%0 Journal Article %1 Zuzek2015Epidemic %A Zuzek, L. G. Alvarez %A Stanley, H. E. %A Braunstein, L. A. %D 2015 %J Scientific Reports %K influenza, sir epidemic-models multiplex-networks %P 12151+ %R 10.1038/srep12151 %T Epidemic Model with Isolation in Multilayer Networks %U http://dx.doi.org/10.1038/srep12151 %V 5 %X The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the dynamic movement of infected individuals, e.g., how they are often kept in isolation, is disregarded. We study the SIR model in two multilayer networks and use an isolation parameter, indicating time period, to measure the effect of isolating infected individuals from both layers. This isolation reduces the transmission of the disease because the time in which infection can spread is reduced. In this scenario we find that the epidemic threshold increases with the isolation time and the isolation parameter and the impact of the propagation is reduced. We also find that when isolation is total there is a threshold for the isolation parameter above which the disease never becomes an epidemic. We also find that regular epidemic models always overestimate the epidemic threshold, and this overestimation strongly affects the decision-making process of health authorities when they evaluate whether to declare an epidemic and how to implement mitigation policies.

@article{Zuzek2015Epidemic, abstract = {{The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the dynamic movement of infected individuals, e.g., how they are often kept in isolation, is disregarded. We study the SIR model in two multilayer networks and use an isolation parameter, indicating time period, to measure the effect of isolating infected individuals from both layers. This isolation reduces the transmission of the disease because the time in which infection can spread is reduced. In this scenario we find that the epidemic threshold increases with the isolation time and the isolation parameter and the impact of the propagation is reduced. We also find that when isolation is total there is a threshold for the isolation parameter above which the disease never becomes an epidemic. We also find that regular epidemic models always overestimate the epidemic threshold, and this overestimation strongly affects the decision-making process of health authorities when they evaluate whether to declare an epidemic and how to implement mitigation policies.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Zuzek, L. G. Alvarez and Stanley, H. E. and Braunstein, L. A.}, biburl = {https://www.bibsonomy.org/bibtex/2198a79cb33b49895d507f6dc3185697c/nonancourt}, citeulike-article-id = {13452055}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/srep12151}, citeulike-linkout-1 = {http://arxiv.org/abs/1412.1430}, citeulike-linkout-2 = {http://arxiv.org/pdf/1412.1430}, day = 15, doi = {10.1038/srep12151}, eprint = {1412.1430}, interhash = {b04b3841451513fd9f9862a693c32834}, intrahash = {198a79cb33b49895d507f6dc3185697c}, issn = {2045-2322}, journal = {Scientific Reports}, keywords = {influenza, sir epidemic-models multiplex-networks}, month = jul, pages = {12151+}, posted-at = {2014-12-04 11:01:30}, priority = {2}, timestamp = {2019-07-31T12:37:21.000+0200}, title = {{Epidemic Model with Isolation in Multilayer Networks}}, url = {http://dx.doi.org/10.1038/srep12151}, volume = 5, year = 2015 }