Skip to main content
SpringerLink
Log in

Comparing Two Sexual Mixing Schemes for Modelling the Spread of HIV/AIDS

  • Conference paper
Simulating Interacting Agents and Social Phenomena

Part of the book series: Agent-Based Social Systems ((ABSS,volume 7))

Abstract

In this paper we compare the impact of two sexual mixing schemes on the characteristics of the resulting sexual networks and the spread of HIV. This work is part of our studying social complexity in the Sekhukhune district of the Limpopo province in South Africa. While the agent-based models are constrained by evidence wherever possible, little or no evidence is available about individuals’ choice of partners in the region and their sexual behaviour. Since we therefore have to depend on plausible assumptions we decided to study different sexual mixing schemes and their effect on the formation of sexual networks. We report on some fundamental network signatures and discuss the resulting HIV/AIDS prevalence as a macro-level output of the simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Repast Agent Simulation Toolkit – http://repast.sourceforge.net/.

  2. 2.

    JESS (Java Expert System Shell) – http://www.jessrules.com/jess/.

  3. 3.

    This scheme is adapted from the mechanism originally proposed by Todd and Bilari [20].

  4. 4.

    Endorsements were first devised by Paul [6] as a device for resolving conflicts in rule-based expert systems. Scott [13] modified and extended their use within a model of learning by social agents. This latter version of endorsements has been adapted for the declarative model.

  5. 5.

    http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

  6. 6.

    See Moss [14] for further discussion on this subject.

  7. 7.

    Elsewhere, Alam [1] present a more elaborate and a contextualized enhancement of the first sexual mixing scheme. The results presented in this paper are based on the scheme described in our previous work [3].

References

  1. Alam SJ (2008) On understanding the social complexity in the context of HIV/AIDS: a case study in South Africa. Doctoral thesis, Center for Policy Modelling, Manchester Metropolitan University, Manchester, UK

    Google Scholar 

  2. Alam SJ, Meyer R, Ziervogel G, Moss S (2007) The impact of HIV/AIDS in the context of socioeconomic stressors: an evidence-driven approach. J Artif Soc Soc Simulat 10(4):7.http://jasss.soc.surrey.ac.uk/10/4/7.html

    Google Scholar 

  3. Alam SJ, Meyer R, Norling E (2009) A model for HIV spread in a South African village. In: Multi-agent-based simulation IX (MABS’08). LNAI, vol 5269. Springer, pp 33–45

    Chapter  Google Scholar 

  4. Buston PM, Emlen ST (2003) Cognitive processes underlying human mate choice: the relationship between self-perception and mate preference in Western society. Proc Natl Acad Sci USA 100(15):8805–8810

    Article  Google Scholar 

  5. Bearman PS, Moody J, Stovel K (2004) Chains of affection: the structure of adolescent romantic and sexual networks. Am J Sociol 110(1):44–91

    Article  Google Scholar 

  6. Cohen P (1985) Heuristic reasoning – an artificial intelligence approach. Pitman, Boston

    Google Scholar 

  7. Helleringer S, Kohler H-P (2007) Sexual network structure and the spread of HIV in Africa: evidence from Likoma Island, Malawi. AIDS 21(17):2323–2332

    Article  Google Scholar 

  8. Heuveline P, Sallach D, Howe T (2003) The structure of an epidemic: modelling AIDS transmission in Southern Africa. In: Proc. symposium on agent-based computational modelling, Vienna

    Google Scholar 

  9. Jin EM, Girvan M, Newman MEJ (2001) Structure of growing social networks. Phys Rev E 64:4

    Article  Google Scholar 

  10. Koopman JS et al (1997) The role of early HIV infection in the spread of HIV though populations. J Acquir Immune Defic Syndr Hum Retrovirol 14:249–258

    Article  Google Scholar 

  11. Kretzschmar M, Morris M (1996) Measures of concurrency in networks and the spread of infectious disease. Math Biosci 133(2):165–195

    Article  MATH  Google Scholar 

  12. Liljeros F, Edling CR, Amaral LAN (2003) Sexual networks: implications for the transmission of sexually transmitted infections. Microbes Infect 5:189–196

    Article  Google Scholar 

  13. Moss S (1995) Control metaphors in the modelling of decision-making behaviour. Comput Econ 8:283–301

    Article  MATH  Google Scholar 

  14. Moss S (2008) Simplicity, generality and truth in social modeling. In: Proc. second world congress on social simulation (WCSS’08), Fairfax, VA. http://cfpm.org/cpmrep187.html

  15. Newell M et al (2004) Mortality of infected and uninfected infants born to HIV-infected mothers in Africa: a pooled analysis. Lancet 364:1236–1243

    Article  Google Scholar 

  16. Nordvik MK, Liljeros F (2006) Number of sexual encounters involving intercourse and the transmission of sexually transmitted infections. Sex Transm Dis 33(6):342–349

    Article  Google Scholar 

  17. RADAR (2005) The RADAR IMAGE study – intervention with microfinance for AIDS and gender equity. http://web.wits.ac.za/Academic/Health/PublicHealth/Radar/

  18. Robins G, Pattison P, Woolcock J (2005) Small and other worlds: global network structures from local processes. Am J Sociol 110(4):894–936

    Article  Google Scholar 

  19. Simão J, Todd PM (2003) Emergent patterns of mate choice in human populations. Artif Life 9(4):403–417

    Article  Google Scholar 

  20. Todd PM, Billari FC (2003) Population-wide marriage patterns produced by individual mate-search heuristics. Agent-based computational demography: using simulation to improve our understanding of demographic behavior. Physica-Verlag, New York, pp 117–137

    Google Scholar 

  21. Wawer MJ et al (2005) Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 191:1403–1409

    Article  Google Scholar 

  22. Werth B, Geller A, Meyer R (2007) He endorses me – he endorses me not – he endorses me... contextualized reasoning in complex systems. In: Papers from AAAI Fall Symposium: emergent agents and socialities – social and organizational aspects of intelligence. AAAI technical report: FS-07-04

    Google Scholar 

  23. Ziervogel G et al (2006) Adapting to climate, water and health stresses: insights from Sekhukhune, South Africa. Stockholm Environmental Institute Report, Oxford, UK

    Google Scholar 

Download references

Acknowledgments

This work was done within the CAVES project, which was funded under the EU 6FP NEST programme. We are thankful to the editors, the reviewers and the participants of WCSS’08 for their feedback and comments. Thanks to Scott Moss, Bruce Edmonds, our colleagues at the Stockholm Environment Institute (Oxford) and at the Centre for Policy Modelling for their input and feedback over the course of the CAVES project and this work.

Author information

Authors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University Business School, Manchester, M1 3GH, United Kingdom

    Shah Jamal Alam & Ruth Meyer

Authors
  1. Shah Jamal Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruth Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shah Jamal Alam .

Editor information

Editors and Affiliations

  1. Department of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Keiki Takadama (Associate Professor) (Associate Professor)

  2. Center for Social Complexity, George Mason University, 4400 University Drive, MSN 6B2, Fairfax, Virginia, 22030, USA

    Claudio Cioffi-Revilla (Professor) (Professor)

  3. Head of LISC (Laboratoire d’Ingénierie des Systèmes Complexes), Cemagref 24 avenue des Landais, 50085, 63172, Aubière Cedex 1, France

    Guillaume Deffuant

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer

About this paper

Cite this paper

Alam, S.J., Meyer, R. (2010). Comparing Two Sexual Mixing Schemes for Modelling the Spread of HIV/AIDS. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol 7. Springer, Tokyo. https://doi.org/10.1007/978-4-431-99781-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-99781-8_5

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-99780-1

  • Online ISBN: 978-4-431-99781-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation