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Epidemic potential by sexual activity distributions

Published online by Cambridge University Press:  24 April 2017

JAMES MOODY
Affiliation:
Department of Sociology, Duke University, Durham, NC, USA Department of Sociology, King Abdulaziz University, Jedda, Saudi Arabia (e-mail: jmoody77@soc.duke.edu)
JIMI ADAMS
Affiliation:
Department of Health and Behavioral Sciences, University of Colorado Denver, USA (e-mail: jimi.adams@ucdenver.edu)
MARTINA MORRIS
Affiliation:
Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA (e-mail: morrism@u.washington.edu)

Abstract

For sexually transmitted infections like HIV to propagate through a population, there must be a path linking susceptible cases to currently infectious cases. The existence of such paths depends in part on the degree distribution. Here, we use simulation methods to examine how two features of the degree distribution affect network connectivity: Mean degree captures a volume dimension, while the skewness of the upper tail captures a shape dimension. We find a clear interaction between shape and volume: When mean degree is low, connectivity is greater for long-tailed distributions, but at higher mean degree, connectivity is greater in short-tailed distributions. The phase transition to a giant component and giant bicomponent emerges as a positive function of volume, but it rises more sharply and ultimately reaches more people in short-tail distributions than in long-tail distributions. These findings suggest that any interventions should be attuned to how practices affect both the volume and shape of the degree distribution, noting potential unanticipated effects. For example, policies that primarily affect high-volume nodes may not be effective if they simply redistribute volume among lower degree actors, which appears to exacerbate underlying network connectivity.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

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References

adams, j., Moody, J. J., & Morris, M. (2013). Sex, drugs, and race: How behaviors differentially contribute to sexually transmitted infection risk network structure. American Journal of Public Health, 103 (2), 322–29.CrossRefGoogle ScholarPubMed
Amaral, L. A. N., Scala, A., Barthelemy, M., & Stanley, H. E. (2000). Classes of small world networks. Proceedings of the National Academy of Science, 97 (21), 11149–52.CrossRefGoogle ScholarPubMed
Armbruster, B., Wang, L., & Morris, M. (2016). Forward reachable sets: Analytically derived properties of connected components for dynamic networks. Network Science, forthcoming.Google Scholar
Artzrouni, M. (2009). Transmission probabilities and reproduction numbers for sexually transmitted infections with variable infectivity: Application to the spread of HIV between low- and high-activity populations. Mathematical Population Studies, 16 (4), 266287.CrossRefGoogle Scholar
Barabasi, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286 (5439), 509512.Google Scholar
Brewer, D. D., Potterat, J., Garrett, S. B., Muth, S. Q., Roberts, J. M., Kasprzyk, D., . . . William, W. (2000). Prostitution and the sex discrepancy in reported number of sexual partners. Proceedings of the National Academy of Sciences, 97 (22), 1238512388.Google Scholar
Carnegie, N. B., & Morris, M. (2012). Size matters: Concurrency and the epidemic potential of HIV in small networks. PLoS ONE, 7 (8), e43048.Google Scholar
De, P., Singh, A. E., Wong, T., Yacoub, W., & Jolly, A. M. (2004). Sexual Network Analysis of a gonorrhea outbreak. Sexually Transmitted Infections, 80, 280285.CrossRefGoogle ScholarPubMed
Dezso, Z., & Barabasi, A. L. (2002). Halting viruses in scale-free networks. Physical Review E, 65, 055103.CrossRefGoogle ScholarPubMed
Dombrowski, K., Curtis, R., Friedman, S., & Khan, B. (2013). Topological and historical considerations for infectious disease transmission among injection drug users in Bushwick, Brooklyn (USA). World Journal of AIDS, 3, 19.CrossRefGoogle ScholarPubMed
Ferrari, M. J., Bansal, S., Meyers, L. A. & Bjørnstad, O. N. (2006). Network frailty and the geometry of herd immunity. Proceedings of the Royal Society B: Biological Sciences, 273 (1602), 27432748.Google Scholar
Hamilton, D. T., Handcock, M. S., & Morris, M. (2008). Degree distributions in sexual networks: A framework for evaluating evidence. Sexually Transmitted Diseases, 35 (1), 3040.CrossRefGoogle ScholarPubMed
Harary, F. (1969). Graph theory. Reading, Massachusetts: Addison-Wesley.CrossRefGoogle Scholar
Helleringer, S., & Kohler, H.-P. (2007). Sexual network structure and the spread of HIV in Africa: Evidence from Likoma Island, Malawi. AIDS, 21 (17), 23232332.CrossRefGoogle ScholarPubMed
Hethcote, H. (2000). The mathematics of infectious diseases. SIAM Review, 42 (42), 599653.Google Scholar
Holmes, K. K., Sparling, P. F., Mardh, P.-A., Lemon, S. M., Stamm, W. E., Piot, P., & Wasserheit, J. N. (1999). Sexually transmitted diseases. New York: McGraw Hill.Google Scholar
Jones, J., & Handcock, M. (2003). Sexual contacts and epidemic thresholds. Nature, 423, 605606.Google Scholar
Koumans, E. H., Farley, T. A., & Gibson, J. J. (2001). Characteristics of persons with syphilis in areas of persisting syphilis in the United States–-sustained transmission associated with concurrent partnerships. Sexually Transmitted Diseases, 28 (9), 497503.Google Scholar
Laumann, E. O., Gagnon, J. H., Michael, R. T., & Michaels, S. (1994). The social organization of sexuality: Sexual practices the United States. Chicago: University of Chicago Press.Google Scholar
Liljeros, F., Edling, C. R., Nunes Amaral, L. A., Eugene Stanley, H., & Aberg, Y. (2001). The web of human sexual contacts. Nature, 411, 907908.Google Scholar
Molloy, M., & Reed, B. (1998). The size of the largest component of a random graph on a fixed degree sequence. Combinatorics, Probability and Computing, 7, 295306.CrossRefGoogle Scholar
Moody, J. (2002). The importance of relationship timing for diffusion. Social Forces, 81 (1), 2556.CrossRefGoogle Scholar
Moody, J., & Benton, R. (2016). Interdependent effects of cohesion and concurrency for epidemic potential. Annals of Epidemiology, 26 (4), 241248.CrossRefGoogle ScholarPubMed
Moody, J., & White, D. R. (2003). “Social cohesion and embeddedness: A hierarchical conception of social groups. American Sociological Review, 68 (1), 103127.CrossRefGoogle Scholar
Morris, M. (1997). Sexual networks and HIV. AIDS 97: Year in Review, 11 (Suppl A), S209S216.Google Scholar
Morris, M., Epstein, H., & Wawer, M. (2010). Timing is everything: International variations in historical sexual partnership concurrency and HIV prevalence. Plos One, 5 (11), e14092. doi:10.1371/journal.pone.0014092.Google Scholar
Morris, M., & Kretzschmar, M. (1997). Concurrent partnerships and the spread of HIV. AIDS, 11, 641648.CrossRefGoogle ScholarPubMed
Morris, M., Goodreau, S., & Moody, J. (2007). Sexual networks, concurrency, and STD/HIV. In Holmes, K. K (Eds.), Sexually transmitted diseases (4th ed.) (pp. 109126). New York: McGraw-Hill.Google Scholar
Morris, M., Kurth, A. E., Hamilton, D. T., Moody, J., & Wakefield, S. (2009). Concurrent partnerships and HIV prevalence disparities by race: Linking science and public health. American Journal of Public Health, 99 (6), 10231031.Google Scholar
Newman, M. E. J., Strogatz, S. J., & Watts, D. J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64, 026118.Google Scholar
Newman, M. (2003). The spread of epidemic disease on networks. Physical Review E, 66 (1), 016128.Google Scholar
Potterat, J. H., Zimmerman-Rogers, H., Muth, S., Rothenberg, R., Green, D., Taylor, J., . . . White, H. (1999). Chlamydia transmission: concurrency, reproduction number and the epidemic trajectory. American Journal of Epidemiology, 150 (12), 13311339.Google Scholar
Rothenberg, R. B., Potterat, J. J., Woodhouse, W. W., Darrow, S. Q., Muth, S. Q., & Klovdahl, A. S. (1995). Choosing a centrality measure: Epidemiologic correlates in the colorado springs study of social networks. Social Networks, 17, 273297.Google Scholar
Rothenberg, R. B., Potterat, J. J., Woodhouse, D. E., Muth, S. Q., Darrow, W. W., & Klovdahl, A. S. (1998). Social network dynamics and HIV transmission. Aids, 12 (12), 15291536.Google Scholar
Todd, J., Cremin, I., McGrath, N., Bwanika, J. B., Wringe, A., Marston, M.,. . .Zaba, B. (2009). Reported number of sexual partners: Comparison of data from four African longitudinal studies. Sexually Transmitted Infections, 85 (Suppl 1), i72i80. doi: 10.1136/sti.2008.033985.CrossRefGoogle ScholarPubMed
Young, A. M., Halgin, D. S., DiClemente, R. J., Sterk, C. E., & Havens, J. R. (2014). Will Hiv vaccination reshape Hiv risk behavior networks? a social network analysis of drug users' anticipated risk compensation. PLoS One, 9 (7), e101047.Google Scholar