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

Overview of attention for article published in PLoS Computational Biology, March 2011
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Title
Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1001109
Pubmed ID
Authors

Luis E. C. Rocha, Fredrik Liljeros, Petter Holme

Abstract

Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI). Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution) slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.

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Geographical breakdown

Country Count As %
United States 6 3%
Switzerland 3 2%
United Kingdom 3 2%
Italy 2 1%
Germany 2 1%
Netherlands 1 <1%
Australia 1 <1%
South Africa 1 <1%
Portugal 1 <1%
Other 2 1%
Unknown 161 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 31%
Researcher 40 22%
Student > Master 20 11%
Professor > Associate Professor 12 7%
Professor 11 6%
Other 30 16%
Unknown 13 7%
Readers by discipline Count As %
Physics and Astronomy 39 21%
Mathematics 21 11%
Computer Science 21 11%
Agricultural and Biological Sciences 20 11%
Engineering 15 8%
Other 45 25%
Unknown 22 12%