Biomedical subjects

M G G Chagunda

Publications and source records attributed to M G G Chagunda.

4 recordsLinked to original sources

PubMed2006

A model for detection of individual cow mastitis based on an indicator measured in milk.

A dynamic deterministic biological model was developed that generates, for a given cow on a given day, a value for her risk of having mastitis. The model combines real-time information from a mastitis indicator measured in milk with additional factors that are other known risk factors of mastitis but that are not reflected in the indicator. l-Lactate dehydrogenase (LDH), an enzyme whose activity is increased because of mastitis, is used as an example of a mastitis indicator. The additional factors incorporated in the model are days from calving, breed, parity, milk yield, udder characteristics, other disease records, electrical conductivity, and herd characteristics. The model is designed to run each time a new LDH value is recorded and can run in the absence of the additional factors. Electrical conductivity measurements and disease records, where available, also trigger the model to run. As an input, milk LDH activity values (micromol/min per L) are multiplied by milk yield (L) to produce the amount of LDH (micromol/min) and are then smoothed using an extended Kalman filter before being processed by the biological model. The output comprises a risk of acute mastitis and a relative degree of chronic mastitis. The model also produces a days-to-next sample value that allows sampling frequency to be either increased or reduced depending on the risk of mastitis. The days-to-next sample value was designed to make the best use of opportunities afforded by automated, inline sampling technology. The model functionality was investigated using simulated data, and real-farm data of naturally occurring mastitis were then used to validate the model. The results demonstrated that the model is robust to sampling frequency and random noise in the LDH measurements. It was able to detect mastitis reasonably well: Using a threshold mastitis risk of 0.7, sensitivity for detecting clinical mastitis was 82%. Specificity, that is, the ability to avoid misclassifying healthy observations as mastitis, was 99%.

Acute Disease↗

PubMed2005

Technical and economic effects of an inline progesterone indicator in a dairy herd estimated by stochastic simulation.

For several years progesterone in milk or blood has been recognized as an indicator of different cow states related to reproduction. For this study, an existing simulation model was modified in order to analyze the technical and economic effects of including information on progesterone status in an automatic inline monitoring system. Implementation of an inline progesterone indicator was assumed to directly affect the estrus detection rate, the period until treatment for post-partum anestrus and the number of mistimed AIs. Different implementations of an inline progesterone indicator were simulated in a typical Danish dairy herd with 120 cows and in other herd situations represented by: a herd with poor reproduction efficiency, a herd with a high estrus detection rate and a herd using a 9 week postponed AI period for primiparous cows. It was concluded that implementation of an inline progesterone indicator in a dairy herd previously using visual estrus detection has a break-even price of 3-81 euros per cow-year depending on differences in implementation type and herd reproduction management. The highest break-even price was found using the assumptions that simulated a herd with initially poor reproductive efficiency. With the assumptions that simulated a typical Danish herd the break-even price was 46 euros per cow-year. Attributable effects of using the indicator, including effects of labor time, are discussed.

Animals↗

PubMed2005

Predicting risk of ketosis in dairy cows using in-line measurements of beta-hydroxybutyrate: a biological model.

Automated monitoring of individual cows to determine health status is a potentially valuable management tool, especially in large dairy herds. Herein is described the rationale, structure, and functionality of a biological model to predict risk of ketosis in individual cows using in-line measurements of the ketone body beta-hydroxybutyrate (BHBA) in milk. The model also uses acceleration in milk yield, body fatness at calving, diseases in current lactation, and incidences of ketosis in earlier lactations as additional risk factors for ketosis. However, the model is designed to function merely on the basis of milk BHBA in the absence of other data. Values of milk BHBA are smoothed using a state space model before these are used in calculations in the biological part of the model. The model is designed to be updated each time a new BHBA measurement or a disease occurrence is available and then uses previous and current data. Outputs of the model are the risk of ketosis (value between 0 and 1, where 0 = no risk and 1 = clinical ketosis) and how many days until the next milk sample should be taken and analyzed for BHBA. At higher risks for ketosis, more frequent milk sampling is the recommended output. Test examples from cows for which BHBA has been measured extensively were used to show the functionality of the model. The model performed equally well when reductions in sampling frequency were applied, and it was also relatively robust to the addition of up to +/- 2 residual SD of random noise in the BHBA values. This model has the potential to provide the basis for a useful disease monitoring and management tool. However, thorough validation awaits a much larger dataset and testing of the model under a variety of on-farm situations.

3-Hydroxybutyric Acid↗

PubMed2005

A stochastic model simulating pathogen-specific mastitis control in a dairy herd.

The objective of this study was to develop a model simulating mastitis control in dairy herds and to investigate how sensitive the model is when varying the effect parameters according to the uncertainty. The model simulates 9 pathogen-specific mastitis types, each of which can be subclinical or clinical. The clinical cases can be 1 of 4 severities defined according to the effect of the mastitis case: mild, moderate, severe, and permanent effect. The risk factors include lactation stage, parity, yield level, previous diseases, season, and contagious spread of the infection from herd mates. Occurrence of mastitis is modeled to have direct effects on feed intake, body weight, milk yield, somatic cell count in the milk, subsequent mastitis cases within the cow and in herd mates, voluntary and involuntary culling, mortality, and milk withdrawal. Thirty-five scenarios were simulated to study model behavior and model sensitivity. The consequences per cow/yr of mastitis in the default simulated herd included 0.42 clinical mastitis occurrences, 0.56 subclinical mastitis occurrences, loss of 385-kg milk yield, a 1.3% reduced feed intake, 61-kg milk withdrawal and 146 in reduced economic net return. Based on scenarios demonstrating model behavior and sensitivity analysis, the model appears to produce valid consequences of mastitis control strategies. Representation of the effect of subclinical mastitis and of variation in mastitis severity was concluded in this study to be important when modeling mastitis economics in a dairy herd. The model offers the opportunity to study the long-term herd specific effects of a wide range of control strategies against mastitis.

Animals↗