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Parameterizing Spatial Models of Infectious Disease Transmission that Incorporate Infection Time Uncertainty Using Sampling-Based Likelihood Approximations
A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to va...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2016
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701410/ https://www.ncbi.nlm.nih.gov/pubmed/26731666 http://dx.doi.org/10.1371/journal.pone.0146253 |