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Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts

Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts

Rocha, Luis E. C. ORCID: 0000-0001-9046-8739, Liljeros, Fredrik and Holme, Petter (2011) Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Computational Biology, 7 (3):e1001109. ISSN 1553-734X (Print), 1553-7358 (Online) (doi:https://doi.org/10.1371/journal.pcbi.1001109)

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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.

Item Type: Article
Additional Information: © 2011 Rocha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords: epidemic modeling, network science, simulations
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Faculty of Business > Department of International Business & Economics
Last Modified: 09 Nov 2018 17:43
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/20555

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