Topographic Spreading Analysis of an Empirical Sex Workers’ Network

  • Johannes Bjell
  • Geoffrey Canright
  • Kenth Engø-Monsen
  • Valencia P. Remple
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)

The problem of epidemic spreading over networks has received considerable attention in recent years, due both to its intrinsic intellectual challenge and to its practical importance. A good recent summary of such work may be found in Newman (8), while (9) gives an outstanding example of a non-trivial prediction which is obtained from explicitly modeling the network in the epidemic spreading. In the language of mathematicians and computer scientists, a network of nodes connected by edges is called a graph. Most work on epidemic spreading over networks focuses on whole-graph properties, such as the percentage of infected nodes at long time. Two of us have, in contrast, focused on understanding the spread of an infection over time and space (the network) (61; 63; 62). This work involves decomposing any given network into subgraphs called regions (61). Regions are precisely defined as disjoint subgraphs which may be viewed as coarse-grained units of infection—in that, once one node in a region is infected, the progress of the infection over the remainder of the region is relatively fast and predictable (63). We note that this approach is based on the ‘Susceptible-Infected’ (SI) model of infection, in which nodes, once infected, are never cured. This model is reasonable for some infections, such as HIV—which is one of the diseases studied here. We also study gonorrhea and chlamydia, for which a more appropriate model is Susceptible-Infected-Susceptible (SIS) (67) (since nodes can be cured); we discuss the limitations of our approach for these cases below.


Weighted Graph Transmission Probability Link Weight Start Node Epidemic Spreading 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



GC and KEM acknowledge partial support from the Future and Emerging Technologies unit of the European Commission through Project DELIS (IST-2002-001907). VPR acknowledges the financial and in-kind support, respectively, of the BC Medical Services Fdn and HIV/STI Prevention and Control, BC Centre for Disease Control.


  1. 1.
    G. Canright and K. Engø-Monsen. Roles in networks. Science of Computer Programming, pages 195–214, 2004.Google Scholar
  2. 2.
    G. Canright and K. Engø-Monsen. Epidemic spreading over networks: a view from neighbourhoods. Telektronikk, 101:65–85, 2005.Google Scholar
  3. 3.
    G. Canright and K. Engø-Monsen. Spreading on networks: a topographic view. In Proceedings, European Conference on Complex Systems, 2005.Google Scholar
  4. 4.
    G. S. Canright and K. Engø-Monsen. Some relevant aspects of network analysis and graph theory. In J. Bergstra and M. Burgess, editors, Handbook of Network and Systems Admnistration. Elsevier, Amsterdam, 2007.Google Scholar
  5. 5.
    K. Holmes, R. Levine, and M. Weaver. Effectiveness of condoms in preventing sexually transmitted infections. Bull World Health Organ, 82:454–461, 2004.Google Scholar
  6. 6.
    A. M. Jolly, M. E. Moffatt, M. V. Fast, and R. C. Brunham. Sexually transmitted disease thresholds in Manitoba, Canada. Ann Epidemiol, 15:781–788, 2005.CrossRefGoogle Scholar
  7. 7.
    M. Kretzschmar, Y. T. P. H. van Duynhoven, and A. J. Severijnen. Modeling prevention strategies for gonorrhea and chlamydia using stochastic network simulations. American Journal of Epidimiology, 144:306–317, 1996.Google Scholar
  8. 8.
    M. Newman. The structure and function of complex networks. SIAM Review, 45:167–256, 2003.CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    R. Pastor-Satorras and A. Vespignani. Epidemic spreading in scale-free networks. Phys Rev Lett, 86:3200–3203, 2001.CrossRefGoogle Scholar
  10. 10.
    V. P. Remple, D. M. Patrick, C. Johnston, M. W. Tyndall, and A. Jolly. Clients of indoor commercial sex workers: Heterogeneity in patronage patterns and implications for HIV and STI propagation through sexual networks. Sexually Transmitted Diseases, May 2007.Google Scholar

Copyright information

© Birkhäuser Boston, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Telenor R&IFornebuNorway
  2. 2.Telenor R&IFornebuNorway
  3. 3.Telenor R&IFornebuNorway
  4. 4.BC Centre for Disease Control EpidemiologyUniversity of British ColumbiaVancouver, BCCanada

Personalised recommendations