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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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Network theory 101

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Network theory 101

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


Network theory 101

Network theory 101


Network theory 1011

Network theory 101


Network theory 1012

Network theory 101


Network theory 1013

Network theory 101


Temporal effects

Temporal effects


Temporal effects1

Temporal effects


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


Worst case scenario vs null model

Worst case scenario vs. null-model


Network theory 101

Threshold in transmission probability


Network theory 101

Threshold in disease dynamics


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Two stage HIV model


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A society-wide context


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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


Network theory 101

Temporal vaccination strategies

Simulation setup


Network theory 101

Temporal vaccination strategies

Simulation setup


Network theory 101

Temporal vaccination strategies

Strategy “Recent”


Network theory 101

Temporal vaccination strategies

Strategy “Weight”


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Relative efficiency, worst case


Network theory 101

Relative efficiency, SIR model


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Explanatory model


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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


Network theory 101

Thank

You!

March 28 – April 20

nordita.org/network2011

deadline March 10

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


Network theory 101

SI model, vsρ = 1


Network theory 101

Parameter dependence, relative efficiency


Network theory 101

Outbreak diversity


Network theory 101

Contact sequence vs other types of models


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