Reshared post from Bruno Gonçalves

The visualization of temporal contact patterns is interesting, using examples of both memetics and disease spread:

"Epidemic outbreaks of an infectious disease are complex functions of both the characteristics of the pathogen and the movement and interaction patterns of the people. The diversity in people's contact patterns carries over into disease spreading. It is believed that an outbreak such as the SARS epidemics of 2003 might not have become a major event if not for a few highly influential spreaders exhibiting behavior far outside the norm. To lower the threshold for herd immunity, it is crucial to identify and vaccinate these potentially influential individuals. The idea in this paper is to use empirical contact structures, more or less close to those over which disease may spread, to identify important people to vaccinate. One early example of this approach is the neighborhood vaccination (NV) protocol—choose a person at random among all persons that have been involved in at least one contact at time t*, ask her to name someone she met, vaccinate this other person, and repeat until a desired fraction of the vertices are vaccinated. Chances are high that this other person has a large degree (number of neighbors) in the static interaction network and may be influential in spreading disease. The contact structure thus not only influences disease dynamics, it is also a source of information that can be exploited to stop the disease. Human interaction patterns have much more structure that can be utilized in immunization protocols than merely the distribution of degrees in a static network, which is what neighborhood vaccination protocols build on. There is a great deal of temporal structure as well. The simplest such patterns are cyclic—we are more likely to meet others at 3PM than at 3AM. Another potentially important temporal pattern is a broad distribution of contact rates between pairs of individuals. Especially for diseases with a relatively high infectious dose, needing a prolonged exposure to transfer, this could have an impact on the disease dynamics that is hard to predict from network structure alone. A straightforward extension of the NV protocol to capture this structure would be to ask the person chosen at random to name the person she has met most often since some specific time. This is one of the protocols we test. A third temporal pattern, which static network models do not capture, is the overturn of relationships, i.e. that an edge is active for a limited period of time and never again after this. If there is a positive correlation between the activity over an edge and the activity of the vertices at either side, then it is important to vaccinate people who are engaged in a period of activity. This leads to another extension of the NV protocol—ask the individual picked at random who her most recent contact was (who could spread the disease), and then vaccinate that person. Just like the NV protocol, this is a method does not require any global knowledge and can be implemented in practice."

Bruno Gonçalves originally shared this post:

Exploiting Temporal Network Structures of Human Interaction to Effectively Immunize Populations

by Sungmin Lee, Luis E. C. Rocha, Fredrik Liljeros, Petter Holme

Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important …

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