Statistics Seminars: Bayesian estimation of the basic reproduction number in epidemic models
12 December 2008 13:15 in CM105
In Bayesian inference for epidemic models, it has become commonplace to treat unobserved data (such as times of infection) as extra parameters to be
estimated, typically using MCMC. Instead of this, we derive bounds on the posterior distribution of the basic reproduction number R0, allowing the
unobserved infection times to vary across their whole feasible region. Using linear programming, we can find such bounds quickly and easily, providing a
diagnostic check on MCMC results. We apply the method to a few different epidemic models from the literature.
This is joint work with PD O'Neill.
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