MiCull2 -- simulating mastitis transmission through milking order
Contagious mastitis pathogens can be transmitted through milking. However, previously published simulation models, such as MiCull, have not directly taken this into account. We have reimplemented the MiCull model to model transmission of contagious mastitis pathogens through milking in a milking parlor. This additional complexity requires a substantial increase in computations and a need to structure the program code to make it more flexible for future use. The aim of this paper was threefold: First, to implement the new model in a faster programming language; secondly, to describe the new model, in particular transmission of a contagious mastitis pathogen through milking; and thirdly, to compare three different milking order strategies in regards to prevalence and incidence of intramammary infections. For each scenario, 500 herds with 200 cows each were simulated over 10 years. The model was calibrated using available mastitis parameters from the literature. We hypothesized that milking order should have a considerable effect on disease transmission, especially if the infected cows with clinical enter the milking parlor first and thereby have a high risk of infecting the following cows. The milking order scenarios examined were random milking order and milking clinical cases first, or last. Unexpectedly, there were no large differences between these scenarios for reasonably sized infection rates corresponding to a herd with a moderate level of clinical mastitis in the herd. Larger differences are expected to be found in herds with very high infection rates. We have developed a transmission simulation model of mastitis pathogens using a new mode of transmission by milking order. We expect that this new version of MiCull will be useful for both researchers and advisors since it is flexible, can be fitted to various in-herd situations and the computations are fast.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Supervised Learning Model for Key Frame Identification from Cow Teat Videos
This paper proposes a method for improving the accuracy of mastitis risk assessment in cows using neural networks and video analysis. Mastitis, an infection of the udder tissue, is a critical health problem for cows and …
Using runs of homozygosity to detect genomic regions associated with susceptibility to infectious and metabolic diseases in dairy cows under intensive farming conditions
Runs of homozygosity (ROH) are contiguous stretches of homozygous genome which likely reflect transmission from common ances- tors and can be used to track the inheritance of haplotypes of interest. In the present paper,…
Can We Detect Mastitis earlier than Farmers?
The aim of this study was to build a modelling framework that would allow us to be able to detect mastitis infections before they would normally be found by farmers through the introduction of machine learning techniques…
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows
Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance. The use of AI techniques to identify the early onset of this disease, thus has significant implicati…
Decision MakingMilking CowMask for Semi-Supervised Image Classification
Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbat…
ClassificationGeneral Classificationimage-classificationImage Classification+1