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Network theory 101

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Temporal network structure of human contact patterns and its implication for disease dynamics and control Petter Holme, Umeå University with Luis EC Rocha, Sungmin Lee, Fredrik Liljeros. Network theory 101. Network theory 101. Network theory 101. Network theory 101. Temporal effects.

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Presentation Transcript
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Temporal network structure of human contact patterns and its implication for disease dynamics and controlPetter Holme, Umeå Universitywith Luis EC Rocha, Sungmin Lee, Fredrik Liljeros
what we are interested in
What we are interested in
  • What kind of relevant temporal /topological structures are there? Why?
  • How does temporal structures in empirical networks affect disease spreading?
  • Can we exploit these structures to slow down disease spreading?
our datasets
Our datasets
  • E-mail: 3,188 nodes, 309,125 contacts over 83 days
  • Internet dating: 29,341 nodes, 536,276 contacts over 512 d
  • Hospital: 295,107 nodes, 64,625,283 contacts over 8,521 d
  • Prostitution: 16,730 nodes, 50,632 contacts over 2,232 d
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Half time summary
  • Temporal correlations speed up the outbreaks on a short time scale & slows it down on a longer time scale
  • Temporal effects create distinct and comparatively high epidemic thresholds
  • HIV can not spread in the prostitution data alone and probably does not serve as a reservoir of HIV in a society-wide perspective
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Temporal vaccination strategies

Strategy “Recent”

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Temporal vaccination strategies

Strategy “Weight”

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Summary
  • Temporal correlations do affect disease spreading and can be exploited in targeted vaccination
  • The best vaccination strategy depends on the type of temporal structure
  • Until more structural information is available, we recommend the strategy Recent
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Thank

You!

March 28 – April 20

nordita.org/network2011

deadline March 10

http://www.tp.umu.se/~holme/

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