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Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts
- Source :
- PLoS Computational Biology, Vol 7, Iss 3, p e1001109 (2011), PLoS Computational Biology
- Publication Year :
- 2011
- Publisher :
- Public Library of Science (PLoS), 2011.
-
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.<br />Author Summary Human sexual contacts form a spatiotemporal network—the underlying structure over which sexually transmitted infections (STI) spread. By understanding the structure of this system we can better understand the dynamics of STIs. So far, there has been much focus on the static network structure of sexual contacts. In this paper, we extend this approach and also address temporal effects in a special type of sexual network—that of Internet-mediated prostitution. We analyze reported sexual contacts, probably the largest record of such, from a Brazilian Internet community where sex buyers rate their encounters with escorts. First, we thoroughly investigated disease spread in this dynamic sexual network. We found that the temporal correlations in this system would accelerate disease spread, especially at shorter time scales, whereas geographical effects would slow down an outbreak. More specifically, we found that this contact structure could sustain more contagious diseases, like human papillomavirus, but not HIV. These results highlight the importance of prostitution in the global dynamics of STIs.
- Subjects :
- Male
Human immunodeficiency virus (HIV)
Public Health and Epidemiology/Infectious Diseases
medicine.disease_cause
law.invention
Disease Outbreaks
Models
law
Econometrics
Sociology
Biology (General)
Ecology
Statistical
Transmission (mechanics)
Sexual Partners
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Probability distribution
Female
Brazil
Network analysis
Research Article
Sexual network
Physics - Physics and Society
QH301-705.5
Sexually Transmitted Diseases
FOS: Physical sciences
Topology (electrical circuits)
Prostitution
Physics and Society (physics.soc-ph)
Network topology
Models, Biological
Physics/Interdisciplinary Physics
Cellular and Molecular Neuroscience
Genetics
medicine
Infectious Diseases/Sexually Transmitted Diseases
Humans
Fysik
Computer Simulation
Quantitative Biology - Populations and Evolution
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Simulation
Sex work
Internet
Models, Statistical
Populations and Evolution (q-bio.PE)
Biological
Sex Work
FOS: Biological sciences
Public Health and Epidemiology/Epidemiology
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 7
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....63f075ee7662965a45821982b963d795