1. Public Health Dynamics Laboratory Using computational models to advance the theory and practice of Public Health Mark S. Roberts, MD, MPP Professor and Chair, Health Policy and Management Director, PHDL
Briefing for Michelle Dunn, PhD
Senior Advisor, Data Science Training, Diversity and Outreach
Office of the Associate Director for Data Science
National Institutes of Health
PHDL Overview

2. Public Health Decision
Mental & Computational Models in Public Health
"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).
Prior individual knowledge
Mental Model
Public Health
Decision
Expert advice
Why open access to public health data is important NOW vs. previously:
- new methods for analysis
- more data collected
Statistical models
Mechanistic
Models & Simulations
Data
PHDL Overview

3. Mission of the Public Health Dynamics Lab
Develop interdisciplinary approaches using computational models to advance the theory and practice of Public Health
Contribute to "Systems Thinking" in the training of the next generation of health professionals
Develop and distribute computational tools to be used to improve real-world population health decision- making
PHDL Overview

4. “Systems Public Health”
A Systems Approach to Public Health
“Systems Public Health”
Scale
108 m Earth
106 m Country
104 m City
102 m Village
100 m Human
10-2m Organ
10-4 m Lymph Follicle
10-6 m Cell
10-8 m DNA
10-10 m Nucleotide
10-12 X Ray
10-14 Atomic Nucleus
“Systems Biology”
PHDL Overview

5. Sponsors and Partners Models of Infectious Disease Agent Study (MIDAS)
National Center of Excellence
PI: Burke
Sponsor: NIGMS/NIH
(Harvard, Washington, 12 others)
Vaccine Modeling Initiative
PI Burke
Sponsor: Bill & Melinda Gates Foundation
(Imperial and Princeton)
Public Health Adaptive Systems Studies
PI: Potter
Sponsor: CDC
Public Health International Modeling Fellows Program
PI: Grefenstette/Burke
Sponsor: Benter Foundation
Partners:
Benter
Foundation
PHDL Overview

6. FRED (Framework for Reconstructing Epidemiological Dynamics)
PHDL Overview

7. Census-matched Synthetic Population
PHDL Overview 7

8. Matched actual demographics
Iterative proportional fitting assures that synthetic attributes are distributed as real ones are

9. Individual-based models focus on how interactions among individual simulated agents can result in complex patterns at a population level. Here is a movie of our team’s first large scale model (of the possible introduction of avian influenza into the USA).
Each agent is explicitly modeled and tracked
Agents have individual features: age, sex, occupation, health
Agents interact with a limited set of other agents based on shared activity
Agents have bounded rationality and make use of realistic decision rules based on local information
Agents usually interact within an explicit geographical environment
Over time, agent interactions can generate large-scale macrosopic phenomena of fundamental interest
Ferguson NM, Cummings DA, Fraser C, Cajka JC, Cooley PC, Burke DS.
Strategies for mitigating an influenza epidemic
Nature July 27, 2006; 442:
PHDL Overview

10. Prediction – Pushing the Boundaries
PHDL Overview

11. Modeling to Inform Policy
We have seen several example of PHDL-derived tools used in policy
FRED used to estimate mitigation strategies in 2009 Flu epidemic
Tycho and FRED Measles to inform consequences of vaccination rates
Our expertise has provided a platform (PI Everette James) to conduct research for PA:
“University will assist DPW in monitoring and evaluating changes in health insurance coverage; access to care; health care and Medicaid financing; state and federal policies, regulations and legislation affecting health care; and the quality of health care delivery in the Commonwealth’s Medicaid program.”
PHDL Overview

12. FRED Measles
PHDL Overview

13. Fred Ages and Stages
PHDL Overview

14. Goal: High Resolution Detail in FRED
Diseases:
Diabetes
Heart Disease
Hypertension
Asthma
COPD
other chronic dz 
Environmental
Characteristics
Pollution
Walkability
Food deserts
PHDL Overview

15. Clinically complex model of Hepatitis C
Aspirational future
Social Network/
Behavioral Research .
Infection/Transmission Research . . . . . . . . . . . . . . . .
Cardiovascular Disease . . . . . . . .
Influenza . . . . . . . . . . . . . . . . . . . . . .
HIV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Clinically complex model of Hepatitis C . . . . . . . .
Geographic/Social Specificity
Legal and
Regulatory
Population
Behavior
Health Care
Simulation
Health Care
Resources
Engine
Costs
Disease
Interventions
in silico Pennsylvania
Agent-Based Simulation Model (FRED)
PHDL Overview 15