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Predicting and controlling infectious disease epidemics using temporal networks
University of Tokyo.
Umeå University, Faculty of Science and Technology, Department of Physics. (IceLab)ORCID iD: 0000-0003-2156-1096
2013 (English)In: F1000 Prime Reports, ISSN 2051-7599, Vol. 5, 6- p.Article, review/survey (Refereed) Published
Abstract [en]

Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

Place, publisher, year, edition, pages
2013. Vol. 5, 6- p.
National Category
Public Health, Global Health, Social Medicine and Epidemiology Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:umu:diva-66819DOI: 10.12703/P5-6OAI: oai:DiVA.org:umu-66819DiVA: diva2:609372
Funder
Swedish Research Council, 2012-3651
Available from: 2013-03-05 Created: 2013-03-05 Last updated: 2013-09-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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