Semiparametric space–time survival modeling of chronic wasting disease in deer |
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Authors: | Andrew Lawson Hae-Ryoung Song |
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Institution: | (1) Department of Civil Engineering, The University of Hong Kong, Hong Kong, China |
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Abstract: | In this paper, we propose a semiparametric survival model to investigate the pattern of spatial and temporal variation in
disease prevalence of chronic wasting disease (CWD) in wild deer in Wisconsin over the years 2002 and 2006. The semiparametric
survival model we suggested allows to build a more flexible model than the parametric model with fewer parametric assumptions
by modeling the baseline hazard using a Gamma process prior. Based on the proposed model, we investigate the geographical
distribution of CWD, and assess the effect of sex on disease prevalence. We use a Bayesian hierarchical framework where latent
parameters capture temporal and spatial trends in disease incidence, incorporating sex and spatially correlated random effects.
We also propose bivariate baseline hazard which change over age and time simultaneously to adopt different effects of age
and time on the baseline hazard. Inference is carried out by using MCMC simulation techniques in a fully Bayesian framework.
Our results suggest that disease has been spreaded mainly in the disease eradication zone and male deer show a significantly
higher infection probability than female deer. |
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Keywords: | |
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