Medicine 2012; 105: 195201.

16. A Alba , Assessment of different surveillance systems for avian influenza in commercial poultry in Catalonia (North-Eastern Spain). Preventive Veterinary Medicine 2010; 97: 107118.

17. D Böhning , M Greiner . Modelling cumulative evidence for freedom from disease with applications to BSE surveillance trials. Journal of Agricultural, Biological, and Environmental Statistics 2006; 11: 280295.

18. M-J Martinez , Methodological approach for substantiating disease freedom in a heterogeneous small population. Application to ovine scrapie, a disease with a strong genetic susceptibility. Preventive Veterinary Medicine 2010; 95: 108114.

19. A Foddai , Comparison of output-based approaches used to substantiate bovine tuberculosis free status in Danish cattle herds. Preventive Veterinary Medicine 2015; 121: 2129.

20. SJ More , Defining output-based standards to achieve and maintain tuberculosis freedom in farmed deer, with reference to member states of the European Union. Preventive Veterinary Medicine 2009; 90: 254267.

21. J Riviere , Sensitivity of bovine tuberculosis surveillance in wildlife in France: a scenario tree approach. PloS ONE 2015; 10.

22. H Wahlström , Demonstrating freedom from Mycobacterium bovis infection in Swedish farmed deer using non-survey data sources. Preventive Veterinary Medicine 2010; 94: 108118.

23. J Frössling , Surveillance system sensitivities and probability of freedom from Mycobacterium avium subsp. paratuberculosis infection in Swedish cattle. Preventive Veterinary Medicine 2013; 108: 4762.

24. SJ More , The effect of alternative testing strategies and bio-exclusion practices on Johne's disease risk in test-negative herds. Journal of Dairy Science 2013; 96: 15811590.

25. PAJ Martin , Demonstrating freedom from disease using multiple complex data sources 2: Case study – classical swine fever in Denmark. Preventive Veterinary Medicine 2007; 79: 98115.

26. S Welby , Bluetongue surveillance system in Belgium: a stochastic evaluation of its risk-based approach effectiveness. Preventive Veterinary Medicine 2013; 112: 4857.

27. L Alban , Comparison of risk-based versus random sampling in the monitoring of antimicrobial residues in Danish finishing pigs. Preventive Veterinary Medicine 2016; 128: 8794.

28. J Riviere , Bovine tuberculosis surveillance in cattle and free-ranging wildlife in EU Member States in 2013: a survey-based review. Veterinary Microbiology 2014; 173: 323331.

29. V Rodriguez-Prieto , Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations. Epidemiology and Infection 2015; 143: 20182042.

30. S Binder , Emerging infectious diseases: Public health issues for the 21st century. Science 1999; 284: 13111313.

36. DC Hadorn , SS Haracic , KDC Stärk . Comparative assessment of passive surveillance in disease-free and endemic situation: example of Brucella melitensis surveillance in Switzerland and in Bosnia and Herzegovina. BMC Veterinary Research 2008; 4: 19.

37. K Schulz , Hunters' acceptability of the surveillance system and alternative surveillance strategies for classical swine fever in wild boar – a participatory approach. BMC Veterinary Research 2016; 12.

38. C Calba , The added-value of using participatory approaches to assess the acceptability of surveillance systems: the case of bovine tuberculosis in Belgium. PLoS ONE 2016; 11.

40. FC Dorea , J Sanchez , CW Revie . Veterinary syndromic surveillance: current initiatives and potential for development. Preventive Veterinary Medicine 2011; 101: 117.

41. FC Dorea , Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts. PLoS ONE 2013; 8.

43. H Schwermer , I Reding , DC Hadorn . Risk-based sample size calculation for consecutive surveys to document freedom from animal diseases. Preventive Veterinary Medicine 2009; 92: 366372.

44. D Paolotti , Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clinical Microbiology and Infection 2014; 20: 1721.

48. KDC Stark , One Health surveillance – more than a buzz word? Preventive Veterinary Medicine 2015; 120: 124130.

49. A Binot , A framework to promote collective action within the One Health community of practice: using participatory modelling to enable interdisciplinary, cross-sectoral and multi-level integration. One Health 2015; 1: 4448.

52. KDC Staerk , B Haesler . The value of information: current challenges in surveillance implementation. Preventive Veterinary Medicine 2015; 122: 229234.

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