Table of Contents Author Guidelines Submit a Manuscript
Interdisciplinary Perspectives on Infectious Diseases
Volume 2011, Article ID 543520, 13 pages
Research Article

Epidemic Percolation Networks, Epidemic Outcomes, and Interventions

1Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA 98195-7232, USA
2Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
3Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA

Received 17 July 2010; Revised 3 November 2010; Accepted 23 December 2010

Academic Editor: Lauren Meyers

Copyright © 2011 Eben Kenah and Joel C. Miller. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The supplementary material contains Python code used for Section 3. It requires the NumPy and SciPy packages ( and the networkX package ( The first section of the code (lines 12-333) was used for sections 3.1 (mass-action models) and 3.2 (network-based models). The epidemic probability and attack rate can be calculated using “epiP” and “epiAR” methods, respectively. The second section of the code (lines 336-848) is a largely self-contained package for creating EPNs from given networks. It allows the user to assign the rules for transmission or to use any of a number of commonly used models, including fixed generation time and exponentially distributed infectious period. The functions allow the user to create a realization of an EPN from the given network and infection process. The user can then calculate the probability or attack rate as well as the epidemic curve.

  1. Supplementary Material