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

A so

ciety

-wid

e co

ntex

t

•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

efficie

ncy,

wo

rst c

ase

Rela

tive

efficie

ncy,

SIR

m

odel

Expl

anat

ory

mod

el

•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 20nordita.org/network2011

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

ThankYou!

SI m

odel

, vs ρ

= 1

Parameter dependence, relative efficiency

Outbreak diversity

Contact sequence vs other types of models