title: Pathogens, social networks, and the paradox of transmission scaling.
abstract: 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.
authors: Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.
authors: Matthew J
authors: MJ
authors: Ferrari
authors: Sarah E
authors: SE
authors: Perkins
authors: Laura W
authors: LW
authors: Pomeroy
authors: Ottar N
authors: ON
authors: Bjørnstad