Inference in Epidemic Models without Likelihoods

McKinley Trevelyan, Cook Alex R and Deardon Robert
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McKinley Trevelyan: University of Cambridge
Cook Alex R: National University of Singapore
Deardon Robert: University of Guelph

The International Journal of Biostatistics, 2009, vol. 5, issue 1, 40

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.

Keywords: Approximate Bayesian Computation; inference; Markov Chain Monte Carlo; infectious disease modelling; Sequential Monte Carlo (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (19)

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DOI: 10.2202/1557-4679.1171

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