We want to model infectious diseases. These diseases can spread from one member of a population to another; we try to gain insights into how quickly they spread, what proportion of a population they infect, what proportion dies, etc. One of the easiest ways to model them (and the way we’re focusing on here) is with a compartmental model. A compartmental model separates the population into several compartments, for example:
This project is focused on fitting an extended SIR model with time-dependent $R_0$-values and resource-dependent death rates to real Coronavirus data, in order to come as close as possible to the real numbers and make informed predictions about possible future developments. But before we jump right into fitting the data to our model, let’s do something that is often overlooked — let’s have a short look at what our model cannot do.