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Small world network models of the dynamics of HIV infection

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  • I. Vieira
  • R. Cheng
  • P. Harper
  • V. Senna

Abstract

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has recently been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • I. Vieira & R. Cheng & P. Harper & V. Senna, 2010. "Small world network models of the dynamics of HIV infection," Annals of Operations Research, Springer, vol. 178(1), pages 173-200, July.
    • Handle: RePEc:spr:annopr:v:178:y:2010:i:1:p:173-200:10.1007/s10479-009-0571-y
    DOI: 10.1007/s10479-009-0571-y
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    References listed on IDEAS

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    1. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    2. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
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    Cited by:

    1. Eva Enns & Margaret Brandeau, 2011. "Inferring model parameters in network-based disease simulation," Health Care Management Science, Springer, vol. 14(2), pages 174-188, June.
    2. Matthew Eden & Rebecca Castonguay & Buyannemekh Munkhbat & Hari Balasubramanian & Chaitra Gopalappa, 2021. "Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases," Health Care Management Science, Springer, vol. 24(3), pages 623-639, September.
    3. Olufolajimi Oke & Kavi Bhalla & David C. Love & Sauleh Siddiqui, 2018. "Spatial associations in global household bicycle ownership," Annals of Operations Research, Springer, vol. 263(1), pages 529-549, April.
    4. Benjamin Armbruster & Ekkehard Beck & Mustafa Waheed, 2014. "The importance of extended high viremics in models of HIV spread in South Africa," Health Care Management Science, Springer, vol. 17(2), pages 182-193, June.
    5. László Á. Kóczy, 2022. "Core-stability over networks with widespread externalities," Annals of Operations Research, Springer, vol. 318(2), pages 1001-1027, November.
    6. Nadia N Abuelezam & Kathryn Rough & George R Seage III, 2013. "Individual-Based Simulation Models of HIV Transmission: Reporting Quality and Recommendations," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-1, September.
    7. Flaer, Paul J. & Cistone, Peter J. & Younis, Mustafa Z. & Parkash, Jai, 2013. "A connectivity model for assessment of HIV transmission risk in injection drug users (IDUs)," Evaluation and Program Planning, Elsevier, vol. 39(C), pages 23-27.

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