I'm using an agent based model to study disease transmission. After the initial outbreak, percentage of population infected decreases to zero. I'm looking for a rigorous way to choose when the % infected is not meaningfully different from 0 and can end the simulation.

Clearly there cannot be a negative rate of infection so the distribution of trial outcomes at a certain point in time cannot follow a normal distribution and the variances of different scenarios are different so I don't believe I can use a t-test.

Is there obvious alternatives, the distribution for outcomes generally looks like a poisson where lamba grows smaller and smaller until it becomes 0. My goal is simply to identify the point at which I can say with 95% confidence that lambda is 0.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.