An Agent-Based Epidemic Model

1
An Agent-Based Epidemic Model

• Brendan GreenleyPeriod 3

2
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.

3
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

4
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

5
Up, up, and away
6
Extinction
7
NetLogo

• Still using NetLogo
• Programming language (Northwestern)
• Allows for System Dynamics Agent Based Modeling
• Crossplatform support
• Windows, Nix, Mac
• Depends on Java
• Free!

8
Procedure

• Agents 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

9
BehaviorSpace

• Allows me to export data to Excel
• Can incrementally increase specified values as
  the model runs
• Useful for post-run data analysis

10
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

11
Timeline (Continued)

• February
• Implement quarantine
• Have agents 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.

12
Project Evolution

• System Dynamics -gt Agent Based
• Short-term -gt Long-term
• Predetermined equations -gt more complex
  individual agent decisions
• Graphs highlight changes