STDSIM: A Microsimulation Model for Decision Support in STD Control

Catharina P. B. Van der Ploeg, Carina Van Vliet, Sake J. De Vlas, Jeckoniah O. Ndinya-Achola, Lieve Fransen, Gerrit J. Van Oortmarssen and J. Dik F. Habbema
Additional contact information
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

Interfaces, 1998, vol. 28, issue 3, 84-100

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.

Keywords: decision analysis; applications; health care; epidemiology (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:28:y:1998:i:3:p:84-100

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