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.