I'm interested in generating random numbers.
I have a distribution [for simplicity let it be uniform distribution] of some event occurrence, i.e. the event (visits to a doctor) occurs between $1$ to $3$ times per year.
I want to change resolution of events to be on per month basis. We know how to do it for one tracked person.
In case there are more than one person (e.g. I want to simulate a small town of $200,000$ inhabitants visiting my favorite clinic), this imposes a formidable difficulty, as from programming point of view, I need now to track each visitor with a dedicated PRNG.
My interest is to obtain a single PRNG, which in streaming mode, aka the one used for a per year event generation, will produce events in the needed or extremely close distribution.
E.g., for per year generation, I can generate a number of visits in a loop, to asses a total number of visits per year. Each call for PRNG produces a number of visits for the next person.
Is it possible from mathematical point of view to create a single per month PRNG?
After more thoughts, it would be better to model arrivals, as a Poisson process, then due to infinite divisibility property we can model arrivals per month as independent $\frac{1}{12}$th of per year process. And they are all independent (we can assume this).