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Bayesian Analysis of Epidemics - Zombies, Influenza, and other Diseases

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1311.6376

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Abstract

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.

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Author and article information

Journal
2013-11-25
2013-11-27

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Custom metadata
16 pages, 6 figures, 2 tables. Corrected email address typo from previous version
q-bio.PE stat.AP

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