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Are there cases where Zipf Law appears in epidemiology?

I ranked provinces of China by their coronavirus confirmed cases (2020-01-30 14:29):

4586, 428, 311, 278, 277, 200, 165, 162, 145, 142, 129, 114, 101, 101, 78, 70, 65, 63 ...

Then I tried fitting the Zipf Law to it. I managed to get a good fit for a segment of it by using $x_n = 1600/n$:

4586, 428, 311, 278, 277, 200, 165, 162, 145, 142, 129, 114, 101, 101, 78, 70, 65, 63 ...

1600, 800, 533, 400, 320, 266, 229, 200, 178, 160, 146, 133, 123, 114, 107, 100, 94, 89 ...

The fit isn't very good, but good enough to be interesting. It makes me think there might be situations in infectious diseases where a Zipf Law appears. And by (Cristelli et al, 2012), I expect this situation to be particularly likely in places with a lot of economic and social integration.

I searched on Google Scholar with phrases like "Zipf infectious disease", but did not get anything interesting.

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  • $\begingroup$ I am now aware of Zipf Law and its applications in biology but I want to ask what exactly are you trying to model? There are some well know modelling approaches for epidemiological data. $\endgroup$
    – WYSIWYG
    Jan 30 '20 at 12:21
  • $\begingroup$ @WYSIWYG I'm just trying to figure out if Zipf's Law applies to this dataset. What it actually models, I'm not sure, but Zipf's Law applies to many phenomena for unknown reasons, so I think it'd be cool to see if it applies to epidemiological data. $\endgroup$ Jan 30 '20 at 14:22

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