That faint blue wireless icon beside the I Integrator indicates that I'm logging that signal. That enabled me to record the results for multiple different infection rates. I then used the Simulation Data Inspector to visualize daily cases for infection rates ranging from .12 to .40. It makes a big difference in the results for a population of 1 million:
Tom's model inspired me to think about how to develop a more elaborate model. Why do the infection and recovery rates need to be a constant? Converting them to time-varying functions to model quarantine measures would be a variation you could quickly try out. You could also break the populations into demographic categories such as age brackets and/or job type. The infection rate could be customized for each group to account for the different risks of essential workers, retirees, and so forth. What ideas do you have to build an even more sophisticated simulation?
Let us know what you think here or leave a comment for Tom.
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