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Title: Inference in epidemic models without likelihoods
Authors: McKinley, T.
Cook, A.R. 
Deardon, R.
Keywords: Approximate Bayesian computation
Infectious disease modelling
Markov Chain Monte Carlo
Sequential Monte Carlo
Issue Date: 2009
Source: McKinley, T., Cook, A.R., Deardon, R. (2009). Inference in epidemic models without likelihoods. International Journal of Biostatistics 5 (1) : -. ScholarBank@NUS Repository.
Abstract: Likelihood-based inference for epidemic models can be challenging, in part due to difficulties in evaluating the likelihood. The problem is particularly acute in models of large-scale outbreaks, and unobserved or partially observed data further complicates this process. Here we investigate the performance of Markov Chain Monte Carlo and Sequential Monte Carlo algorithms for parameter inference, where the routines are based on approximate likelihoods generated from model simulations. We compare our results to a gold-standard data-augmented MCMC for both complete and incomplete data. We illustrate our techniques using simulated epidemics as well as data from a recent outbreak of Ebola Haemorrhagic Fever in the Democratic Republic of Congo and discuss situations in which we think simulation-based inference may be preferable to likelihood-based inference. Copyright ©2009 The Berkeley Electronic Press. All rights reserved.
Source Title: International Journal of Biostatistics
ISSN: 15574679
DOI: 10.2202/1557-4679.1171
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