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The Importance of Different Social Networks for Infectious Diseases. Fredrik Liljeros Stockholm University Karolinska institutet Supported by the Swedish Institute for Public Health and The Swedish Emergency Management Agency. S-GEM. Stockholm Group for Epidemic Modelling , S-GEM.

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the importance of different social networks for infectious diseases

The Importance of Different Social Networks for Infectious Diseases

Fredrik Liljeros

Stockholm University

Karolinska institutet

Supported by the Swedish Institute for Public Health

and

The Swedish Emergency Management Agency

S-GEM

stockholm group for epidemic modelling s gem
Stockholm Group for Epidemic Modelling, S-GEM

Johan Giesecke SMI/KI Åkes Svensson SMI/SU Fredrik Liljeros SU/KI

S-GEM

why model epidemics
Why model epidemics?
  • Will there be an outbreak?
  • How many will be infected?
  • The speed of the outbreak?
  • How can we best limit the effects of an outbreak
  • How many must be vaccinated?
  • Who should be vaccinated?

S-GEM

outline
Outline
  • Traditional Models
  • Networks
  • Empirical Network Studies

S-GEM

key concepts
Key Concepts
  • Variation in number of contacts
  • Assortative interaction
  • Clustering/Transitivity
  • Small World Network

S-GEM

epidemic models
Epidemic models

Deterministic models

Stochastic models

Agent-based models (Micro simulation models)

S-GEM

a very simplified example
A very simplified example

Suceptible

Infected

S-GEM

it is possible to study important properties of deterministic models analytically
It is possible to study important properties of deterministic models analytically

S-GEM

it is possible to let a deterministic model capture ma n y relevant properties
It is possible to let a deterministic model capture manyrelevant properties
  • Individuals may become immune
  • Individuals may die
  • New individuals may be borned
  • Individuals may belong to different groups with different type of behavior

S-GEM

erd s r nyi network 1960
Erdös-Rényi network (1960)

Pál Erdös(1913-1996)

S-GEM

struktural effects
Struktural effects

Variation in contacts

Lower epidemic treshold

Smaller outbreaks

assortativity

Slower outbreaks

Clustring

S-GEM

milgrams study
Milgrams Study

Nebraska

Pamela

Five persons

Massachusetts

Kansas

S-GEM

but we know that social networks are clustred
But we know that social networks are clustred

Should not the distance between randomly selected individuals be long?

S-GEM

watts strogatz model

(from http://www.aip.org/aip/corporate/2000/watts.htm

& http://tam.cornell.edu/Strogatz.html)

Watts-Strogatz Model

C(p) : clustering coeff. L(p) : average path length

(Watts and Strogatz, Nature 393, 440 (1998))

take home messages
Take Home messages
  • Variation in number of contacts
  • Assortative interaction
  • Clustering/Transitivity
  • Small World Network

S-GEM

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