  Mathematical models of epidemic dynamics offer significant insight into
predicting and controlling infectious diseases. The dynamics of a disease model
generally follow a susceptible, infected, and recovered (SIR) model, with some
standard modifications. In this paper, we extend the work of Munz et.al (2009)
on the application of disease dynamics to the so-called "zombie apocalypse",
and then apply the identical methods to influenza dynamics. Unlike Munz et.al
(2009), we include data taken from specific depictions of zombies in popular
culture films and apply Markov Chain Monte Carlo (MCMC) methods on improved
dynamical representations of the system. To demonstrate the usefulness of this
approach, beyond the entertaining example, we apply the identical methodology
to Google Trend data on influenza to establish infection and recovery rates.
Finally, we discuss the use of the methods to explore hypothetical intervention
policies regarding disease outbreaks.
