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 4 February 2013
  • Computer Science
  • 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… 

Figures and Tables from this paper

Information content of contact-pattern representations and predictability of epidemic outbreaks
TLDR
This paper studies both outbreak sizes from unknown sources, and from known states of ongoing outbreaks, using empirical proximity data to investigate the effect of successive inclusions of more information in the temporal network structure of the data sets.
Temporal network structures controlling disease spreading.
TLDR
This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.
The Basic Reproduction Number as a Predictor for Epidemic Outbreaks in Temporal Networks
TLDR
Among 31 explanatory descriptors of temporal network structure, those that make R 0 an imperfect predictor of Ω are identified, finding that descriptors related to both temporal and topological aspects affect the relationship between R 0 and Ω, but in different ways.
Immunization strategies for epidemic processes in time-varying contact networks
Computing the vulnerability of time-evolving networks to infections
TLDR
This work analytically compute the epidemic threshold on a generic temporal network, accounting for several different disease features, and provides new methodologies for assessing and predicting the risk associated to an emerging pathogen, both at the population scale and targeting specific hosts.
Fast Inference for Network Models of Infectious Disease Spread
Models of infectious disease over contact networks offer a versatile means of capturing heterogeneity in populations during an epidemic. Highly connected individuals tend to be infected at a higher
Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution
TLDR
It is found that permanent or temporary immunity do not affect the estimation of the epidemic threshold through the infection propagator approach, and weight-topology correlations are found to be the critical factor to be preserved to improve accuracy in the prediction.
A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models
We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic
From temporal network data to the dynamics of social relationships
TLDR
This work presents a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent, and implements this interdependence in a parsimonious two-parameter model and applies it to several human and non-human primates’ datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks.
...
...

References

SHOWING 1-10 OF 52 REFERENCES
Networks and epidemic models
TLDR
A variety of methods are described that allow the mixing network, or an approximation to the network, to be ascertained and how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control are suggested.
Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts
TLDR
It is found that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network, suggesting that temporal structures are needed alongside network topology to fully understand the spread of STIs.
Bursts of Vertex Activation and Epidemics in Evolving Networks
TLDR
A stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event intervals, and may leave and enter the system is proposed, finding that prevalence is generally higher for heterogeneous patterns, except for sufficiently large infection duration and transmission probability.
Statistical inference to advance network models in epidemiology.
Susceptible–infected–recovered epidemics in dynamic contact networks
  • E. Volz, L. Meyers
  • Biology
    Proceedings of the Royal Society B: Biological Sciences
  • 2007
TLDR
A mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time, thereby providing a bridge between disparate classes of epidemiological models.
Temporal Networks
How disease models in static networks can fail to approximate disease in dynamic networks.
  • N. Fefferman, K. L. Ng
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2007
TLDR
This work examines the assumption that the effects of shifting social associations within groups are small by modeling disease spread within dynamic networks where associations shift according to individual preference based on three different measures of network centrality, and shows that this assumption may not hold in many cases.
The dynamic nature of contact networks in infectious disease epidemiology
TLDR
Recent data-driven and process-driven approaches that capture the dynamics of human contact are reviewed, and future challenges for the field are discussed.
Epidemic thresholds in dynamic contact networks
TLDR
It is shown that social mixing fundamentally changes the epidemiological landscape and, consequently, that static network approximations of dynamic networks can be inadequate.
Contagion dynamics in time-varying metapopulation networks
TLDR
This work focuses on the SIR process and determines analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models, and finds profound differences from the case of static networks.
...
...