Disclaimer: I was interested in simulating this out and I’m 100000% sure I’m over generalizing and leaving important things out which can make a huge difference. Im not a medical professional just simply wanted to test a few simulations out.
Last night I thought about how we can simulate the spread of the Ebola virus and remember the neat little tool I used during my civil uprising simulations i temporarily did at USC. NetLogo. so I set out to find something I could use and what do you know? I found a general Virus simulator. Link below. So i thought. “Ok i want to modify this for Ebola test out 3 use-cases” Shown below.
- No control of spread. There are no plans in place to contain disease. No treatment of disease at all.
- Quarantine I. Move the person to a safe location and treat. Those treating, are free to move but in observation. So some movement.
- Quarantine II. Introduce mandatory movement restrictions for ebola infected.
The below image contains a few items. People, infectiousness, chance-recover, duration, quarantine (I added this), visual aid to see the spread, and a graph out lining the spread, healthy people etc.
Using some of the data readily available on the web I set the below variables.
- Chance of Recovery = 30%.
There’s a 70% death rate: http://www.pbs.org/newshour/rundown/70-percent-ebola-death-rate-calculate
- Duration = 21 days or 3 weeks.
- Infectiousness = How easily is someone able to transmit the virus. I would say 90% during the 2 weeks?
- People = Densely populated area. Set to 300 for high density
Changes & Run
Use Case #1
Changed, Chance of Recovery, to 1%. Since without medical treatment it doesn’t look promising for people.
Use Case #2
Movement is not 100% its reduced to 10%. This, though not perfect, will simulate movement of people who are infected but don’t know they are infected or are given the OK to move around as we’re seeing lately.
Use Case #3
Enforced no movement. Simulating the harshest type. Travel bans, movement bans etc.
Use Case #1
Within a year the virus wipes almost all simulated people away. But within a few months healthy people thrive and the virus is at very low levels to the point where its undetectable.
Use Case #2
Within a year there are many infected people. There are more infected people (Red Line) than healthy people (Green Line). Unlike the Use case #1 the population isnt affected as much and quickly bounces back (Blue Line). We do continue to see the virus within the population though.
Use Case #3
Since the simulation did not have a way to restrict movement I added the code in. The logic was simple. If the person is infected do not allow to move..anywhere. The results were pretty good. Within the year the the virus was gone.
Also, I couldn’t simply say that, chance of recover, goes up when we reduce travel and there by simply increase the slider in the GUI. I had to affect the initial logic by restricting the movement explicitly.
A mandatory clamp on movement will work and will beat the virus faster than the other means based on the simulation.
- Wilensky, U. (1998). NetLogo Virus model. http://ccl.northwestern.edu/netlogo/models/Virus. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
- Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.