Agent-based modeling of chronic diseases: A narrative review and future research directions

Yan Li, Mark A. Lawley, David S. Siscovick, Donglan Zhang, José A. Pagán

Research output: Contribution to journalArticlepeer-review


The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Original languageEnglish (US)
Article number150561
JournalPreventing Chronic Disease
Issue number5
StatePublished - May 1 2016

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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