Epidemic Modeling in NetLogo - PowerPoint PPT Presentation

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Epidemic Modeling in NetLogo

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Focus on data collection and make conclusions from data, look at derivatives of graphs, etc. ... Ticks. Should a tick represent a day? An hour? Post-development ... – PowerPoint PPT presentation

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Title: Epidemic Modeling in NetLogo


1
Epidemic Modeling in NetLogo
  • Brendan Greenley Pd. 3

2
Purpose
  • Create a simple yet realistic model of an
    epidemic
  • Figure out how manipulating variables changes the
    behavior of an epidemic.

3
Goals
  • Identify how changes in variables such as death
    rate, virus duration, and time until quarantine
    affect the behavior/duration of an epidemic
  • Allow for simple tweaking of variable through
    sliders
  • Implement System Dynamics (SD)

4
Program Run
  • Run time possibilities
  • Short term (one epidemic)
  • Long term (reoccurring epidemics)
  • Key End Results
  • Death count
  • Length of outbreak
  • Time with maximum of infections
  • Results shown graphically (real-time)

5
Similar Projects
  • Numerous epidemic models
  • None implement System Dynamics
  • More complex
  • Parametrics
  • Differentials
  • Basic assumptions frequently the same
  • Infections can only spread from sick -gt
    unaffected
  • Those who survive gain immunity (unless virus
    mutates)
  • Those infected but quarantined cannot spread
    virus

6
NetLogo
  • Programming language (Northwestern)
  • More popularly used in multi-agent based
    programming
  • My use System Dynamics
  • Crossplatform support
  • Windows, Nix, Mac
  • Free!

7
Procedure
  • Add stock/flows in smaller groups
  • Check to see if smaller, simpler linkages work
    properly by tracking stock populations in test
    runs
  • Attempt to link smaller linkages into one greater
    system
  • Check with a test run and repeat until project
    has one, huge working system of flows.

8
Time Line
  • First quarter
  • Learn how to use NetLogo
  • Experiment with non-SD procedures
  • Second
  • Successfully create a basic model that
    encompasses unaffected, infected, quarantined,
    and immune stocks
  • Third
  • Add more variables and flows to model
  • Attempt to have epidemics repeat over a longer
    period of time (centuries), with different
    variations and mutations expressed by a change in
    variables
  • Fourth
  • Focus on data collection and make conclusions
    from data, look at derivatives of graphs, etc.

9
Problems
  • Changing rates over time
  • As awareness of disease increases, so should
    quarantine rate
  • Using flows realistically
  • Balancing population shifts with eachother
  • Combining System Dynamics and non-SD components
    can be difficult
  • Ticks
  • Should a tick represent a day? An hour?

10
Post-development Possibilities
  • What if I finish early
  • Try to create same model in NetLogo, but without
    using System Dynamics
  • Extend my epidemic model so it can be used to
    model long term diseases like HIV/AIDS
  • Agents to represent the populations and have them
    shown on a GUI

11
Results
  • Due to natural immunities, killing off an entire
    population in one epidemic is difficult.
  • Viruses that are too deadly are poor diseases,
    they quickly die off.
  • Quarantining is a very effective measure to slow
    the infection rate.
  • My SD model yields smoother curves than my non-SD
    model, (though there are slightly different
    algorithms/values used)

12
System Dynamics Model
13
Non-System Dynamics Model
14
Plan Changes
  • I shifted away from System Dynamics, but came
    back to it
  • SD environment yields less mistakes fewer
    chances for typos/forgetting to update variables
    than non-SD NetLogo
  • I initially was going to do agent based modeling,
    but that is difficult to do with System Dynamics.
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