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Interdisciplinary Perspectives on Infectious Diseases
Volume 2011, Article ID 267049, 10 pages
Research Article

Pathogens, Social Networks, and the Paradox of Transmission Scaling

1Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
2Cardiff School of Biosciences, Cardiff University, Biomedical Sciences Building, Museum Avenue, Room C7.29, Cardiff CF10 3AX, UK
3Department of Veterinary Preventative Medicine, The Ohio State University, Columbus, OH 43210, USA
4Center for Infectious Disease Dynamics, Departments of Biology and Entomology, The Pennsylvania State University, University Park, PA 16802, USA

Received 24 September 2010; Revised 26 December 2010; Accepted 15 January 2011

Academic Editor: Katia Koelle

Copyright © 2011 Matthew J. Ferrari et al. 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.


Understanding the scaling of transmission is critical to predicting how infectious diseases will affect populations of different sizes and densities. The two classic “mean-field” epidemic models—either assuming density-dependent or frequency-dependent transmission—make predictions that are discordant with patterns seen in either within-population dynamics or across-population comparisons. In this paper, we propose that the source of this inconsistency lies in the greatly simplifying “mean-field” assumption of transmission within a fully-mixed population. Mixing in real populations is more accurately represented by a network of contacts, with interactions and infectious contacts confined to the local social neighborhood. We use network models to show that density-dependent transmission on heterogeneous networks often leads to apparent frequency dependency in the scaling of transmission across populations of different sizes. Network-methodology allows us to reconcile seemingly conflicting patterns of within- and across-population epidemiology.