Abstract
The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
Highlights
Studies have shown variability among cows for natural resistance to intramammary infection (IMI)
Let ztk 1⁄4 0 if ytk is from an unknown IMIÀ sample and ztk 1⁄4 1 if ytk is from an unknown IMI+ sample
Convergence rates were checked with an EM algorithm and the Gibbs sampler on models similar to those used in the simulation of this study but without genetic covariance structure (SCSi = Mili + ei)
Summary
Studies have shown variability among cows for natural resistance to intramammary infection (IMI). Since these are usually unidentified, breeding values tend to be biased. To reduce this bias and to infer more precisely the cows’ individual probabilities to be IMIÀ or IMI+, several authors have used the mixture model methodology on SCS [2,9,12,17]. A generalization of the mixture model is the hidden Markov model (HMM) that presents the advantages of estimating individual probabilities of being infected and of predicting individual probabilities of new infection and of recovery Both are useful to compute epidemiological measures of IMI spread within a population and to assist mastitis control programs
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