You're right, this is not at all an accurate epidemic model. My goal with this was to better understand the relationship between the infection rate and the spee

You're right, this is not at all an accurate epidemic model. My goal with this was to better understand the relationship between the infection rate and the speed of the virus spread, as well as how it affects different age cohorts via an age-dependent mortality. It's a simple probabilistic model with discrete time steps, I think it yields similar results to the SIR model but isn't based on a differential equation. Cohort simulation techniques are e.g. used by the census bureau here in Germany to simulate aging of the population. Please note that the transmission rates are not fixed, the model assumes that an infected person makes random connections to other people from the population (so not small world but not far-fetched for a small country like Germany), the more people are already infected or cured from those people, the smaller the effective transmission rate (hence not all people end up infected). You could of course improve this by using a network model or a much more complex social simulation, most of the dynamics will be qualitatively (not quantitatively) very similar though. You can see e.g. that as of now, despite the boggling complexity of the real world the number of infections almost perfectly follows an exponential function. So even a simple model can sometimes give you qualitative insights into a problem. I am sure that the epidemology institutes like the Robert-Koch-Institut have much much more powerful models to predict the spread of the disease, this here is just a toy.