Many mathematicians have shown that epidemic events are well fitted to a ‘Gaussian’ or ‘Normal’ distribution commonly known as a Bell Curve.
William Farr managed to get a law named after himself
https://en.wikipedia.org/wiki/Farr%27s_laws
Certainly looking at charts of seasonal influenza from the CDC this seems to be the case.
How does a bell shaped curve usually arise?
These shapes commonly arise in nature when a large number of random events are contributing towards an outcome.
For example the height of an individual person depends upon a multitude of random factors but the overall height distribution in a population is always the same shape – bell curve.
Here is a good demonstration using a Galton board to show that many random events can give a predictable outcome. Note that each ball follows a completely random path but the overall outcome is always the same.
So for large numbers, randomness eventually leads to predictability and ‘causality’. The balls will ‘never’ produce any pattern but this and of they produce a different shape then it needs explaining.
Mathematical modelling
Mathematical modelling on the other hand will produce a Gompertz Curve.
This is actually different from a Bell Curve but looks very similar.
Two now famous modelled curves showing how to ‘flatten the curve’.
Notice that the overall shape is always the same regardless of the ‘infection’ rate.
More examples of actual data from the video underneath.
An Oxford mathematician explains mathematical modelling.
Influenza produces a bell curve year after year, centred precisely at the winter solstice. This is such an ‘unlikely’ event that there must be some causality here somehow, See Influenza and Resonance.
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