Biomedical subjects

L W Schruben

Publications and source records attributed to L W Schruben.

3 recordsLinked to original sources

PubMed2000

Disease management research using event graphs.

Event Graphs, conditional representations of stochastic relationships between discrete events, simulate disease dynamics. In this paper, we demonstrate how Event Graphs, at an appropriate abstraction level, also extend and organize scientific knowledge about diseases. They can identify promising treatment strategies and directions for further research and provide enough detail for testing combinations of new medicines and interventions. Event Graphs can be enriched to incorporate and validate data and test new theories to reflect an expanding dynamic scientific knowledge base and establish performance criteria for the economic viability of new treatments. To illustrate, an Event Graph is developed for mastitis, a costly dairy cattle disease, for which extensive scientific literature exists. With only a modest amount of imagination, the methodology presented here can be seen to apply modeling to any disease, human, plant, or animal. The Event Graph simulation presented here is currently being used in research and in a new veterinary epidemiology course.

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PubMed1998

A simulation of strategies to lower bulk tank somatic cell count below 500,000 per milliliter.

In the future, the Pasteurized Milk Ordinance may make milk quality standards more stringent by lowering the somatic cell count (SCC) limit on Grade A raw milk to 500,000/ml. Therefore, using a discrete event simulation model, we investigated the effects of the prevention of intramammary infection (as recommended by the National Mastitis Council), lactation therapy, and dry cow therapy (all seven possible combinations) on bulk tank SCC; milk, fat, and protein yields; prevalence of intramammary infection; and culling for mastitis. Untreated controls were also tested. Ten replicates of each intervention and each control were run for 2 simulated yr, including the daily sampling of 100 cows. The goal was to lower bulk tank SCC < 500,000/ml in the 2nd yr for herds that previously had stable bulk tank SCC between 500,000 and 750,000/ml. Although all strategies occasionally met this goal, on no occasion did all replicates perform without a violation in the 2nd yr of the study (median last month of violation ranged from mo 12 to 23). The combination of the prevention of intramammary infection, lactation therapy, and dry cow therapy resulted in the lowest bulk tank linear score, most replicated without a violation in the 2nd yr, fewest months with a bulk tank linear score > or = 5.3, and fewest mastitis culls. The combination of the prevention of intramammary infection and dry cow therapy also was favorably ranked (highest milk yield, fewest clinical intramammary infections during lactation, and highest percentage of uninfected cows).

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PubMed1998

Design and validation of a dynamic discrete event stochastic simulation model of mastitis control in dairy herds.

A dynamic stochastic simulation model for discrete events, SIMMAST, was developed to simulate the effect of mastitis on the composition of the bulk tank milk of dairy herds. Intramammary infections caused by Streptococcus agalactiae, Streptococcus spp. other than Strep. agalactiae, Staphylococcus aureus, and coagulase-negative staphylococci were modeled as were the milk, fat, and protein test day solutions for individual cows, which accounted for the fixed effects of days in milk, age at calving, season of calving, somatic cell count (SCC), and random effects of test day, cow yield differences from herdmates, and autocorrelated errors. Probabilities for the transitions among various states of udder health (uninfected or subclinically or clinically infected) were calculated to account for exposure, heifer infection, spontaneous recovery, lactation cure, infection or cure during the dry period, month of lactation, parity, within-herd yields, and the number of quarters with clinical intramammary infection in the previous and current lactations. The stochastic simulation model was constructed using estimates from the literature and also using data from 164 herds enrolled with Quality Milk Promotion Services that each had bulk tank SCC between 500,000 and 750,000/ml. Model parameters and outputs were validated against a separate data file of 69 herds from the Northeast Dairy Herd Improvement Association, each with a bulk tank SCC that was > or = 500,000/ml. Sensitivity analysis was performed on all input parameters for control herds. Using the validated stochastic simulation model, the control herds had a stable time average bulk tank SCC between 500,000 and 750,000/ml.

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