Pathogens, social networks, and the paradox of transmission scaling

Matthew J. Ferrari, Sarah E. Perkins, Laura W. Pomeroy, Ottar N. Bjrnstad

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations


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 modelseither assuming density-dependent or frequency-dependent transmissionmake 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.

Original languageEnglish (US)
Article number267049
JournalInterdisciplinary Perspectives on Infectious Diseases
StatePublished - 2011

All Science Journal Classification (ASJC) codes

  • Parasitology
  • Microbiology
  • Microbiology (medical)
  • Infectious Diseases
  • Virology


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