%0 Generic %1 Holme2014Model %A Holme, Petter %D 2014 %K sis networks algorithms epidemic-models sir %R 10.3969/j.issn.1672-7843.2014.03.001 %T Model versions and fast algorithms for network epidemiology %U http://dx.doi.org/10.3969/j.issn.1672-7843.2014.03.001 %X Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions - one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people), the other where the duration of the disease is constant. We find that, for most practical purposes, these versions are qualitatively the same.

@misc{Holme2014Model, abstract = {{Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions - one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people), the other where the duration of the disease is constant. We find that, for most practical purposes, these versions are qualitatively the same.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Holme, Petter}, biburl = {https://www.bibsonomy.org/bibtex/2dce0e0a0903a510e0694e57e47a30b9e/nonancourt}, citeulike-article-id = {13095214}, citeulike-linkout-0 = {http://dx.doi.org/10.3969/j.issn.1672-7843.2014.03.001}, citeulike-linkout-1 = {http://arxiv.org/abs/1403.1011}, citeulike-linkout-2 = {http://arxiv.org/pdf/1403.1011}, day = 5, doi = {10.3969/j.issn.1672-7843.2014.03.001}, eprint = {1403.1011}, interhash = {32e6deee7d128cadfcbc69e5c3d55040}, intrahash = {dce0e0a0903a510e0694e57e47a30b9e}, keywords = {sis networks algorithms epidemic-models sir}, month = mar, posted-at = {2014-03-06 10:09:49}, priority = {2}, timestamp = {2019-08-22T16:24:44.000+0200}, title = {{Model versions and fast algorithms for network epidemiology}}, url = {http://dx.doi.org/10.3969/j.issn.1672-7843.2014.03.001}, year = 2014 }