CWD Sentinel
Predicting where and when chronic wasting disease (CWD) will emerge or intensify requires integrating spatial, temporal, and epidemiological information across large landscapes. The CWD Sentinel is a hybrid modeling framework that combines Integrated Nested Laplace Approximation (INLA) with machine learning (ML) algorithms to forecast CWD emergence and prevalence increases of at least 1% at the county level. The Sentinel estimates relative risk, spatial and temporal random effects, and fixed covariate effects using INLA, and then incorporates these structured outputs into four ML models: Feedforward Neural Networks, Bayesian Neural Networks, Extreme Gradient Boosting, and Random Forests. Example data are provided in Schuler et al. (2024), and the corresponding methodological paper is described in González-Crespo et al. (under review).
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