1 Leg disorders in finishers An economical problem for farmers due - - PDF document

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1 Leg disorders in finishers An economical problem for farmers due - - PDF document

Aug 25, 2023 247 likes •390 views

The PigLame Model An example of an Object- Oriented Bayesian network model Leonardo de Knegt Tina Birk Jensen Outline 1.The case problem 2.Modeling methods in general 3.Qualitative structure of the model 4.Elicitation of

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An example of an Object- Oriented Bayesian network model

Leonardo de Knegt Tina Birk Jensen

The “PigLame” Model

Outline 1.The case problem 2.Modeling methods in general 3.Qualitative structure of the model 4.Elicitation of probabilities 5.Use of the model Leg disorders in finishers

  • Leg disorders:

Any lesion or dysfunction of the leg or claw that might give rise to lameness

  • Lameness:

Deterioration in the gait and/or posture

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Leg disorders in finishers

An economical problem for farmers due to:

  • Increased work load
  • Cost of treatments
  • Reduced productivity
  • Risk of condemnations
  • A negative impact on animal welfare

Causes of leg disorders

  • 1. Infectious

Mycoplasma hyosynoviae, Erysipelothrix rhusiopathiae, Haemophilus parasuis, Streptococcus suis

  • 2. Physical

Fracture, lesion to the claw wall, lesion to the claw sole

  • 3. Inherited

Osteochondrosis manifesta, osteochondrosis dissecans

<2% 50-80% <1% 30% 70%

Control strategies

Control strategies against leg disorders will depend on the cause category

  • Infectious leg disorders
  • Physical leg disorders
  • Inherited leg disorders

Antibiotics Reconstructing the pen Boar semen Weight gain

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Useful information

Herd level

  • Herd size (number of pigs delivered)
  • Stocking density (high/low)
  • Floor type in pens (slatted/concrete)
  • Supply of straw in pens (deep/sparse/no)
  • Purchase policy (own piglets/1/>1)
  • Production type (sectioned/continuous)

Useful information

Pig level

  • Observe pigs from outside the pen
  • Clinical investigation
  • Bacteriological investigation
  • Pathological investigation

Cheap Cheap Expensive Expensive

To make a herd diagnosis of leg disorders

Challenges

  • What information to use
  • How much information to use
  • How to collect the information
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The ”PigLame” model

Purpose of the model

  • To estimate probability distributions of different

manageable causes of leg disorders in finisher herds

Strategy 2 Pig Info Leg disorder Strategy 1 Herd Info Strategy 3

Qualitative structure of the model

Characteristics

  • Based on information from the literature
  • All nodes are discrete
  • Each cause-category defined as a risk index I on an arbitrary

scale from 0 to 9

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Qualitative structure of the model

Characteristics Object-oriented structure

  • Ease the specification of the Bayesian network
  • Hierarchical structure
  • Two classes: Herd class and pig class

Object-oriented Bayesian network

The Pig class

  • Individual pig information

Gender Lean meat percentage Diagnostic test results Leg disorder

Object-oriented Bayesian network

The Herd class

Herd size Production Purchase Pen density Floor type Straw Cause category

Pig object Pig object Pig object

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Pen Den Floor Straw Physical Infectious Inherited Herd Size Produc- tion Gain Breed Feed strat Pur- chase

Herd class

Physical Infectious Inherited

Obs Lame Pig Lame Frac ture Claw Wall Claw Sole Myco Strep Erysi Hae mo OCM OCD Gen der LMP

Physical Infectious Inherited

C1 P1 C2 P2 C3 P3 C4 P4 B1 C5 P5 B2 C6 P6 B3 C7 P7 B4 C8 P8 C9 P9

Pig class

Physical Infectious Inherited

Floor Pen Den Gain

……

Frac ture Claw Wall Claw Sole Myco Strep Erysi Haemo OCM OCD

Herd class Pig class

Floor Pen Den Gain

Frac ture Claw Wall Claw Sole Myco Strep Erysi Haemo OCM OCD

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Elicitation of probabilities

The probabilities in the model are based on

  • 1. Results from published literature
  • Conversion of odds ratios to conditional probabilities
  • <40 conditional probabilities
  • 2. Expert opinions (9 experts)
  • >150 conditional probabilities
  • Not randomly distributed
  • Average of individual elicitations

Physical Infectious Inherited

Floor Pen Den Gain

……

Frac ture Claw Wall Claw Sole Myco Strep Erysi Haemo OCM OCD

Pig class Herd class

Floor

Frac ture

Example: P(Fracture|fully slatted floors)

Consider 100 pigs examined individually at a herd visit. The herd has fully slatted floors in the pens. How often do you, during the examination expect to find a pig with a fracture?

Always (almost) 15 25 50 75 85 100 Usually Often As often as not Sometimes Once in a while (Almost) never

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Elicitation of probabilities R2 R1 Rn I D1 D2 Dn

Pig class

Elicitation of probabilities

Cause-categories:

  • Defined as Risk Index I on an arbitrary scale
  • 0: Low risk
  • 9: High risk

Elicitation of probabilities R2 R1 Rn I D1 D2 Dn

Pig class

R2 R1 Rn

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Elicitation of probabilities

Risk index based on a linear equation

  • I

the resulting risk index

  • μ

the intercept

  • ρk the systematic effect of risk factor k
  • ε

random residuals

  • Assumptions:
  • No interactions between the risk factors
  • The effects are additive

Elicitation of probabilities R2 R1 Rn I D1 D2 Dn

Pig class

Elicitation of probabilities

Leg disorder nodes: Modeled using a logistic regression

  • Logit(P(Dk)): Logistic transformation of the conditional probability
  • f a pig to have the leg disorder k
  • α: Intercept indicating the base prevalence of the leg disorder k
  • β: Slope indicating the sensitivity to changes in the risk level of

the herd

  • I: Risk Index

I D P Logit

k k k

β α + = )) ( (

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Elicitation of probabilities

  • Parameter estimates for the Risk Index and leg disorder nodes found

by:

  • Using the probabilities elicited by experts or literature
  • Fitting a logistic linear model
  • Optimizing the fit

Use of the model

  • Decide on the level of information needed in order to identify the

most likely cause-category

  • Is it necessary to investigate individual pigs in a herd?
  • Which diagnostic test(s) should be performed?
  • How should pigs for diagnostic examination be selected?
  • How many pigs should be selected?

Use of the model

Two fictitious herds with same prevalence of lameness: 20% pigs are lame due to Mycoplasma hyosynoviae

  • Low risk herd:

Deliver 2000 finishers annually Sectioned production Produce own piglets Low pen densities Solid floors No supply of straw

  • High risk herd: Deliver 6000 finishers annually

Continuous production Purchase from several herds High pen densities Partially slatted floors Sparse supply of straw

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Use of the model

Different scenarios investigated:

  • 1. Herd evidence
  • 2. Herd evidence and observing 50 randomly selected pigs for lameness
  • 3. Herd evidence and performing diagnostic ex. of lame pigs
  • 4. Herd evidence and performing diagnostic ex. of all pigs

Risk index Risk index Risk index Risk index Risk index Risk index Risk index Risk index

Use of the model

  • Low risk herd
  • Necessary to perform diagnostic examination of pigs
  • High risk herd
  • Information regarding the herd characteristics is sufficient

More economic benefit in performing diagnostic examination of individual pigs in the low risk herd

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Conclusion

  • ”PigLame” model is an OOBN model
  • Ease the specification of the model
  • Suitable method of combining information from two

different levels

  • A similar approach can be used for other problems at

herd level

  • Probabilities mainly from experts
  • Prone to subjectivity