Infection Simulation

Alex Tweedly alex at
Wed Dec 29 09:23:09 EST 2021

That's really cool - thanks.

Improvements ?   How much time do you have :-)

1. Visual display of contagion zone while running.

2. Recovery. After being infected for some length of time, an individual 
stops being infectious; either it dies, and becomes a static black dot, 
or recovers and becomes a white dot again. Probably different times for 
each of those to happen.  And obviously a control for the likelihood of 
recovery vs death.

3.  Probabilistic infection. Rather than always infect another which 
comes within reach, make that a probability of infection.

4. Viral load. Make the probability as above follow a bell curve over 
time, as the individual becomes more infected, then recovers.

5. Simulate superspreader events. Maybe introduce short-term "gravity" 
that attracts individuals within a range together - and hence if one is 
already infected they infects lots of others.

And I could go on almost forever :-)
Thinking of features is orders of magnitude easier and quicker than 
implementing them :-)

Thanks again,


On 29/12/2021 03:37, Roger Guay via use-livecode wrote:
> I just uploaded a stack called “Infection" to Sample Stacks which might be a little fun (in a scary way) for some of you. It’s a Monte Carlo simulation wherein a number of individuals randomly moving about in an enclosed space, are infected by a single randomly infected individual. I welcome any feedback that might lead to more accuracy in or improvement to this model.
> Cheers,
> Roger
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