Toward epidemic thresholds on temporal networks: a review and open questions
| dc.contributor.author | Leitch, Jack | en |
| dc.contributor.author | Alexander, Kathleen A. | en |
| dc.contributor.author | Sengupta, Srijan | en |
| dc.contributor.department | Fish and Wildlife Conservation | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2019-11-18T13:03:44Z | en |
| dc.date.available | 2019-11-18T13:03:44Z | en |
| dc.date.issued | 2019-11-14 | en |
| dc.date.updated | 2019-11-17T04:20:19Z | en |
| dc.description.abstract | Epidemiological contact network models have emerged as an important tool in understanding and predicting spread of infectious disease, due to their capacity to engage individual heterogeneity that may underlie essential dynamics of a particular host-pathogen system. Just as fundamental are the changes that real-world contact networks undergo over time, both independently of and in response to pathogen spreading. These dynamics play a central role in determining whether a disease will die out or become epidemic within a population, known as the epidemic threshold. In this paper, we provide an overview of methods to predict the epidemic threshold for temporal contact network models, and discuss areas that remain unexplored. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Applied Network Science. 2019 Nov 14;4(1):105 | en |
| dc.identifier.doi | https://doi.org/10.1007/s41109-019-0230-4 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/95566 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The Author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Toward epidemic thresholds on temporal networks: a review and open questions | en |
| dc.title.serial | Applied Network Science | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |