Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

Details

• Journal Article:
  Preventing Chronic Disease (PCD)
• Personal Author:
  Li, Yan ; Lawley, Mark A. ; Siscovick, David S. ; Zhang, Donglan ; Pagán, José A.
• Description:
  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.
• Subjects:
  Tools and Techniques
• Source:
  Prev Chronic Dis. 13.
• DOI:
  http://dx.doi.org/10.5888/pcd13.150561
• ISSN:
  1545-1151
• Pubmed ID:
  27236380
• Pubmed Central ID:
  PMC4885681
• Document Type:
  Journal Article
• Volume:
  13
• Collection(s):
  Preventing Chronic Disease
• Main Document Checksum:
  urn:sha-512:a69d3dbe40f96130c2d8200992b91d7009f2ba8dfa114a10dcc4ddfb2053e2eff05732f361755835a693de8fc944a3f51959ddf33b4cf2b8bf1522ad98c64fbd
• Download URL:
  https://stacks.cdc.gov/view/cdc/39729/cdc_39729_DS1.pdf
• File Type:
  [PDF - 293.05 KB ]