Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks – Peaks, growths, and foresight in swine production

Autoři: Ana Carolina Lopes Antunes aff001;  Vibeke Frøkjær Jensen aff001;  Dan Jensen aff002
Působiště autorů: Division for Diagnostics & Scientific Advice–Epidemiology, National Veterinary Institute/Centre for Diagnostics–Technical University of Denmark, Lyngby, Denmark aff001;  Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg C, Denmark aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223250


As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

Klíčová slova:

Age groups – Antibiotics – Death rates – Disease surveillance – Epidemiology – Livestock – Swine – Veterinary diseases


1. Salman M. Animal Disease Surveillance and Survey Systems: Methods and Applications. 1st Editio. Iowa: Blackwell Publishing; 2003.

2. Buckeridge DL. Outbreak detection through automated surveillance: A review of the determinants of detection. J Biomed Inform. 2007;40:370–9. doi: 10.1016/j.jbi.2006.09.003 17095301

3. Khoury MJ, Ioannidis JPA. Big data meets public health. Science. 2014;346:1054–5. doi: 10.1126/science.aaa2709 25430753

4. Chan EH, Sahai V, Conrad C, Brownstein JS. Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance. PLoS Negl Trop Dis. 2011;5:e1206. doi: 10.1371/journal.pntd.0001206 21647308

5. Martin LJ, Lee BE, Yasui Y. Google Flu Trends in Canada: a comparison of digital disease surveillance data with physician consultations and respiratory virus surveillance data, 2010–2014. Epidemiol Infect. 2016;144:325–32. doi: 10.1017/S0950268815001478 26135239

6. Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big Data for Infectious Disease Surveillance and Modeling. J Infect Dis. 2016;214 suppl 4:S375–9. doi: 10.1093/infdis/jiw400 28830113

7. Houe H, Gardner I, Nielsen L. Use of information on disease diagnoses from databases for animal health economic, welfare and food safety purposes: strengths and limitations of recordings. Acta Vet Scand. 2011;53 Suppl 1:S7.

8. Stevens KB, Pfeiffer DU. Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems. Spat Spatiotemporal Epidemiol. 2015;13:15–29. doi: 10.1016/j.sste.2015.04.003 26046634

9. Gates MC, Holmstrom LK, Biggers KE, Beckham TR. Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities. Front public Heal. 2015;3:74.

10. VanderWaal K, Morrison RB, Neuhauser C, Vilalta C, Perez AM. Translating Big Data into Smart Data for Veterinary Epidemiology. Front Vet Sci. 2017;4:110. doi: 10.3389/fvets.2017.00110 28770216

11. Birkegård AC, Fertner ME, Jensen VF, Boklund A, Toft N, Halasa T, et al. Building the foundation for veterinary register-based epidemiology: A systematic approach to data quality assessment and validation. Zoonoses Public Health. 2018;65:936–46. doi: 10.1111/zph.12513 30105809

12. Union European. Directive 2000/15/EC of the European Parliament and the Council of 10 April 2000 amending Council Directive 64/432/EEC on health problems affecting intra-Community trade in bovine animals and swine. 2000.

13. Food and Agriculture Organization of the United Nations (FAO). Drivers, dynamics and epidemiology of antimicrobial resistance in animal production. 2016.

14. Stege H, Bager F, Jacobsen E, Thougaard A. VETSTAT—the Danish system for surveillance of the veterinary use of drugs for production animals. Prev Vet Med. 2003;57:105–15. 12581594

15. Lopes Antunes AC, Dórea F, Halasa T, Toft N. Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms. Prev Vet Med. 2016;127. doi: 10.1016/j.prevetmed.2016.07.015

16. Backer JAA, Brouwer H, van Schaik G, van Roermund HJWJW, Thulke H-H. Using mortality data for early detection of Classical Swine Fever in The Netherlands. Prev Vet Med. 2011;99:38–47. doi: 10.1016/j.prevetmed.2010.10.008 21081252

17. Lopes Antunes AC, Ersbøll AK, Bihrmann K, Toft N. Mortality in Danish Swine herds: Spatio-temporal clusters and risk factors. Prev Vet Med. 2017;145. doi: 10.1016/j.prevetmed.2017.06.013

18. Frøkjaer Jensen V, Enøe C, Wachmann H, Nielsen EO. Antimicrobial use in Danish pig herds with and without postweaning multisystemic wasting syndrome. Prev Vet Med. 2010;95:239–47. doi: 10.1016/j.prevetmed.2010.04.001 20471123

19. Vigre H, Dohoo IR, Stryhn H, Jensen VF. Use of register data to assess the association between use of antimicrobials and outbreak of Postweaning Multisystemic Wasting Syndrome (PMWS) in Danish pig herds. Prev Vet Med. 2010;93:98–109. doi: 10.1016/j.prevetmed.2009.10.010 19939482

20. DANMAP. DANMAP. 2015.

21. Ostersen T, Cornou C, Kristensen AR. Detecting oestrus by monitoring sows’ visits to a boar. Comput Electron Agric. 2010;74:51–8. doi: 10.1016/j.compag.2010.06.003

22. Antunes ACL, Jensen D, Halasa T, Toft N. A simulation study to evaluate the performance of five statistical monitoring methods when applied to different timeseries components in the context of control programs for endemic diseases. PLoS One. 2017;12.

23. Jensen DB, van der Voort M, Hogeveen H. Dynamic forecasting of individual cow milk yield in automatic milking systems. J Dairy Sci. 2018;101:10428–39. doi: 10.3168/jds.2017-14134 30172403

24. SEGES Danish Pig Research Centre. 2015.

25. Jensen VF, de Knegt L V., Andersen VD, Wingstrand A. Temporal relationship between decrease in antimicrobial prescription for Danish pigs and the “Yellow Card” legal intervention directed at reduction of antimicrobial use. Prev Vet Med. 2014;117:554–64. doi: 10.1016/j.prevetmed.2014.08.006 25263135

26. R Core Team. R: A language and environment for statistical computing. 2017. https://www.r-project.org/.

27. Sørensen KJ, Bøtner A, Smedegaard Madsen E, Strandbygaard B, Nielsen J. Evaluation of a blocking Elisa for screening of antibodies against porcine reproductive and respiratory syndrome (PRRS) virus. Vet Microbiol. 1997;56:1–8. doi: 10.1016/s0378-1135(96)01345-4 9228677


29. Bøtner A, Nielsen J, Bille-Hansen V. Isolation of porcine reproductive and respiratory syndrome (PRRS) virus in a Danish swine herd and experimental infection of pregnant gilts with the virus. Vet Microbiol. 1994;40:351–60. doi: 10.1016/0378-1135(94)90122-8 7941298

30. Berger SS, Lauritsen KT, Boas U, Lind P, Andresen LO. Simultaneous detection of antibodies to five Actinobacillus pleuropneumoniae serovars using bead-based multiplex analysis. J Vet Diagnostic Investig. 2017;29:797–804.

31. Nielsen R, Plambeck T, Foged NT. Blocking enzyme-linked immunosorbent assay for detection of antibodies to Actinobacillus pleuropneumoniae serotype 2. J Clin Microbiol. 1991;29:794–7. 1890179

32. Klausen J, Ekeroth L, Grøndahl-Hansen J, Andresen LO. An indirect enzyme-linked immunosorbent assay for detection of antibodies to Actinobacillus Pleuropneumoniae serovar 7 in pig serum. J Vet Diagnostic Investig. 2007;19:244–9.

33. Klausen J, Andresen LO, Barfod K, Sørensen V. Evaluation of an enzyme-linked immunosorbent assay for serological surveillance of infection with Actinobacillus pleuropneumoniae serotype 5 in pig herds. Vet Microbiol. 2002;88:223–32. doi: 10.1016/s0378-1135(02)00111-6 12151197

34. Jensen VF, Jacobsen E, Bager F. Veterinary antimicrobial-usage statistics based on standardized measures of dosage. Prev Vet Med. 2004;64:201–15. doi: 10.1016/j.prevetmed.2004.04.001 15325773

35. Jensen DB, Toft N, Kristensen AR. A multivariate dynamic linear model for early warnings of diarrhea and pen fouling in slaughter pigs. Comput Electron Agric. 2017;135:51–62. doi: 10.1016/j.compag.2016.12.018

36. Jensen DB, Hogeveen H, De Vries A. Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis. J Dairy Sci. 2016;99:1–18. doi: 10.3168/jds.2015-9445

37. West M, Harrison J. Bayesian Forecasting and Dynamic Models. 2nd Ed. New York, USA: Springer; 1997.

38. Jensen DB, Cornou C, Toft N, Kristensen AR. A multi-dimensional dynamic linear model for monitoring slaughter pig production. In: Precision Livestock Farming 2015—Papers Presented at the 7th European Conference on Precision Livestock Farming, ECPLF 2015. 2015.

39. Jensen DB, Toft N, Kristensen AR. A multivariate dynamic linear model for early warnings of diarrhea and pen fouling in slaughter pigs. Comput Electron Agric. 2017.

40. Dominiak KN, Kristensen AR. Prioritizing alarms from sensor-based detection models in livestock production—A review on model performance and alarm reducing methods. Comput Electron Agric. 2017;133:46–67. doi: 10.1016/j.compag.2016.12.008

41. Hogeveen H, Kamphuis C, Steeneveld W, Mollenhorst H. Sensors and clinical mastitis—the quest for the perfect alert. Sensors (Basel). 2010;10:7991–8009. doi: 10.3390/s100907991 22163637

42. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5. doi: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3 15405679

43. Montgomery D. Control charts for attributes. In: Welter J, Dumas S, Sapira L, Owens M, editors. Introduction to statistical quality control. 6th edition. Arizona State University: John Wiley & Sons, Inc.; 2009. p. 288–343.

44. Antunes ACL, Jensen D, Halasa T, Toft N. A simulation study to evaluate the performance of five statistical monitoring methods when applied to different timeseries components in the context of control programs for endemic diseases. PLoS One. 2017;12:1–18.

45. Radostits OM, Gay CC, Blood DC, Hinchcliff KW. Veterinary Medicine- A textbook of the diseases of cattle, horses, sheep, pigs and goats. 9th Ed. W.B. Saunders Company Ltd; 2000.

46. Lopes Antunes AC, Jensen VF, Toft N. Outcomes From Using Mortality, Antimicrobial Consumption, and Vaccine Use Data for Monitoring Endemic Diseases in Danish Swine Herds. Front Vet Sci. 2019;6:41. doi: 10.3389/fvets.2019.00041 30854377

47. Lopes Antunes AC, Halasa T, Lauritsen KT, Kristensen CS, Larsen LE, Toft N. Spatial analysis and temporal trends of porcine reproductive and respiratory syndrome in Denmark from 2007 to 2010 based on laboratory submission data. BMC Vet Res. 2015;11:303. doi: 10.1186/s12917-015-0617-0 26689831

48. Bøtner A. Diagnosis of PRRS. Vet Microbiol. 1997;55:295–301. doi: 10.1016/s0378-1135(96)01333-8 9220625

Článek vyšel v časopise


2019 Číslo 10