1 / 22

Campylobacter Risk Assessment in Poultry

Campylobacter Risk Assessment in Poultry. Helle Sommer, Bjarke Christensen , Hanne Rosenquist, Niels Nielsen and Birgit Nørrung. Probability of Exposure. P farmh. P r e v a l e n s. SLAUGHTERHOUSE. RETAIL. CONSUMER. RISK. C a.bleeding. C o n c e n t r a t i o n.

tola
Download Presentation

Campylobacter Risk Assessment in Poultry

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Campylobacter Risk Assessment in Poultry Helle Sommer, Bjarke Christensen, Hanne Rosenquist, Niels Nielsen and Birgit Nørrung

  2. Probability of Exposure Pfarmh. P r e v a l e n s SLAUGHTERHOUSE RETAIL CONSUMER RISK Ca.bleeding C o n c e n t r a t i o n Probability of Infection

  3. Slaughter house modules • Data examinations – distributions • Process model building – explicit equations • Explicit equations/ simulations • Cross contamination • What-if-simulations

  4. Data examinations • Data for 3 different purposes • - prevalence distribution -> slaughterhouse program • - concentration distribution • - model building, before and after a process • From mean values to a distribution • Lognormal/ normal –>illustrations • Same or different distributions –>variance analysis

  5. From mean values to a distribution 17 log mean values from different flocks and from 2 different studies

  6. From mean values to a distribution 17 distributions -> one common distribution

  7. Log-normal or normal distribution ? ”True” data structure = simulated data (sim.=) Assumed distribution (dist.=) Published data = means of 4 samples,6 means from one study sim.= lognormal(6.9,2.3) dist.= normal or lognormal

  8. Normal scale Real data set

  9. New Danish data

  10. Building mathematical models Slaughterhouseprocess

  11. Why new mathematical process models ?

  12. Explicit mathematical process model

  13. Explicit mathematical process model In normal scale μy= μx / Δμ 100 = 10000/100 In log scale μlogy = μlogx – Δμ 2 = 4 - 2

  14. Explicit mathematical process model In normal scale μy = μx / Δμ 100 = 10000/100 In log scale μy = μx – Δμ 2 = 4 - 2 σy2 = β2 · σx2 Transformation line y =  + β·x

  15. Explicit mathematical process model Overall model μy = μx - Δμ σy2 = β2· σx2 Local model Y = + β·x Calculation of = (1-β)· μx- Δμ

  16. Explicit mathematical process model In normal scale μy / μx = 158 In log scale μy = μx - 2.2

  17. Explicit mathematical process model In normal scale y = x + z z Є N (μ, σ)

  18. Summing up • Data + knowledge/logical assumptions of the process -> multiplicativ or additive process • Explicit equations for modelling slaughterhouse processes + Monte Carlo simulations, modelling each chicken with a given status of infection, concentration level, order in slaughtering, etc. • New data of concentration (input distribution) -> different or same distribution ? (mean and shape)

  19. Advantage with explicit equations • Faster than simulations/Bootstrap/Jackknifing • Accounts for homogenization within flocks • More information along the slaughter line does not give rise to more uncertainty on the output distribution.

More Related