VFD standard skabelon

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Campylobacter Risk Assessment in Poultry

Helle Sommer, Bjarke Christensen, Hanne
Rosenquist, Niels Nielsen and Birgit Nørrung
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Probability of Exposure
P r e v a l e n s
Pfarmh.
SLAUGHTERHOUSE
RETAIL
CONSUMER
RISK
Ca.bleeding
C o n c e n t r a t i o n
Probability of Infection
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Slaughter house modules

• Data examinations distributions
• Process model building explicit equations
• Explicit equations/ simulations
• Cross contamination
• What-if-simulations

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Data examinations

• Data for 3 different purposes
• - prevalence distribution -gt slaughterhouse
  program
• - concentration distribution
• - model building, before and after a process

• From mean values to a distribution

• Lognormal/ normal gt illustrations

• Same or different distributions gt variance
  analysis

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From mean values to a distribution
17 log mean values from different flocks and from
2 different studies
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From mean values to a distribution
17 distributions -gt one common distribution
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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
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Real data set
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New Danish data
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Building mathematical models
Slaughterhouse process
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Why new mathematical process models ?
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Explicit mathematical process model
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Explicit mathematical process model
In normal scale µy µx / ?µ 100 10000/100 In
log scale µlogy µlogx ?µ 2 4 - 2
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Explicit mathematical process model
In normal scale µy µx / ?µ 100 10000/100 In
log scale µy µx ?µ 2 4 - 2 sy2 ß2 sx2
Transformation line y ? ßx
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Explicit mathematical process model
Overall model µy µx - ?µ sy2 ß2 sx2 Local
model Y ? ßx
Calculation of ? ? (1-ß) µx- ?µ
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Explicit mathematical process model
In normal scale µy / µx 158 In log scale µy
µx - 2.2
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Explicit mathematical process model
In normal scale y x z z ? N (µ, s)
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Summing up

• Data knowledge/logical assumptions of the
  process -gt 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)
  -gt different or same distribution ? (mean and
  shape)

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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.