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

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 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 1

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% 2. Physical Fracture, lesion to the claw wall, lesion to the claw sole <1% 50-80% 3. Inherited Osteochondrosis manifesta, osteochondrosis dissecans 70% 30% Control strategies Control strategies against leg disorders will depend on the cause category • Infectious leg disorders Antibiotics • Physical leg disorders Reconstructing the pen • Inherited leg disorders Boar semen Weight gain 2

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 Cheap Cheap • Clinical investigation • Bacteriological investigation Expensive • Pathological investigation Expensive To make a herd diagnosis of leg disorders Challenges • What information to use • How much information to use • How to collect the information 3

The ”PigLame” model Purpose of the model • To estimate probability distributions of different manageable causes of leg disorders in finisher herds Herd Pig Info Info Leg disorder Strategy Strategy Strategy 1 2 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 4

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 5

Feed strat Breed Pen Herd Produc- Pur- Floor Straw Gain Den Size tion chase Physical Physical Infectious Infectious Inherited Inherited Herd class Physical Infectious Inherited Gen der LMP Frac Claw Claw Hae Myco Strep Erysi OCM OCD ture Wall Sole mo Pig Lame Obs C3 P3 C4 P4 B1 C5 P5 B2 C6 P6 B3 C7 P7 B4 C8 P8 C9 P9 C1 P1 C2 P2 Lame Pig class Herd class Floor Floor Pen Den Pen Den Gain Gain …… Physical Infectious Inherited Frac Claw Claw Claw Frac Claw Myco Myco Strep Strep Erysi Erysi Haemo Haemo OCM OCM OCD OCD Sole ture ture Wall Wall Sole Pig class 6

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 Herd class Floor Floor Pen Den Gain …… Physical Infectious Inherited Frac Frac Claw Claw Myco Strep Erysi Haemo OCM OCD Sole ture ture Wall Pig class Always (almost) 100 Example: P(Fracture|fully slatted floors) 85 Usually 75 Consider 100 pigs examined individually Often at a herd visit. The herd has fully slatted floors in the pens. As often as not 50 How often do you, during the examination expect to find a pig with a fracture? Sometimes 25 Once in a while 15 (Almost) never 0 7

Elicitation of probabilities R 1 R 2 R n I Pig class D 1 D n D 2 Elicitation of probabilities Cause-categories: • Defined as Risk Index I on an arbitrary scale • 0: Low risk • 9: High risk Elicitation of probabilities R 1 R 1 R 2 R 2 R n R n I Pig class D 1 D n D 2 8

Elicitation of probabilities Risk index based on a linear equation • I the resulting risk index • μ the intercept • ρ k the systematic effect of risk factor k • ε random residuals • Assumptions: • No interactions between the risk factors • The effects are additive Elicitation of probabilities R 1 R 2 R n I Pig class D 1 D n D 2 Elicitation of probabilities Leg disorder nodes: Modeled using a logistic regression ( ( )) = α + β Logit P D I k k k • Logit(P(D k )): Logistic transformation of the conditional probability of 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 9

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 10

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 11

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 • 12