Markov Chain Monte Carlo Methods for Outbreak Data

Authored by: Philip D. O’Neill , Theodore Kypraios

Handbook of Infectious Disease Data Analysis

Print publication date:  November  2019
Online publication date:  November  2019

Print ISBN: 9781138626713
eBook ISBN: 9781315222912
Adobe ISBN:

10.1201/9781315222912-9

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Abstract

In this chapter we explain how Markov chain Monte Carlo (MCMC) methods can be employed to analyze data from an outbreak of infectious disease. The basic approach is to (1) formulate a suitable stochastic transmission model, (2) find a way of deriving the likelihood of the observed data under this model, and (3) adopt a Bayesian framework using MCMC methods to make inference about the model parameters. In practice, simply fitting models to data is rarely of scientific interest in itself, but by choosing models and model parameters appropriately it becomes possible to provide quantitative information about the outbreak or the pathogen. This approach might include information on key quantities such as the basic reproduction number, the importance of relative routes of transmission, the length of the infectious period, or other relevant aspects. The epidemic model also could be used for forecasting using the estimated parameters.

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