Epidemiologically Optimal Static Networks from Temporal Network Data

@article{Holme2013EpidemiologicallyOS,
  title={Epidemiologically Optimal Static Networks from Temporal Network Data},
  author={Petter Holme},
  journal={PLoS Computational Biology},
  year={2013},
  volume={9}
}
  • Petter Holme
  • Published 2013
  • Physics, Computer Science, Biology, Medicine
  • PLoS Computational Biology
  • One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 52 REFERENCES

    Networks and epidemic models

    VIEW 2 EXCERPTS

    Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts

    VIEW 1 EXCERPT

    Bursts of Vertex Activation and Epidemics in Evolving Networks

    Susceptible–infected–recovered epidemics in dynamic contact networks

    Temporal Networks

    How disease models in static networks can fail to approximate disease in dynamic networks.

    Epidemic thresholds in dynamic contact networks