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Syllabus NUT 212 SSinPH 2016_11JAN2016.docx

NUT 212: Systems Science in Public Health

Winter Session 2016

M-F, 8:30-11:30 or 12:00 noon, January 11-15, 2016

Location of Class—Kresge 202a

Instructors Information

Course Director:

Matthew W. Gillman, MD, SM

Professor of Population Medicine, HMS

Professor in the Department of Nutrition, HSPH

133 Brookline Ave., 3rd floor Annex, Boston, MA 02215

matthew_gillman@hms.harvard.edu

Assistant: Julie McGrath

Julie_McGrath@hphc.org

617-509-9968

 

Co-Director:

Ross A. Hammond, PhD

Director, Center on Social Dynamics & Policy

Senior Fellow, Economics

The Brookings Institution

Adjunct Associate Professor in the Department of Nutrition, HSPH

1775 Massachusetts Ave NW

Washington DC 20036

rhammond@brookings.edu

Assistant: Monica Maday

mmaday@brookings.edu

202-797-6000

 

Guest Instructor:

Judy Maro, PhD

Instructor of Population Medicine, HMS

133 Brookline Ave., 3rd floor Annex, Boston, MA 02215

jmaro@mit.edu

617-509-9794

 

Teaching Fellow:                    

Matt Kasman, PhD

Research Associate, Center on Social Dynamics & Policy

The Brookings Institution

1775 Massachusetts Ave NW

Washington DC 20036

mkasman@brookings.edu

303-819-2740

 

Office hours:           

Monday—1130am-1230pm (Drs. Gillman and Hammond), Kresge cafeteria.

12:30-2pm by appointment (Dr. Kasman), Building 2, Room 213

 

Tuesday—noon-1 pm (Drs. Maro, Gillman). Kresge cafeteria.

1-2:30pm by appointment (Dr. Kasman), Building 2, Room 213

 

Wednesday—noon-1 pm (Dr. Hammond). Kresge cafeteria

1-2:30pm by appointment (Dr. Kasman), Building 2, Room 213

 

Thursday—11:30am-12:30pm (Drs Gillman, Hammond). Kresge cafeteria

3:30-5 pm by appointment (Dr. Kasman), Building 2, Room 213

Learning Objectives

The overall goal is to introduce two major systems science techniques with wide applicability to public health—agent-based modeling and system dynamics. We will also introduce concepts of network analysis as they apply to ABM and SD. We will give an introductory overview of each technique, examples of applications, brief discussion of best practices, and some initial hands-on lab experience. At the completion of the course the student will be able to

  • Explain the current and potential future roles of systems science in chronic disease etiology and prevention.
  • Describe the two major approaches to analysis of systems, their advantages, disadvantages, and potential applications.
  • Evaluate literature that incorporates systems science approaches in public health.
  • Design and present a conceptual modeling project applying one of the approaches to a specific public health question.

 

Outcome Measures

Assignments to measure the students’ competence in the course objectives above:

Students will form small groups—3 to a group— to complete a conceptual modeling project for the course, centered around the two techniques and a public health topic area. They will present their project to the class.

Each student or group (TBD) will submit a (min.) 5-page double-spaced paper. It should describe the group project, and each group member’s contribution.

 

Grading Criteria

Pass/fail, based on the project presentation (30%) and paper (70%). Each member of the presentation will get the same grade.

 

Additional Information
Students are required to bring their own laptops, and to install a free software package used in the course; instructions to come (wide compatibility with Windows, Mac OS).

 

Student Course Evaluations

Completion of the evaluation is a requirement for each course.  Your grade will not be available until you submit the evaluation.  In addition, registration for future terms will be blocked until you have completed evaluations for courses in prior terms. 

 

The course is limited to 20 students.

Note on Readings

You can get most of the readings from PubMed. For the ones you can’t (marked with *), try the following: log onto eCommons→ Hollis Catalog→ Search&Find→ Articles→ Google Scholar.

There’s one reading that is no longer available on line (Schelling T. Micromotives and Macrobehavior. New York: Norton and Company. 1978. 11-44), so we’ve posted a pdf on the course website.

Daily Schedule and Readings

Day 1, Monday, January 11, 2016: Introduction and Background (3 hrs)

  • Course introduction and key aims (Gillman)
  • What is systems science? Focus on three important techniques (ABM, SD, networks) and a very brief overview of each (Hammond)
  • Potential advantages for public health. Overview of uses in public health to date.
  • Comparison to other analytical techniques, philosophy of science and the role of modeling
  • Formation of small groups for projects (Instructors)

 

Core readings:

  1. Axelrod R. Advancing the Art of Simulation in the Social Sciences. In Handbook of Research on Nature Inspired by Computing for Economy and Management. Rennard JP (Ed.) 2005, Hersey, PA: Idea Group. http://wwwpersonal.umich.edu/~axe/research/AdvancingArtofSim.pdf
  1. Epstein J. Why Model? Journal of Artificial Societies and Social Simulation 2008; 11:42. http://jasss.soc.surrey.ac.uk/11/4/12.html
  1. *Hammond R. Complex Systems Modeling for Obesity Research. Preventing Chronic Disease 2009; 6:1-10.
  1. *IOM (Institute of Medicine). Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation. Washington, DC: The National Academies Press, Appendix B. 2012.

Day 2, Tuesday, January 12, 2016: System Dynamics (3.5 hrs)

Guest instructor: Judy Maro

  • What is System Dynamics and Why System Dynamics
  • Introduction to Netlogo
  • LAB: Hands on exercises
  • Meeting of small project groups and course instructors for feedback and questions (Instructors)

Core reading:

  1. Sterman JD. Learning from Evidence in a Complex World. Am J Public Health. 2006; 96:505–514.
  1. Homer JB, Hirsch GB. System Dynamics Modeling for Public Health: Background and Opportunities. Am J Public Health 2006; 96:452-458.
  1. Hall KD, Sacks G, Chandramohan D, et al. Quantification of the effect of energy imbalance on bodyweight. Lancet. 2011;378(9793):10.1016/S0140-6736(11)60812-X. doi:10.1016/S0140-6736(11)60812-X.
  1. Fallah-Fini S, Rahmandad H, Huang TT, Bures RM, Glass TA. Modeling US adult obesity trends: a system dynamics model for estimating energy imbalance gap. Am J Public Health. 2014 Jul;104(7):1230-9.

 

Day 3, Wednesday, January 13, 2016: Agent-based modeling (3.5 hrs)

  • What is ABM and Why ABM (Hammond)
  • Further introduction to Netlogo
  • LAB: Hands on exercises

 

Core reading:

  1. *Schelling T. Micromotives and Macrobehavior. New York: Norton and Company. 1978. 11-44.
  1. 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; 168:1-8.
  1. Eubank S, et al. Modelling disease outbreaks in realistic urban social networks. Nature 2004; 429:180-184.
  1. Hammond RA and Ornstein J. “A model of social influence on body weight”. Ann N Y Acad Sci 1331:34-42 (2014)

 

Day 4, Thursday, January 8, 2015: Network Analysis (3 hrs)

  • What is Network Analysis, including Social Network Analysis, and types of applications (Hammond)
  • LAB: Hands on exercises, working with basic network concepts
  • LAB: Combining networks and ABM
  • Overview of the week, and questions

 

Core reading:

  1. Barabási AL, Albert R. Emergence of scaling in random networks. Science 1999; 286: 509.
  1. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med; 2007; 357: 370-379.
  1. *Granovetter MS. The strength of weak ties. American Journal of Sociology. 1973; 78(6): 1360.

 

Day 5, Friday, January 9, 2015: Small Group Projects (3 hrs)

  • Presentation and Discussion of small group projects
  • Next steps and Resources

PLEASE—Complete your course evaluation asap.

 

 

Supplementary Reading Materials

Barabasi A. Network Medicine - From Obesity to the "Diseasome". N Engl J Med 2007; 404-407

Centola D. Social media and the science of health behavior. Circulation. 2013; 127(21):2135-44

*Ellison NB, Steinfield C, Lampe, C. The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer Mediated Communication 2007; 12:1143-1168.

Epstein J, et al. Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations. PLoS ONE 2008; 3(12): e3955.

Epstein, J. “Modelling to Contain Pandemics.” Nature 2009; 460:687.

*Ford DN, Sterman JD. Expert knowledge elicitation to improve formal and mental models. System Dynamics Review 1998; 14(4): 309-340.

Hammond RA. “Considerations and Best Practices in Agent-based Modeling to Inform Policy”. In Assessment of Agent-based Models to Inform Tobacco Policy: Institute of Medicine, National Academy of Sciences Press (2015).

Hammond RA, Dube L. A systems science perspective and transdisciplinary models for food and nutrition security. Proc. Natl. Acad. Sci. U.S.A. 2012; 109: 12356–12363.

Hammond RA, Ornstein JT, Fellows LK, Dube L, Levitan R, Dagher A. A model of food reward learning with dynamic reward exposure. Front. Comput. Neurosci. 2012; 6:82.

Harris JK, Luke DA, Zuckerman, R, Shelton SC. Forty years of secondhand smoke research: The gap between discovery and delivery. American Journal of Preventive Medicine 2009; 36:538-548.

Hawn C. Take two aspirin and tweet me in the morning: How Twitter, Facebook, and other social media are reshaping health care. Health Affairs 2009; 28: 361-368.

Hawe P, Webster C, Shiell A. A glossary of terms for navigating the field of social network analysis. Journal of Epidemiology & Community Health 2004; 58(12): 971-975.

Homer JB, Hirsch GB. System Dynamics Modeling for Public Health: Background and Opportunities. Am J Public Health 2006; 96:452-458.

Homer J, Milstein B, Labarthe D, et al. Simulating and Evaluating Local Interventions to Improve Cardiovascular Health. Preventing Chronic Disease. 2010;7(1):A18.

*Homer JB, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the Local Dynamics of Cardiovascular Health: Risk Factors, Context, and Capacity. Preventing Chronic Disease April 2008; 5(2). Available at: http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm.

Kaplan EH, Craft DL, Wein LM. Emergency response to a smallpox attack: The case for mass vaccination. PNAS 2002; 99: 10935–10940

*Howison J, Wiggins A, Crowston K. Validity Issues in the Use of Social Network Analysis with Digital Trace Data. Journal of the Association for Information Systems 2011; 12(12):767-797

*IOM (Institute of Medicine). Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation. Washington, DC: The National Academies Press. 2012.

Luke DA, Stamatakis KA. Systems Science Methods in Public Health: Dynamics, Networks, and Agents. Annu. Rev. of Public Health 2012; 33: 357-376.

Mabry PL, Marcus SE, Clark PI, Leischow SJ, Mendez D. Systems science: a revolution in public health policy research. Am. J. Public Health 2010; 100:1161–1163.

Metcalf SS, Northridge ME, Lamster IB. A System Perspective for Dental Health in Older Adults. American Journal of Public Health 2011; 101:1820-1822

Shoham DA, Tong L, Lamberson PJ, et al. An actor-based model of social network influence on adolescent body size, screen time, and playing sports. PLoS One. 2012;7(6):e39795.

*Sterman JD. Learning from Evidence in a Complex World. Am J Public Health. 2006; 96:505–514.

*Watts DJ. The “New” science of networks. Annual Review of Sociology 2004; 30: 243-70.

Course Summary:

Course Summary
Date Details Due