Infectious disease is an important cause of lost production and profits to beef cow-calf producers each year. Beef producers commonly import new animals into their herds, but often do not properly apply biosecurity tools to economically decrease risk of disease introduction. Dr. Michael Sanderson, a professor of Beef Production and Epidemiology at Kansas State University’s (KSU) College of Veterinary Medicine, wanted to address this issue by developing a risk management tool for veterinarians and beef cow-calf producers to assist in identifying biologically and economically valuable biosecurity practices, using @RISK.
About KSU College of Veterinary Medicine
The college was established in 1905, and has granted more than 5,000 Doctor of Veterinary Medicine degrees. Departments within the College of Veterinary Medicine include anatomy and physiology, clinical sciences, diagnostic medicine, and pathobiology. The college's nationally recognized instructional and research programs provide the highest standards of professional education. A rich, varied, and extensive livestock industry in the region, a city with many pets and a zoo, and referrals from surrounding states provide a wealth of clinical material for professional education in veterinary medicine.
Dr. Michael Sanderson
College of Veterinary Medicine, Kansas State University
Exploring Risk Factors of Virus Introduction
Reproductive disease is an important cause of lost production and economic return to beef cow-calf producers, causing estimated losses of $400 to $500 million dollars per year. Because of the complex nature of the production system, the biologically and economically optimal interventions to control disease risk are not always clear. Dr. Sanderson and his team (including Drs. Rebecca Smith and Rodney Jones) utilized @RISK to model the probability and economic costs of disease introduction and the cost and effectiveness of management strategies to decrease that risk.
“For this project, @RISK was essential to model variability and uncertainty in risk for disease introduction and impact following introduction, as well as variability and uncertainty in effectiveness of mitigation strategies,” said Dr. Sanderson. “Further, @RISK was crucial for sensitivity analysis of the most influential inputs to refine the model and to identify the most important management practices to control risk. It was also valuable to aggregate results into probability distributions for risk and economic cost over one-year and ten-year year planning periods.“
The project modelled the risk of introduction of the infectious disease Bovine Viral Diarrhea (BVD) into the herd, the impact of disease on the herd (morbidity, mortality, abortion, culling, lost weight) and economic control costs. These risks were aggregated over ten years to identify the optimal management strategy to minimize cost from BVD accounting for both production costs and control costs.
Probability distributions included:
- Disease introduction risk dependant on the age of cattle imported
- Disease impact for infected individuals (abortion risk, morbidity risk, mortality risk, lost weight gain etc.)
- Intervention effectiveness (test sensitivity, vaccine efficacy, etc.)
- Weaned calf value and cull cow value based on historical 10 year prices
Target probabilities were utilized to produce the probability of exceeding a certain cost over one and ten years, provide this data as a single number for each management option and generate descending cumulative probability distributions for exceeding any particular cost value.
As a result of the risk identification insight gained from the research, Dr. Sanderson and his team were able to improve disease management and controls by identifying:
- Management practices that affect the probability of disease introduction and the production and identifying economic impact of disease if introduced into the herd.
- Optimal management strategies to decrease biological and economic risk over a ten-year planning horizon.
“Our utilization of @RISK gave us the ability to account for complex aggregation of inputs and their variability and uncertainty to produce full-outcome probability distributions for more informed decision making,” said Dr. Sanderson. “Further, the ability to use research data from multiple parts of the beef production system and combine those results into a model that accounts for the complexity of the production systems allows recognition of emergent phenomena and decision making based on the full system, rather than only one part. The flexibility to customize outputs provided the most valuable information for decision making.”