Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts
By Luis E C Rocha, Fredrik Liljeros, Petter Holme. PLoS Comput. Biol., 2010
314 citations recorded by the source corpus.
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.
Subfields: Clinical Psychology, General Health Professions, Infectious Diseases
View Paper: https://www.semanticscholar.org/paper/4f1c7b1b9c8f5510baa5d9be00044d51bdba75cf
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In Clinical Psychology
In General Health Professions
In Infectious Diseases
Citations in the corpus
- Mathematical modeling of infectious disease dynamics (2013, methodology): “131 The network was extended over 12 cities.”
- Measuring Large-Scale Social Networks with High Resolution (2014, background): “For example, influenza depends on physical proximity for its spread, while information may diffuse on all types of links; with the deep data approach we can study differences and similarities between various types of spreading and the interplay between the various communication channels [127,128].”
- Temporal Networks (2011, background): “Thus one can go straight from the raw data to simulations of spreading processes (or some other dynamics), and analyze the role of the temporal and topological structure by comparing the results to referencemodels [155].”
- Modern temporal network theory: a colloquium (2015, methodology): “To understand the effects of the order of events, one can randomly permute the times of contacts while keeping the network structure and the numbers of contacts between all node-pairs fixed [102,132,247,213,37,143,196,9,238,270].”