..and Gods hand struck across the country.

Rome, 20 June 2010

Brucellosis in cattle in Uganda

AreaprevalenceTestSourceCentral and Southern Uganda14% RBTNakavuma et al., 1994MbararaHerd 55.6%, indiv 15.8%, within herd 1-90%RBTFaye et al., 2005Central and Western UgandaHerd 100% (pastoral), 5.5.% zero grazingC-ELISAMagona et al., 2009Various districts10% indiv animalMwebe et al., 2011KampalaMean Herd 6.5% & within herd 25.9%C-ELISAMakita et al., 2011Luwero, Nakasongola1.2-4.7%RBT & C-ELISANizeyimana et al., 2013

Field survey of brucellosis in buffaloes in Murchison Falls, Queen Elizabeth, Kidepo Valley and Lake Mburo National Parks in Uganda

SamplingTarget numbers were: Lake Mburo 11, Queen Elizabeth 149, Kidepo 28, Murchison 110 serum samplesHerds were identified and target animals randomly selected and then darted followed by blood collection from jugular or tail veinSeroanalysis was done using RBT and C-ELISA

ResultsOverall percentage prevalence of Brucella antibodies in buffaloes was 21.57%. Queen Elizabeth National Park 26.67% Kidepo Valley National Park 26.19% Murchison Falls National Park 19.84%. Lake Mburo National Park had lowest prevalence 1.82%.

Nature of veterinary dataProblem of variabilityE.g. Milk yield WeightHeightAntibody titres

Data:(1) Nominal:Generate counts:Single counts (e.g. number of animals)Proportions (e.g. prevalence; mortality)

Data:(2) Ordinal:General counts: of ranked data(e.g. body condition score; score of clinical severity)Not true measurementsNot continuous dataSuitable measure of central tendency = Median (not mean)

Data

Data:

(3) Continuous

Variability:Demonstrated in single samples for continuous data (and ordinal data)And in several samples for continuous ordinal and nominal data (e.g. proportions, prevalence)

Relevant to samplingOne-sample case (e.g. field surveys of disease prevalence)Three or more samplesVariability:Two-sample case (e.g. comparing two groups: milk yield, weight gain, disease in relation to risk factors)

Weight (kg)Number of pigs

Median (Q2) = 50th percentileStandard deviation (s)Central tendancySpreadSemi-interquartile range (SIR)SIR = (Q3-Q1) 2Where Q1 = 25th percentile (lower quartile) Q3 = 75th percentile (upper quartile)

The Normal distribution

Properties of the Normal distribution95% of values lie between -1.96 and + 1.96(z = 1.96)For 90%, z = 1.645For 99%, z = 2.576

Population values take Greek lettersNote convention: Samples provide only estimates of population values Sample values take Roman letterse.g. Population mean = Sample standard deviation = sPopulation standard deviation = The bigger the sample, the more precise sample estimate

YESDo sample means have distributions??

The Normal distribution

YES!Do sample means have distributions?The distribution is Normal, too!The standard deviation of the mean is called the standard error of the mean (SEM) to avoid confusion with the standard deviation of the individual values in a sampleThe SEM can be estimated from a single sample:SEM = s/n

A User-Friendly way of describing a range within which a population value lies, with defined probablityConfidence intervals For Normally distributed data, uses the properties of the distribution Based on the standard error Correction for variability induced by the sample size

Confidence limits: (1) On 95% of occasions: (2) In a single sample:

-3-2-10123Proportion of values of x/unit0.10.20.30.40.50.60.7xNormal distributiont distributionlognormal distribution

Include:Other distributions:BinomialPoisson

Normal approximations:Acceptable with large samples:Leading to asymptotic (large sample), as opposed to exact, methodsOther approximations:Poisson to the binomial

A Hat: Additional sample notation:

samples may be indicated byAn asterisk: *

**********************