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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
Network theory 101
Network theory 101
Network theory 101
Network theory 101
Temporal effects
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
•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
Threshold in transmission probability
Threshold in disease dynamics
Two stage HIV model
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•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”
Rela
tive e
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wors
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Rela
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SIR
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Exp
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•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 10March 28 – April 20
nordita.org/network2011
http://www.tp.umu.se/~holme/
ThankYou!
SI m
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Parameter dependence, relative efficiency
Outbreak diversity
Contact sequence vs other types of models
