Mathematical modeling of the mastitis infection process

Area of Science:

• Veterinary Medicine
• Mathematical Biology
• Animal Science

Background:

• Mastitis is a complex biological process in dairy animals.
• Understanding its progression and impact on milk production is crucial for herd management.
• Stochastic elements and serial progression characterize mastitis infections.

Purpose of the Study:

• To develop a mathematical model for mastitis infection dynamics.
• To quantify the impact of mastitis on milk yield across different lactation stages.
• To identify specific infection states with the most significant productivity losses.

Main Methods:

• Utilized Markov chain theory to model the mastitis infection process.
• Defined four infection progression pathways based on lactation number (1st, 2nd, 3rd, 4th+).
• Developed two Markov matrices to represent transitions between seven distinct infection states.

Main Results:

• Calculated expected milk yield productivity for individual quarters across lactations: 0.93 (1st), 0.88 (2nd), 0.85 (3rd), and 0.84 (4th+).
• Determined that quarters with subclinical infections (infected but not clinical) exhibit the lowest milk yield productivity.
• The model successfully predicted lactational consequences of mastitis infection.

Conclusions:

• The developed Markov chain model provides a robust framework for analyzing mastitis progression.
• Mastitis significantly reduces milk yield, with cumulative effects across successive lactations.
• Early identification and management of subclinical mastitis are essential to mitigate economic losses in dairy production.