%0 Journal Article %1 Ferrari:2011:ipid %A Ferrari, Matthew J %A Perkins, Sarah E %A Pomeroy, Laura W %A Bjørnstad, Ottar N %D 2011 %J Interdisciplinary Perspectives on Infectious Diseases %K epidemes %P 267049 %R 10.1155/2011/267049 %T Pathogens, Social Networks, and the Paradox of Transmission Scaling %V 2011 %X 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.

@article{Ferrari:2011:ipid, 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.}, added-at = {2017-12-18T09:50:24.000+0100}, author = {Ferrari, Matthew J and Perkins, Sarah E and Pomeroy, Laura W and Bj{\o}rnstad, Ottar N}, biburl = {https://www.bibsonomy.org/bibtex/21d369280d6bc0992e1a1ca424f81d21d/krevelen}, comment = {21436998[pmid]}, doi = {10.1155/2011/267049}, interhash = {4b0e2b2c1a521958ba9c5c8eb1a2865f}, intrahash = {1d369280d6bc0992e1a1ca424f81d21d}, issn = {16877098}, journal = {Interdisciplinary Perspectives on Infectious Diseases}, keywords = {epidemes}, month = jan, pages = 267049, timestamp = {2017-12-18T09:51:39.000+0100}, title = {Pathogens, Social Networks, and the Paradox of Transmission Scaling}, volume = 2011, year = 2011 }