Network theory 101

Temporal effects

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 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

Threshold in transmission probability

Threshold in disease dynamics

Two stage HIV model

A society-wide context

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 Half time summary

Temporal vaccination strategies Simulation setup

Temporal vaccination strategies Simulation setup

Temporal vaccination strategies Strategy “Recent”

Temporal vaccination strategies Strategy “Weight”

Relative efficiency, worst case

Relative efficiency, SIR model

Explanatory model

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 Summary

deadline March 10 March 28 – April 20 nordita.org/network2011 March 28 – April 20 nordita.org/network2011

SI model, vs ρ = 1

Parameter dependence, relative efficiency

Outbreak diversity

Contact sequence vs other types of models