Chapter 28 Coming Soon! Positive Predictor

Last updated 24 December 2024

28.1 What is the Positive Predictor?

The Positive Predictor uses a machine learning approach in conjunction with regional surveillance data to predict which counties may turn CWD-positive in upcoming years.

28.2 What Questions Does it Answer?

Question 1. Where is CWD likely to emerge given historical disease emergence around the region? Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD.

Question 2. What factors contribute to CWD emergence? The Positive Predictor highlights co-factors that contribute to the prediction of CWD-status at the sub-administrative level.

28.3 Output Details

A map containing the predictions of CWD emergence for each sub-administrative area in the study area.

28.4 Abbreviated Tutorial

  1. Run the Positive Predictor from the CWD Data Warehouse.
  2. Explore the machine learning predictions of disease status.

28.5 Parameters Needed to Execute the Model

  • Model type: Select ‘Positive Predictor’ from the drop-down list.

  • Reference name: Label the run.

  • (Optional) Applicable season-year: Label the season-year of the run to assist in documentation.

  • (Optional) Notes: Enter any remarks about the run.

  • Season-year: Select the season-year to be used in the estimation.

28.6 Details on the Theory

Ahmed MS, Hanley BJ, Mitchell CI, et al. 2024. Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning. Scientific Reports. 14:14373. https://doi.org/10.1038/s41598-024-65002-7