1. Disease spreading &
control in temporal
networks
Petter Holme, Umeå University & SKKU
in KAIST Dec 14, 2010
with Luis EC Rocha, Sungmin Lee, Fredrik Liljeros
3. What we are interested
in
• How does temporal structures in empirical
networks affect disease spreading?
• Can we exploit these structures to slow
down disease spreading?
4. 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
13. 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
19. 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
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