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Dr. Aaron Scott - Advantages of Probability-Based Approaches to Animal Disease Surveillance

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Advantages of Probability-Based Approaches to Animal Disease Surveillance - Dr. Aaron Scott, Center Director, USDA/APHIS/VS/CEAH, from the 2012 Annual Conference of the National Institute for Animal Agriculture, March 26 - 29, Denver, CO, USA.

More presentations at: http://www.trufflemedia.com/agmedia/conference/2012-decreasing-resources-increasing-regulation-advance-animal-agriculture

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Dr. Aaron Scott - Advantages of Probability-Based Approaches to Animal Disease Surveillance

  1. 1. NIAA March 27, 2012 Aaron Scott, DVM PhD DACVPM (epi) Center Director, NSU 1
  2. 2. 2Safeguarding Animal Health
  3. 3. 3
  4. 4. 4
  5. 5. 5
  6. 6. 6 Find disease if its not there… Prove it!!!
  7. 7. 7 Surveillance is evidence!
  8. 8. 8
  9. 9. 9 Evidence may be combination of sample testing and other knowledge about risk factors
  10. 10.  “Risk” = likelihood * consequence  = animals more likely to get disease?  = animals more likely to have disease?  = animals more likely to be detected?  = more dangerous to people???? 10
  11. 11. 11 …is drawing a conclusion about the population from one or more pieces of evidence
  12. 12. 12 Convenience Random Representative
  13. 13. 13 •Inference is based on random sampling •Listing of all subjects (sample frame) •Each has equal chance of selection •Is always representative
  14. 14. 14 “n” = 3X when 1:X 3,000,000 samples to detect 1 in 1,000,000
  15. 15. 15 A representative sample could be just anybody!
  16. 16. 16
  17. 17. 17
  18. 18. 18 If not random, must be other supporting evidence for representativeness…
  19. 19. 19 Concept: Every sample gives information Corollary: Some are worth more than others Targeted = intentional bias!
  20. 20. Must clearly define sub populations Must have knowledge of risk factors Must know magnitude of risk factors Targeting represents sub-populations Must know relationship 20
  21. 21. 3X
  22. 22. 24 40 H-risk * value 3 = 120 10 L-risk * value 0.5 = 5 50 tests = 125 “virtual” samples
  23. 23. 25 Intentionally biased sample + other information
  24. 24.  Sows and boars (value = 2.5x to 5x )  Exposure to feral pigs (10x ? 100x ?)  Non-confinement (??) Some samples from all populations Market swine (< 1x) 26
  25. 25. 27 From 3 herds: 5,000, 30, 10 Need “n” of 30/farm for inference 30 + 30 + 10 =70 samples Without additional info, n = 5,040!!!
  26. 26. Collect all with traceable ID Send to lab and sort Only knowledge is State of origin Discard all but 5% from each State 28
  27. 27. 29 Collaboration: • NPB, AASV, industry reps • 6 State Veterinarians • Veterinary Services
  28. 28. High value (feral swine exposure) Limit to number needed Collect “pink tag”, scan ID, combine zip with feral risk, target # needed Other tags Spend the money on other diseases 30
  29. 29. Safeguarding Animal Health 31 national.surveillance.unit@aphis.usda.gov http://www.aphis.usda.gov/vs/ceah/ncahs/nsu/ Thanks!!

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