Inferring model parameters in network-based disease simulation

Eva A. Enns, Margaret L. Brandeau

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)174-188
Number of pages15
JournalHealth Care Management Science
Volume14
Issue number2
DOIs
StatePublished - Jun 2011

Bibliographical note

Funding Information:
Acknowledgement 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.

Keywords

  • Concurrent partnerships
  • Network dynamics
  • Sexually transmitted diseases
  • Stochastic simulation

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