Johne’s disease is a serious, long-term disease that is difficult to eradicate from a herd once present, but testing can be a useful practice. The Johne’s Testing Decision Tool helps beef cattle producers compare benefits of different testing options for Johne’s disease in cow-calf herds.
- User Guide
- Johnson et al. (2022) Investigating effective testing strategies for the control of Johne’s disease in western Canadian cow-calf herds using an agent-based simulation model
- Part I – Introduction and Overview
- Part II – Customizing the Model to Answer Your Questions
- Part III – Understanding Results
- Part IV – Exploring Multiple Runs
Johne’s Testing Decision Tool Overview
The economic cost of Johne’s disease can be significant due to reduced weaning weights, later rebreeding, and losing or culling cows before they have recouped their production costs. Johne’s disease is very difficult to eradicate once it has become established in a herd, so biosecurity precautions are essential and testing options can be important. This tool provides a range of possible outcomes reflecting the relative impacts of different disease management strategies and the variability we can see in disease transmission and herd performance.
The objective of this simulation model is to provide a tool for comparing potential benefits of different testing options for Johne’s disease in cow-calf herds. The model was built using surveillance and research data primarily from western Canadian cow-calf herds. This is NOT intended to predict what will happen in a particular herd, but rather provides a tool to help producers, vets and other stakeholders simplify the complex task of managing and monitoring Johne’s disease.
FIRST open the “Single customizable experiment” on the side panel. This allows users to edit the start up settings to reflect testing options of interest and the structure of your herd. The other experiments on the side panel also allow you to run different scenarios and testing options and see the results directly.
Using the INITIALIZATION PARAMETERS, the user can customize the herd size and structure, replacement strategy and pre-existing Johne’s risk. The model is set up to compare common testing strategies. Blood and fecal testing are common methods to assess Johne’s disease. Samples are typically analysed using ELISA or PCR testing, as recommended by your veterinarian at collection time.
The model takes into account the uncertainty of test accuracy. The user is asked to define what test is used, testing duration, and frequency. Testing frequency can be managed in two time blocks. For example, you can test more frequently for the first few years and follow that with less frequent testing. The user can also control whether all animals above a certain age are tested or whether a portion of the herd is tested.
Possible options for testing a portion of the herd include a random sample of animals from the herd, only testing animals that have not previously tested negative or risk-based testing targeting higher risk animals. Examples of higher risk animal groups include bulls, purchased cows, heifers from dams that test positive or were identified with clinical Johne’s disease, thin cows or cows within a specific age cohort. It is also possible to cull the daughters of cows that have clinical disease without testing.
Graphics are available in the OUTPUTS section (located below the Initialization Parameters) and display changes in herds size, buying and culling patterns, testing numbers, disease progression in the herd, and the resulting “true prevalence” of infection vs test positivity.
This model recognizes the often-random nature of disease transmission and the uncertainty and variability in how disease progresses in individual animals, in addition to the uncertainty in the performance of the diagnostic tests themselves. Because of this YOU WILL GET A SLIGHTLY DIFFERENT ANSWER each time you run the model even if you use the same settings, which more realistically reflects what happens in real life.
You can run the model 10 to 30 times relatively quickly and scroll down to see the results graphed below. Feel free to customize the settings for your herd.
By using compare function after each experiment has been run, you can click on multiple different management scenarios and compare the average results on the same graph. You can download and save the results using the buttons at the top of the screen.