2009 InferenceinEpidemicModelsWithou

• (McKinley et al., 2009) ⇒ Trevelyan McKinley, Alex R Cook, and Robert Deardon. (2009). “Inference in Epidemic Models Without Likelihoods.” In: The International Journal of Biostatistics, 5(1).

Subject Headings: Epidemic Model, Epidemic Modeling.

Cited By

• http://scholar.google.com/scholar?q=%222009%22+Inference+in+Epidemic+Models+Without+Likelihoods

Quotes

Author Keywords

• Approximate Bayesian Computation; inference; Markov Chain Monte Carlo; infectious disease modelling; Sequential Monte Carlo

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 InferenceinEpidemicModelsWithou	Trevelyan McKinley Alex R Cook Robert Deardon			Inference in Epidemic Models Without Likelihoods						2009