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STDSIM: A Microsimulation Model for Decision Support in STD Control

Author

Listed:
  • Catharina P. B. Van der Ploeg

    (Centre for Decision Sciences in Tropical Disease Control, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands)

  • Carina Van Vliet

    (Centre for Decision Sciences in Tropical Disease Control, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands)

  • Sake J. De Vlas

    (Centre for Decision Sciences in Tropical Disease Control, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands)

  • Jeckoniah O. Ndinya-Achola

    (Department of Medical Microbiology, University of Nairobi, PO Box 19676, Nairobi, Kenya)

  • Lieve Fransen

    (Commission of the European Communities, Rue de la Loi 200, B-1049 Brussels, Belgium)

  • Gerrit J. Van Oortmarssen

    (Centre for Decision Sciences in Tropical Disease Control, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands)

  • J. Dik F. Habbema

    (Centre for Decision Sciences in Tropical Disease Control, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands)

Abstract

STDSIM is a dynamic stochastic simulation model for decision support in attempts to control sexually transmitted diseases (STDs). It describes the mechanisms responsible for the transmission of five STDs, including HIV/AIDS, at the level of individuals. A large variety of outcome measures (for example, STD prevalence, incidence, and mortality) can be calculated for different groups of the population (for example, adolescents, prostitutes, pregnant women, and migrants) to evaluate the effects of alternative STD control interventions. We designed the model to support decision making for different settings in developing countries, with Nairobi (Kenya) as its first application area. The model adequately describes STD prevalences and the time-trend of HIV prevalence measured in Nairobi. As an illustration of the possibilities of the model, we show model predictions of the effects of improved STD care and increased condom use on the prevalence and incidence of HIV.

Suggested Citation

  • Catharina P. B. Van der Ploeg & Carina Van Vliet & Sake J. De Vlas & Jeckoniah O. Ndinya-Achola & Lieve Fransen & Gerrit J. Van Oortmarssen & J. Dik F. Habbema, 1998. "STDSIM: A Microsimulation Model for Decision Support in STD Control," Interfaces, INFORMS, vol. 28(3), pages 84-100, June.
    • Handle: RePEc:inm:orinte:v:28:y:1998:i:3:p:84-100
    DOI: 10.1287/inte.28.3.84
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    Citations

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    Cited by:

    1. Leigh F. Johnson & Rob Dorrington, 2006. "Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 14(22), pages 541-574.
    2. White, Leroy & Smith, Honora & Currie, Christine, 2011. "OR in developing countries: A review," European Journal of Operational Research, Elsevier, vol. 208(1), pages 1-11, January.
    3. Leigh Johnson & Rob Dorrington & Debbie Bradshaw & Victoria Pillay-Van Wyk & Thomas Rehle, 2009. "Sexual behaviour patterns in South Africa and their association with the spread of HIV: insights from a mathematical model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 21(11), pages 289-340.
    4. Robert S. Bernstein & David C. Sokal & Steven T. Seitz & Bertran Auvert & John Stover & Warren Naamara, 1998. "Simulating the Control of a Heterosexual HIV Epidemic in a Severely Affected East African City," Interfaces, INFORMS, vol. 28(3), pages 101-126, June.
    5. 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.

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