Not very useful because it doesn't take into consideration the most significant causes (or restrictions) of spread of Ebola or similar diseases, which relates to cultural interpersonal interactions (such as handling and washing the dead), education, and poverty levels.
Take a dozen Ebola infected persons and drop them in New York. The infection will be eliminated in a few transmissions. Take a dozen Ebola infected persons and drop them in the poorest quarter of Monrovia in Liberia, and well... we're watching what happens.
That's a good point and the author kind of anticipates this with an umbrella "this isn't legit" clause at the end. Though I must say just as a demonstration of (albeit simple) predictive disease modeling in Mathematica, this article still stands on its own.
It would be interesting to see how organizations like the CDC characterize and formalize all the variables that this article is forced to ignore for simplicity's sake. Are there maps of health care quality in use? Are there other important factors related to transmission other than infectiousness and population density of a given area? etc
Epidemic simulations, or contagion simulations, turn out to have pretty wide applications. For example, you can adapt a lot of the basic agent-in-a-grid logic many simulators use to model bushfires[1].
Similarly, if you take the S/I/R model and modify the states and infection functions to model the spread of ideas in a social network. Eg instead of an infection function based purely on proximity, you might also adjust for similarity (homophily) between two agents.
If you want to see a much less sophisticated epidemic model, performed on a simple grid, I wrote a crappy one as a student[2].
Except that to make a few diagram at the end required a lot of Wolfram code, although the model was very simplistic.
To me (working with complex models for work) I fear that complexity with Wolfram would shoot up to unmanageable levels once you start doing serious work.
[+] [-] steven777400|11 years ago|reply
Take a dozen Ebola infected persons and drop them in New York. The infection will be eliminated in a few transmissions. Take a dozen Ebola infected persons and drop them in the poorest quarter of Monrovia in Liberia, and well... we're watching what happens.
[+] [-] doomrobo|11 years ago|reply
It would be interesting to see how organizations like the CDC characterize and formalize all the variables that this article is forced to ignore for simplicity's sake. Are there maps of health care quality in use? Are there other important factors related to transmission other than infectiousness and population density of a given area? etc
[+] [-] Anon84|11 years ago|reply
[+] [-] jacques_chester|11 years ago|reply
Similarly, if you take the S/I/R model and modify the states and infection functions to model the spread of ideas in a social network. Eg instead of an infection function based purely on proximity, you might also adjust for similarity (homophily) between two agents.
If you want to see a much less sophisticated epidemic model, performed on a simple grid, I wrote a crappy one as a student[2].
[1] http://www.bushfirecrc.com/projects/a51/computer-simulation-... [2] https://github.com/jchester/ruby-epidemic-model
[+] [-] contingencies|11 years ago|reply
[+] [-] Goopplesoft|11 years ago|reply
[+] [-] knz42|11 years ago|reply
To me (working with complex models for work) I fear that complexity with Wolfram would shoot up to unmanageable levels once you start doing serious work.
[+] [-] allegory|11 years ago|reply
[+] [-] withinthreshold|11 years ago|reply