Modeling chronic wasting disease using Gaussian Process Boosting
No Thumbnail Available
Authors
Meeting name
Sponsors
Date
Journal Title
Format
Thesis
Subject
Abstract
Chronic Wasting Disease (CWD) is a fatal neurological condition that affects cervids (white tail deer, elk, mule deer, etc.). Veterinary epidemiologists at the state and federal level are interested in methods to accurately predict the presence of CWD in free-range cervids, and to provide inferences about how location and other environmental factors could affect the spread of CWD. The data for this project was provided by Dr. Ram Raghavan at the University of Missouri School of Veterinary Medicine and was originally collected by researchers in Kansas. Each observation notes the presence of CWD in the cervid carcass along with the coordinates and certain soil measurements from the locations where hunters harvested them. Understanding the spread of CWD is key, since government officials, scholars, and farmers who are in the business of captive breeding are interested in developing methods to contain it geographically, eradicate it, and ultimately preserve the health of existing herds. To this purpose, machine learning models, such as gradient boosting, offer flexibility and predictive accuracy, but lack interpretability. in order to make inferences regarding the features, we use SHapley Additive exPlanations (SHAP), which quantifies the influence of each feature on predictions. Spatial dependence between locations is not accounted for with gradient boosting, but can be modeled with Gaussian Processes. In this project, we use a recently developed spatial modeling method known as Gaussian Process Boosting, which preserves the flexibility and accuracy of gradient boosting while capturing spatial random effects with Gaussian Processes. Results show an 87.5 percent prediction accuracy on a binary response, and that the prediction contributions from the spatial random effects helped accuracy. SHAP values allowed for useful inferences to be made regarding the features.
Table of Contents
DOI
PubMed ID
Degree
M.A.