an agent-based epidemic model brendan greenley period 3
DESCRIPTION
Why Agent-Based? Originally tried System Dynamics Agent-Based Modeling makes more sense –Individual behavior differs and can greatly affect the course of an epidemic outbreak –A user can observe an agent over time –Children can inherit values from two parents –Continuous visual representation of populationTRANSCRIPT
An Agent-Based Epidemic Model
Brendan GreenleyPeriod 3
Why An Epidemic Model?• Epidemics have been
responsible for great losses of like and have acted as a population control (Black Plague, Spanish Influenza)
• Epidemics are still a cause of concern today and in the future (SARS, Avian Flu)
• Analyzing certain characteristics of an epidemic outbreak or response can help shape plans in case of a real outbreak.
Why Agent-Based?• Originally tried System
Dynamics• Agent-Based Modeling
makes more sense– Individual behavior differs and
can greatly affect the course of an epidemic outbreak
– A user can observe an agent over time
– Children can inherit values from two parents
– Continuous visual representation of population
Scope of project• Population/environment bounds dictated
by computer resources• ~10,000 agents maximum• All about maintaining a population balance• Unrealistic assumptions are made
– Mating– Interactions– Movement
Up, up, and away…
Extinction
NetLogo
• Still using NetLogo• Programming language (Northwestern)• Allows for System Dynamics & Agent
Based Modeling• Crossplatform support
– Windows, *Nix, Mac• Depends on Java• Free!
Procedure
• Agent’s To-Do List:– Move in a random direction– Check for potential mate– Check for possible
exposure to disease– Age++
• Starting populations, immunity, and original % infected are set by user
BehaviorSpace
• Allows me to export data to Excel
• Can incrementally increase specified values as the model runs
• Useful for post-run data analysis
Sample Run of Epidemic Model
0
500
1000
1500
2000
2500
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Ticks
Peop
le count turtles
infected
Moving Average (# Alive)
Moving Average (# Infected)
Timeline
• First Quarter– Used System Dynamics Modeling
• Second Quarter– Late Dec: Switched to Agent-Based Modeling– Jan:
• Implemented susceptibility distribution• Implemented more realistic mating/children
characteristics• Learned how to use BehaviorSpace
Timeline (Continued)• February
– Implement quarantine– Have agent’s epidemic state affect behavior– Create children a bit after mating
• March– Possibly allow for drugs/vaccines to counter disease– As time increases, have agents use their past experience with
epidemics to make smarter decisions (increase the amount they limit contact with others when a disease is widespread, etc.)
• April/May/June– Allow myself extra time, as the previously mentioned tasks may take
longer than expected– Use BehaviorSpace to collect data and analyze multiple situations– Work on interpreting the data for my final project
presentation/poster/etc.
Project Evolution
• System Dynamics -> Agent Based
• Short-term -> Long-term• Predetermined equations
-> more complex individual agent decisions
• Graphs highlight changes
Sample Run of Epidemic Model
0
500
1000
1500
2000
2500
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Ticks
Peop
le count turtles
infected
Moving Average (# Alive)
Moving Average (# Infected)