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

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  • 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.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.

Suggested Citation

  • Luis E C Rocha & Fredrik Liljeros & Petter Holme, 2011. "Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-9, March.
  • Handle: RePEc:plo:pcbi00:1001109
    DOI: 10.1371/journal.pcbi.1001109
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    References listed on IDEAS

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    1. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
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    2. Eugenio Valdano & Davide Colombi & Chiara Poletto & Vittoria Colizza, 2023. "Epidemic graph diagrams as analytics for epidemic control in the data-rich era," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Yanjie Xu & Tao Ren & Shixiang Sun, 2021. "Identifying Influential Edges by Node Influence Distribution and Dissimilarity Strategy," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    4. Hong, Xiao & Han, Yuexing & Wang, Bing, 2023. "Impacts of detection and contact tracing on the epidemic spread in time-varying networks," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    5. Hao, Hongchang & Xing, Wanli & Wang, Anjian & Song, Hao & Han, Yawen & Zhao, Pei & Xie, Ziqi & Chen, Xuemei, 2022. "Multi-layer networks research on analyzing supply risk transmission of lithium industry chain," Resources Policy, Elsevier, vol. 79(C).
    6. Anirban Dasgupta & Srijan Sengupta, 2022. "Scalable Estimation of Epidemic Thresholds via Node Sampling," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 321-344, June.
    7. Jeffrey A. Smith & Jessica Burow, 2020. "Using Ego Network Data to Inform Agent-based Models of Diffusion," Sociological Methods & Research, , vol. 49(4), pages 1018-1063, November.
    8. Liu, Kang & Yin, Ling & Ma, Zhanwu & Zhang, Fan & Zhao, Juanjuan, 2020. "Investigating physical encounters of individuals in urban metro systems with large-scale smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    9. Zhao, Xiuming & Yu, Hongtao & Li, Shaomei & Liu, Shuxin & Zhang, Jianpeng & Cao, Xiaochun, 2022. "Effects of memory on spreading processes in non-Markovian temporal networks based on simplicial complex," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    10. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    11. Petter Holme, 2021. "Fast and principled simulations of the SIR model on temporal networks," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-15, February.
    12. Eugenio Valdano & Chiara Poletto & Armando Giovannini & Diana Palma & Lara Savini & Vittoria Colizza, 2015. "Predicting Epidemic Risk from Past Temporal Contact Data," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-19, March.
    13. Luis E C Rocha & Vincent D Blondel, 2013. "Bursts of Vertex Activation and Epidemics in Evolving Networks," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-9, March.

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