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Explicit, Or Not, Every Decision is Based on a Model Don Burke Dean, Graduate School of Public Health University of Pittsburgh Institute for Systems Science.

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Presentation on theme: "Explicit, Or Not, Every Decision is Based on a Model Don Burke Dean, Graduate School of Public Health University of Pittsburgh Institute for Systems Science."— Presentation transcript:

1 Explicit, Or Not, Every Decision is Based on a Model Don Burke Dean, Graduate School of Public Health University of Pittsburgh Institute for Systems Science and Health 22 May 2011

2 1. Systems thinking, modeling, and dynamics 2. Toy (simple) epidemic models 3. Serious H1N1 pandemic decision support models 4. Health behavior models 5. In defense of modeling 6. Conclusions Outline

3 Jay Forrester (1918- ) MIT Professor of Computer Science and Management Founder of the field of System Dynamics "All decisions are made on the basis of models. Most models are in our heads. Mental models are not true and accurate images of our surroundings, but are only sets of assumptions and observations gained from experiences... Computer simulation models can compensate for weaknesses in mental models" (Forrester, 1994).

4 System Dynamics “System dynamics is the necessary foundation underlying effective thinking about systems”- Jay Forrester, 1999

5 Time Animals Example: Classical Predator-Prey Oscillations in Ecological Systems (Lotka–Volterra)

6 Example: Linked Oscillators in Mechanical Systems




10 BusinessWeek / Best Leaders

11 Prediction can be very difficult... especially if you are trying to predict the future Neils Bohr Physicist



14 Some Systems ● Solar System ● Eco System ● Health Care System ● Financial System

15 10^8 m Earth 10^6 m Country 10^4 m City 10^2 m Village 10^0 m Human 10^-2 Tonsil 10^-4 Lymph follicle 10^-6 Cell 10^-8 DNA 10^-10 Nucleotide 10^-12 X ray 10^-14 Atomic nucleus Systems Biology “Systems Public Health”

16 Association of Schools of Public Health “Systems Thinking” Competency

17 Competencies: Upon graduation a student with an MPH should be able to… 1. Identify characteristics of a system. 2. Identify unintended consequences produced by changes made to a public health system. 3. Provide examples of feedback loops and “stocks and flows” within a public health system. 4. Explain how systems (e.g. individuals, social networks, organizations, and communities) may be viewed as systems within systems in the analysis of public health problems. 5. Explain how systems models can be tested and validated. 6. Explain how the contexts of gender, race, poverty, history, migration, and culture are important in the design of interventions within public health systems. 7. Illustrate how changes in public health systems (including input, processes, and output) can be measured. 8. Analyze inter-relationships among systems that influence the quality of life of people in their communities. 9. Analyze the effects of political, social and economic policies on public health systems at the local, state, national and international levels. 10. Analyze the impact of global trends and interdependencies on public health related problems and systems. Systems Thinking

18 Wordle “Systems Thinking Competencies / ASPH” by don burke

19 Toy (simple) models of system dynamics in public health

20 Growing Artificial Societies Joshua M. Epstein Robert Axtell Brookings Institution 1996 “If you can’t grow it, you don’t understand it.” Josh Generative Social Science Joshua M. Epstein Princeton Univ Press 2007

21 Smallpox modeling: Building Individual-based social structures and contact networks

22 Computer screen at start of model run: one infected individual [N.B. “night-time” = all individuals at home, not at work or school morgue hosp

23 Next Step: Scale-up of social networks & change from cartoon to “real” social structures

24 Models of Infectious Disease Agent Studies NIH/NIGMS National Center of Excellence Pitt ---- PSC ---- CMU Imperial---Hopkins---Brookings

25 H1N1 pandemic decision support using large scale agent based simulations

26 Model of a USA pandemic Ferguson NM, Cummings DA, Fraser C, Cajka JC, Cooley PC, Burke DS. Strategies for mitigating an influenza epidemic Nature July 27, 2006; 442:

27 FRED: Framework for Reconstruction of Epidemic Dynamics FRED Core FRED Simulation Engine FRED Simulation Engine Synthetic Population Synthetic Population Pathogen Parameters Pathogen Parameters Intervention Policies Intervention Policies Behavior Change Model Analysis and Visualization Tools Analysis and Visualization Tools Vaccination Antivirals School Closure Preventive Behaviors GAIA FRANCIS Health Belief Model Social Network Influences Natural History, Viral Evolution FRED Web Service FRED Web Service FRED Web Page FRED Client FRED Client FRED Interface FRED Core Request DB Request DB Request Queue Request Queue Results DB Results DB Simulation Information Management System (FRED SIMS) PSC

28 Pennsylvania Visualization

29 Don Burke, PI Ron Vorhees, Allegheny County Epidemiologist Rick Zimmerman, Community Health Physician John Grefenstette, Computer Scientist Cho-Cho Lin, Economist Sandra Quinn, Behavioral Scientist Jim Stark, Epidemiology Graduate student Shanta Zimmer, Infectious Disease Physician Shawn Brown, Computational Scientist Roni Rosenfeld, Computer Scientist Maggie Potter, Lawyer & Public Health Practice Bruce Lee, Internal Med Physician & Operations Research Bruce or Shawn on phone in DC Typical MIDAS - ASPR /BARDA decision support team meeting at Pitt

30 Washington DC Metro Visualization

31 Behavior Change Theories Health Belief Model Trans-Theoretical Model Social Cognitive Theory Theory of Planned Behavior Social Ecological Model

32 I can calculate the motions of heavenly bodies, but not the madness of people. Isaac Newton, 1721

33 Cases Decision to Stay Home Child Stays Home Cases


35 In defense of modeling Some tips on how to win over the skeptics

36 1. What is a model?

37 Rene Magritte, 1929

38 Ceci n’est pas une epidemie

39 2. Can you trust a model?

40 George E. P. Box Robustness in the Strategy of Scientific Model Building 1979 “All models are wrong, some are useful”


42 3. Aren’t models totally dependent on the quality of data?

43 “Garbage in, garbage out” George Fuechsel IBM technician/instructor in New York ca 1956

44 This is just a true for passive non-computational mental models as it is for computationally explicit models AND it is arguably better to be explicit about your garbage than blissfully ignore it MODELERS STOP APOLOGIZING ! “Garbage in, garbage out”

45 OK Where do we go now?

46 EPISTEMOLOGY The branch of philosophy concerned with the nature and scope ( and limitations) of knowledge. It addresses the questions: What is knowledge? How is knowledge acquired? How do we know what we know?


48 A Bit About The University of Pittsburgh Public Health Dynamics Laboratory Approach Organization Support Output

49 Systems Thinking Data mining Time series decomposition Social networks GIS Game theory Remote sensing Machine learning & A.I. High-performance computing Info Sci Epid Biostat Math Comp Sci Indust Engin Enviro Engin Behav Law Econ Policy Data Patterns & Parameters Equation Based Models Agent & Network Models Modeling of Interventions Cost Benefit Analyses Modeling of Pragmatics Decision Modeling Steps Academic Disciplines Software Philos Benter Foundation Support / Clients The Pitt Public Health Dynamics Lab


51 Thank you for your attention, and welcome to the University of Pittsburgh !

52 END

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