The graph of coronavirus deaths over time, both globally and in the US, is going to follow the typical virus graph. From Dmitry Orlov at cluborlov.blogspot.com:
Take a Petri dish, fill it with agar, put a drop of bacteria sample in it, close it and put it in a warm place. The bacteria will grow explosively at first but then growth will slow down and eventually stop completely once the bacteria have consumed all the nutrients the agar can provide. Mathematically this process can be characterized quite accurately using the logistic function:
The logistic function also works well for characterizing pandemics, since the underlying process is similar. Rate of growth of bacteria depends on the number of bacteria and slows down as nutrients run out; rate of spread of infection depends on the number of infected individuals and slows down as the number of individuals left to infect declines.
Mathematical models can be arbitrarily complicated and, as an immediate consequence, arbitrarily wrong. It is possible to fit a polynomial to just about any data just by adding enough terms to it, but the predictive value of such an exercise is pretty much nil. The logistic model is simple. It uses just three parameters: midpoint, maximum and growth rate. And it models real, physical phenomena that are ubiquitous in nature: exponential growth and exponential saturation.
Reblogged this on uwerolandgross.