1. Sampling of Animal Populations Learning Objectives:  define & differentiate sampling  advantages/disadvantages sampling method sampling method  select sampling strategy

2. Data Sources For epidemiological analyses, must be: CompletenessValidityRepresentativeness

3. Accuracy, Refinement, Precision, Reliability and Validity (Thrusfield, 1986) Accuracy: investigation or measurement conforms to the truth Refinement: eq, 13 kg and 13,781 kg, both represent accurate, but the second is more refined than the first. Another eq., otitis externa, otitis externa by bacteria Precision: as a synonym of refinement and to indicate the concistency of a series measurements (repeated sampling)

4. Accuracy, Refinement………. Reliability (= reproducibility): produces similar results when its repeated Validity: measure what its supposed to measure, its long term characteristic of tehnique

5. Data can be collected as part of  Routine data collection: laboratory submissions, disease surveillance programmes, industry/farm data recording system  Structured data collection: regular monitoring of disease/production  Epidemiological studies

6. Data Collection Process  Whole population interest (=cencus)  It can be restricted to a sample : obtained more quickly, less expensive to collect, more accurate, more efficient

7. Samples Probability Samples : random selections Probability Samples : random selections Non-Probability Samples: a convenience sample, a purposive or judgmental sample Non-Probability Samples: a convenience sample, a purposive or judgmental sample

8. Probability Sampling  Simple Random Sampling  Systematic Sampling  Stratified Sampling  Cluster Sampling  Multistage Sampling

9. Simple Random Sampling Each individual has an equal probability of selection An individual’s selection doesn’t depend on others being selected, homogenous population Disadvantage : may result large variation, thereby requiring larger sample sizes Eq: flipping a coin, using random number tables Ex: catlle on farm

10. Systematic Random Sampling The n sampling units are selected from the sampling frame at the regular intervals The starting point in the first interval is selected on a formal random basis A practical way to obtain a representative sample It ensures that sampling units are distributed evenly over the entire population

11. Stratified Random Sampling The sampling frame is divided into strata, then a simple random or systematic random sample is selected within each stratum To be effective at reducing variation, for example: milk production in population of dairy cows of the Jersey and Holstein breeds. Genetic differences affecting milk volume between the two breeds. Definite strata, but homogenous within it

12. Cluster Sampling Sampling is applied at an aggregated level (=group) of individual units Groups or clusters such litters, pens, herds, artificial groupings (geographic areas) Can be selected by simple, systematic, or stratified random methods Groups with similar characteristics, but heterogenous within groups

13. Multistage Sampling Similar to cluster except that sampling takes place at all stages/at different hiererchical levels og aggregated units of interest Subsampling within the primary units (litters, pens, herds). A sampling of secondary units (e.q., animals) would be selected Often used as part of epidemiological studies Ex: cattle in region, to be sampled to determine TBC or mastitis prevalence

14. Sampling to detect disease Finite populations: Finite populations: n = [1-(1-β) ] [ ( N- d/2) + ½] n = [1-(1-β) ] [ ( N- d/2) + ½] Infinite populations (> 1000) Infinite populations (> 1000) n = [ log (1-β)] / [ log ( 1- d/N) ] n = [ log (1-β)] / [ log ( 1- d/N) ]