Here is a simple SIR model — Susceptible- Infected- Removed:
Here, “removed” can be by recovery or death. Obviously parameters are not exact so the next level would explore randomised changes in possible values and time varying models; noting particular clusters that give dramatic outcomes. The S(t) line is an inverted cumulative case curve — if people don’t flee. So C(t) = 1 – S(t) is cumulative cases, a logistic curve. C(t) will at first look exponential until resistance and running out of susceptible population leads to saturation.
A supplementary equation could partition recovery vs death, and we can work back from observed patterns with infection and deaths.
Further analysis could stratify the population as susceptibility varies with factors such as age, preconditions, interaction with others etc. Another issue would be latency between actual infection and onset of observable symptoms. This may couple to the size of injection of infectious agents and we could go on to model the course of the disease.
So, by running the possibilities across a population of simulations and complications, we can build an aggregate picture. We can then extend to models of mutations and long term population dynamics. Think, here, about Behe’s rule about breaking a bodily or cellular mechanism as a way to sharply reduce susceptibility in a race with the mutation rate and spreading of new strains.
(Covid 19 already has two separately “catchable” strains, L and S. Reportedly, each viral replication may inject about six changes, as a rule of thumb. Ponder implications of borders of islands of function, where “break” becomes fatal for the bug.)
We could contrast impact of environmental changes that affect a population all at once. And so forth.
A way to think about what we are going through globally. END