The aim of the first study was to estimate the economic results in a dairy herd related to different mastitis control strategies, which covered prevention and treatment of clinical and subclinical mastitis caused by Staphylococcus aureus, Streptococcus agalactiae and Eschericia coli, by use of the Monte Carlo simulation technique.
For the purpose of the study a mastitis module with the ability to model contagious transmission and include more mastitis pathogens simultaneously was developed and applied to the existing dynamic, mechanistic and stochastic simulation model SimHerd. We concluded that the simulated indicator-based treatment strategies were profitable. We also found that the total cost of clinical and subclinical mastitis was highly sensitive to the transmission rate of contagious pathogens.
The second study was aimed at quantifying the sensitivity to changes in transmission rate and test sensitivity of the mastitis indicator on the technical and economic simulation outputs in control strategies with focus on test-and-treat and test-and-cull to control contagious mastitis. The new mastitis module was developed to include dry cow treatment in combination with lactational treatment, as well as the possibility to change mastitis test sensitivity and the opportunity to cull infected cows immediately after the infection has been discovered. In conclusion, the study confirmed that transmission rate and cure rate are important in relation to mastitis control.
These simulation studies showed the economic importance of controlling mastitis pathogens and reducing contagious transmission. Identified efficient methods for controlling the spreading of contagious pathogens were based on detection of subclinical infections. Various indicators of subclinical mastitis exist, each with different advantages and drawbacks. The aim of the third study was to examine whether time series of cow level l-lactate dehydrogenase (LDH) measurements are useful to indicate subclinical mastitis. Thus, in-line measurements of LDH from 83 cows in five herds were modelled using a Dynamic Linear Model to develop a method for detection of subclinical S. aureus infections. The variance parameters were estimated using the Expectation-Maximization algorithm and the method used for classification of cows as infected or non-infected was based on a Multi Process Kalman Filter. Bacteriological culturing of milk samples collected was used as gold standard. Model sensitivity was 36.0% and specificity was 82.6%, which indicated poor performance of the method. Results indicated that there is much variation between cows in immune response to subclinical mammary infection, which may complicate the detection of subclinical infections in cows. In conclusion, the milk profile of LDH seemed to reflect the host response to infection rather than the infection, i.e. the presence of pathogens in the mammary gland.
The results of the Ph.D. thesis clearly demonstrate the potential of efficient detection and control of especially contagious pathogens that cause subclinical mastitis. Future studies should therefore aim at the development of cost effective test methods to detect subclinical mastitis. In addition, more knowledge is needed on how the economic consequences of different strategies against contagious pathogens can be estimated in concrete herds in practice for decision-making purposes.