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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 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

- 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

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

Temporal vaccination strategies

Simulation setup

Temporal vaccination strategies

Simulation setup

Temporal vaccination strategies

Strategy “Recent”

Temporal vaccination strategies

Strategy “Weight”

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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