A hidden Markov model to predict early mastitis from test-day somatic cell scores.

In many countries, high somatic cell scores (SCS) in milk are used as an indicator for mastitis because they are collected on a routine basis. However, individual test-day SCS are not very accurate in identifying infected cows. Mathematical models may improve the accuracy of the biological marker by making better use of the information contained in the available data. Here, a simple hidden Markov model (HMM) is described mathematically and applied to SCS recorded monthly on cows with or without clinical mastitis to evaluate its accuracy in estimating parameters (mean, variance and transition probabilities) under healthy or diseased states. The SCS means were estimated at 1.96 (s.d. = 0.16) and 4.73 (s.d. = 0.71) for the hidden healthy and infected states, and the common variance at 0.83 (s.d. = 0.11). The probability of remaining uninfected, recovering from infection, getting newly infected and remaining infected between consecutive test days was estimated at 78.84%, 60.49%, 11.70% and 15%, respectively. Three different health-related states were compared: clinical stages observed by farmers, subclinical cases defined for somatic cell counts below or above 250 000 cells/ml and infected stages obtained from the HMM. The results showed that HMM identifies infected cows before the appearance of clinical and subclinical signs, which may critically improve the power of the studies on the genetic determinants of SCS and reduce biases in predicting breeding values for SCS.