Health Care Management Science

, Volume 14, Issue 2, pp 174–188 | Cite as

Inferring model parameters in network-based disease simulation



Many models of infectious disease ignore the underlying contact structure through which the disease spreads. However, in order to evaluate the efficacy of certain disease control interventions, it may be important to include this network structure. We present a network modeling framework of the spread of disease and a methodology for inferring important model parameters, such as those governing network structure and network dynamics, from readily available data sources. This is a general and flexible framework with wide applicability to modeling the spread of disease through sexual or close contact networks. To illustrate, we apply this modeling framework to evaluate HIV control programs in sub-Saharan Africa, including programs aimed at concurrent partnership reduction, reductions in risky sexual behavior, and scale up of HIV treatment.


Stochastic simulation Network dynamics Concurrent partnerships Sexually transmitted diseases 



This research was funded by the National Institute on Drug Abuse, Grant Number R01-DA15612. Eva Enns is supported by a National Defense Science and Engineering Graduate Fellowship, a National Science Foundation Graduate Fellowship, and a Rambus Inc. Stanford Graduate Fellowship.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Electrical EngineeringStanford UniversityStanfordUSA
  2. 2.Department of Management Science & EngineeringStanford University, Huang Engineering CenterStanfordUSA

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