Agent-Based Models for Population Health

Thursday, June 1, 2017 - Friday, June 30, 2017  

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Course Description


This course will provide an introduction to agent-based models and their application to population health research and health policy. Agent-based models utilize stochastic computer simulations to observe the population-level outcomes and patterns of behavior produced when heterogeneous agents interact with other agents and their environment according to preset rules. These models circumvent many of the limitations of traditional analytic approaches and are increasingly used to investigate the spread of health behaviors and outcomes, as well as to compare interventions and policies to promote population health. Topics covered will include the properties of agent-based models, including illustrations from the health and social sciences; the types of questions best answered by agent-based models; the steps involved in developing, calibrating, and validating agent-based models; and the presentation and interpretation of model results. NetLogo software will be used to demonstrate and practice agent-based modeling techniques. Participants will also have the opportunity to propose and receive feedback on an agent-based model addressing their own research question of interest.


Course Objectives


The primary objective of this course is to provide students with an understanding of the properties and uses of agent-based models for public health policy and research. To reach that objective, participants will gain exposure to classic and current agent-based models in the health and social science literature, review best practices in the development and description of agent-based models, and practice building a simple agent-based model using NetLogo software.


By the end of the course, participants will be able to:


  1. Discuss the properties of agent-based models and their strengths and limitations for population health research.
  2. Formulate research questions that can feasibly be evaluated using agent-based models.
  3. Describe the steps involved in developing and verifying agent-based models, including parameterization, calibration, validation, and sensitivity analyses.
  4. Identify the optimal presentation of results from an agent-based model.
  5. Critically review studies using agent-based models.
  6. Plan and begin programming an agent-based model to address a particular research question.





Some introductory epidemiology and biostatistics knowledge will be assumed. No prior experience with agent-based modeling or NetLogo is expected. Participants will need access to a computer with high-speed Internet access and NetLogo software (version 5.3.1). NetLogo is available for both Windows and Mac operating systems and can be downloaded for free at



Course Reading List


The following readings provide an introduction to the need for and use of agent-based models for population health research, as well as examples that will be discussed in class:


  • Auchincloss AH, Diez Roux AV. A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. Am J Epidemiol 2008; 168(1): 1-8.
  • Gorman DM, Mezic J, Mezic I, Gruenewald PJ. Agent-based modeling of drinking behavior: a preliminary model and potential applications to theory and practice. Am J Public Health 2006; 96: 2055-2060.
  • Kumar S, Grefenstette JJ, Galloway D, Albert SM, Burke DS. Policies to reduce influenza in the workplace: Impact assessments using an agent-based model. Am J Public Health 2013; 103: 1406-1411.
  • Yonas MA, Burke JG, Brown ST, Borrebach JD, Garland R, Burke DS, Grefenstette JJ. Dynamic simulation of crime perpetration and reporting to examine community intervention strategies. Health Education & Behavior 2013; 40(1S): 87S-97S.



The following textbook provides an excellent introduction to agent-based modeling and the use of NetLogo and will be referenced throughout the course:


  • Railsback SF, Grimm V. Agent-based and individual-based modeling: a practical introduction. 2002, Princeton University Press.


Extensive documentation for NetLogo software is available at the following website:




Melissa Tracy, PhD

Melissa Tracy is an Assistant Professor in the Department of Epidemiology and Biostatistics at the University at Albany (SUNY) School of Public Health. Her work uses novel methods to investigate the social factors that influence trajectories of mental health and substance use. She has developed agent-based models to study mental health outcomes after mass traumatic events and to compare interventions aimed at reducing racial disparities in violence and posttraumatic stress disorder. She taught Agent-based Models for Population Health during EPIC 2014, 2016 and 2017.

Course Fee

Registration for this full-length online course is $900.00



The registration period has closed for this event.

Online Course Format


This is a month-long digital course, equivalent to approximately 20 hours of classroom instruction. Lectures and course material will be presented online in roughly weekly segments. The flexible format will include video or audio recordings of lecture material, file sharing and topical discussion, self-assessment exercises, real-time electronic office hours and access to the instructor for feedback during the course. The course utilizes the learning management software, Canvas (; participants will receive an e-mail inviting them to join on the first day of the course. Any additional information about technical requirements and access to the course will be shared in the weeks before the course begins.


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