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 language||English (US)|
|Journal||Interdisciplinary Perspectives on Infectious Diseases|
|State||Published - 2011|
All Science Journal Classification (ASJC) codes
- Microbiology (medical)
- Infectious Diseases