Agent-Based Modeling for

Public Health

Jay Schindler, PhD

Northrop Grumman CorporationPublic Health Division

Atlanta, GAjay.schindler@ngc.com

John H. Holmes, PhD

University of Pennsylvania School of Medicine

Center for Clinical Epidemiology and Biostatistics

Center for Public Health Initiatives

jhholmes@mail.med.upenn.edu

Beginnings

• Welcome

• Introduction

• Audience Assessment

• Course Overview

Schedule for today

• Session 1– Introduction to agent-based modeling

– Introduction to NetLogo

– Modeling and simulation process (part 1)

• Break

• Session 2– Modeling and simulation process (part 2)

– Developing an agent-based model with NetLogo

– Wrap-up

Introduction to

Agent-Based Modeling

“The purpose of science is not to analyze or descr ibe,

but to make useful models of the wor ld. A model is

useful if it allows us to get use out of it .”

--Edward de Bono

“Essentially, all models are wrong, but some are

useful.” --George Box

• Modeling is creating and using a simplified

representation of objects, processes, and

environments. This can help to better…

– Identify, describe, and understand the

complex interactions and relationships in

dynamic systems

– Communicate and share relevant

information or knowledge pertinent to specific

issues or domains

– Predict potential outcomes or results of a

dynamic system based on initial parameters.

What is modeling?

• Support policy development & comparison.

• Guide surveillance and data collection activities

• Discover emergent behavior in complex systems

• Identify system variables and relationships critical to system change.

• Enhance decision-making process.

• Target evaluation & monitoring of real world systems

• Provide more effective use of resources ($$$, personnel, equipment, supplies, etc.)

Benefits of modeling

• Equation Based Modeling

– Equations mathematically define conditions and changes in stocks (accumulators) and flows (processes).

– Agents are defined as an aggregate.• Consistency

• Agent Based Modeling

– Algorithms specify conditions and changes in each agent based on values of parameters

– Agents have individual characteristics• Butterfly effect

Types of modeling

Introduction to NetLogo

• Identify goals, topic, framework

• Specify agents and environments

• Determine relevant interactions

• Develop an interface & displays

• Design and create script

• Run and troubleshoot the model

• Validate and verify model

Building a model using NetLogo

• General purpose modeling software– Strong educational components

– Friendly IDE with its own procedural language

• Freely available (not open source)– Supports an API

• Cross-platform for Windows, Mac, Linux– Java based

• Extensive library of models included– Additional models freely available

– Online applets

When you open NetLogo

Here is where the

model is simulated.

We’ll discuss

these functions

as we go along.

Accessing the Models Library

1. Click on File menu

2. Select Models Library

Let’s pick one: AIDS

Setting the parameter values

Here’s where you get to adjust the

input parameters specified by the

programmer.

Starting the simulation

Clicking on the Setup button populates the model

agents parameterized with the initial conditions.

Clicking on the Go button starts the simulation, or

pauses/resumes one that is running.

Running the simulation

Display of infections over time

This simulation was stopped

after 56 agents were

infected. The agents’ colors

reflect infection status (see

legend in graph at left).

Note where an uninfected

agent is in proximity to an

infected one. In subsequent

iterations, the uninfected

agent might convert to HIV+

with some probability based

on infectiousness and

frequency of sexual contact.

• Systems Dynamics (Equation) Model

• Agent Based (Procedural) Model

Examples in NetLogo

• Who are the end users or intended audience?

• What is the purpose of the model and the questions it is

intended to answer?

• Appropriate ABM platform & model development strategy

• Identify relevant data sources and required agent-related

data

• System analysis

• Agents , agent behaviors, & agent interactions

• Backed by theory and data

• Develop model through multiple iterations

• Examine output to assure links between agent behaviors and

system behaviors

• Validate and verify model

Developing an agent-based model

• Model concept development (Conceptualization)

• Model construction (Formulation)

• Model testing (Testing)

• Model dissemination (Implementation)

Overview of the modeling process

http://www.idiagram.com/ideas/models.html

The modeling process

Model Concept Development

• What is the problem / issue / concern?

• How can we identify those HIV/AIDS communication

prevention strategies that are most cost-effective for

African countries?

Model concept development

• What is the purpose / goal of developing the

model?

• Compare communication intervention approaches that

distribute messages about using condoms so we can

understand which factors most influence their successful

adoption, help save lives, and help save African

agencies or governments financial resources.

Model concept development

• What audiences and applications are driving the

development of the model?

• HIV/AIDS interventionists: Compare effectiveness of

communication models relevant to HIV/AIDS prevention

• African HIV/AIDS health program planners: Determine

most effective strategies to reduce the spread of AIDS

Model concept development

• What level of abstraction or aggregation should the model attain?

• Function at a community level (approximately 1000 – 2500 people). Where individuals can interact with other individuals and health workers, travel to clinics or centers, and allow social interaction to play an important role.

• NOT at the level of biochemical processes within the body, nor at the national level where countries interact through policy, trade, and politics.

Model concept development

• What are the system boundaries to this model?Identify essential, aggregated, directional elements What elements are essential to generate the behavior(s) of interest and must be included in the model? What elements are deemed irrelevant and should be excluded from the model?

• Improve HIV/AIDS information levels in individuals, increase mass media messaging, change communication levels among males or females.

• Irrelevant: national leadership changes, individual drug use, individual hygiene practices

Model concept development

Model Construction

• Which agents (and environments) should be

included in (or excluded from) the model?

• Include: individuals, clinics/hospitals, mass media

channels

• Exclude: HIV/AIDS educators, sex workers, individual TV

or radio stations

Model construction

• What are the initial parameters (and their

conditions) in the agents? Are these conditions

distributed along a continuum or identical?

• Individuals have gender (m or f), HIV status (0 or 1),

communication messages from like gender (#),

communication messages from different gender (#),

mass media behavior change status (0 or 1), peer

communication behavior change status (0 or 1)

• Initial values set by input boxes. Default values are …

Model construction

• How do agents interact with other agents?

How do agents interact with the environment?

• How do variables (stocks or flows) influence

other variables (stocks or flows)? Where do

feedback loops occur?

• Individuals meet other individuals and share information.

With enough information, condom behavior changes.

• Individuals interact with mass media channel

(environment), receive information, and change condom

behavior.

Model construction

• How do exposures occur? How do agents

influence others? Are there synergistic

interactions? Does learning occur?

• Agents influence other agents through random mixing.

When two or more agents share the same space,

communication may (probabilistic) occur.

• Learning occurs when people are exposed to messages

and exceed a “mastery” threshold.

( if count > threshold then behavior := new )

Model construction

Model Testing

• Structural Validity: Model aligned to conceptual

parameters (i.e., problem, purpose, goals,

audience, applications, system boundaries)?

• Intervention models only examine communication

strategies, limiting the scope of the modeling goal (e.g.,

compare various intervention strategies)…

Model testing

• Computational Validity: Model is free of

algorithmic, mathematical, and logic errors?

• Individuals don’t save the intervention program any

money UNTIL they have adopted proactive condom

use? After individuals adopt new behavior, they save

the intervention program money indefinitely?

Model testing

• Behavioral Validity: Model is plausible over the range of variables? Parameters are responsive to changes in the system?

• Peer-to-peer communication model follows S-shaped curve typical of random mixing models where individuals reach a “threshold of exposure”

• Changing the communication success parameter influences the timeframe for the population to “absorb” the message.

Model testing

• Empirical Validity: Model provides results that

are comparable to (or compatible with) real-

world events or observations.

• Do we have data that can be used to compare predicted

and actual outcomes?

• (Need to test this when the model is more “complex and

complete.”)

Model testing

Model Dissemination

• What are the essential system structures, critical

variables, and important initial conditions that

are informative?

• Discussion of mass media intervention approaches,

mixing model of participants, exposure to messages ,

and behavioral change system.

• Assumptions of exposure to peers, permanent behavior

change, barriers to communication, gender roles

Model dissemination

• What are the synergistic effects, emergent

behaviors, and insights that illuminate

theory/practice?

• Exponential growth of peers communicating messages

can lead to rapid behavior change over time.

Model dissemination

• What are appropriate applications, policy

implications, and opportunities for careful

extrapolation from the model?

• Comparing relative exposure to different mass media

channels.

• Linking cost analyses to mass media interventions may

help clarify policy decisions for future intervention.

Model dissemination

• Model concept development (Conceptualization)– Problem Purpose/Goals Applications/Audience– Essential Elements & Exclusions: System Boundaries

• Model construction (Formulation)– Agents & Environments– Conditions & Parameters in Agents & Environments– Agent-Agent & Agent-Environment Interactions– Exposure, Influence, & Interactive Behavior– Synergistic Effects & Learning – Initial Conditions

• Model testing (Testing)– Structural validity: Model aligned to conceptual parameters– Computational validity: Eliminate algorithmic, mathematical, & logic errors– Behavioral validity: Parameter sensitivity, plausibility over variable range,

robust– Empirical validity: Results compatible with/comparable to real-world

observations

• Model dissemination (Implementation)– Essential system structures, critical variables, important initial conditions– Synergistic effects, emergent behaviors, insights that illuminate

theory/practice– Appropriate application, policy implications, opportunities for extrapolation

Review of the modeling process

• Costs: – $$ and Proprietary

– Open source or “Free”

• Training – Tutorials

– User base and support

– Developer support and software updates

• Computer programming skills required or preferred? – Java, C, Python, R, etc.?

• Educational role or capability– Interface for user development or group settings

• Power and speed

– Access to HPC, cluster or grid computing

– GPU use capability

Agent-based modeling

considerations

Modeling and Simulation in

Public Health

• MIDAS (Models of Infectious Disease Agent

Study)– https://www.epimodels.org/midas/about.do

• Maxi-Vac 1.0 & Maxi-Vac Alternative– http://www.bt.cdc.gov/agent/smallpox/vaccination/maxi-

vac/index.asp

• Complex Systems Modeling for Obesity

Research– http://www.cdc.gov/pcd/issues/2009/jul/09_0017.htm

• Milstein’s Health Bound simulation for training– http://forio.com/simulate/manager/cdc/health-bound/index.html

Modeling examples in public health

Some demonstrations

An Epidemiology Application

Modeling disease outbreaks

An Intervention Application

Modeling social or behavioral change

An Supply Chain Application

Modeling resource delivery

An Economic Application

Modeling economic evaluations

Wrap-up

• Online tutorials and introductions– Google it!

• Resource sites – SwarmWiki

• (http://www.swarm.org/index.php/Main_Page )

– Agent Based Computational Economics – Leigh Tesfatsion• (http://www.econ.iastate.edu/tesfatsi/ace.htm )

• Journals – JASSS (Journal of Artificial Societies and Social Simulation)

• (http://jasss.soc.surrey.ac.uk/JASSS.html )

• Organizations – NAACSOS

• ( http://www.casos.cs.cmu.edu/naacsos/ )

• Tools…

Resources to help you

• NetLogo (http://ccl.sesp.northwestern.edu/netlogo/ )

• Repast Simphony (http://repast.sourceforge.net/ )

• MASON (http://www.cs.gmu.edu/~eclab/projects/mason/ )

• SWARM (http://www.swarm.org/index.php/Swarm_main_page )

• AnyLogic (http://www.xjtek.com/ )

• Comparison of tools:• http://www.swarm.org/index.php/Tools_for_Agent-Based_Modelling

Agent-based modeling tools

• Albin, S. (1997). Building a system dynamics model. Part 1:

Conceptualization

http://sysdyn.clexchange.org/sdep/Roadmaps/RM8/D-4597.pdf

• Bossel, H. (2007). Systems and models: Complexity, dynamics,

evolution, sustainability. Books on Demand GmbH, Norderstedt,

Germany.

• Doran, J. (200x). Agent design for agent-based modeling.

http://cswww.essex.ac.uk/staff/doran/doran_revisedviennapaper.PD

F

• Luna-Reyes, Luis F. (2003). Model conceptualization: a critical

review.

http://sysdyn.clexchange.org/sdep/Roadmaps/RM8/D-4597.pdf

• Macal, C. & North, M. (2000). Tutorial on agent based modeling and

simulation Part 2: How to model with agents.

http://portal.acm.org/citation.cfm?id=1218130

Some references

Questions??