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Date: | Fri, 12 Mar 2021 08:01:00 -0700 |
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The first part of the Covid 19 case and death curves for most
countries, areas within countries, as well as regions does appear to be
simple exponential growth, but it consistently has been followed by
reduced rates of growth, eventual plateauing and then drops. Some
countries and regions have had two (possibly even three?) of these
modal events in a year already. Plenty of cumulative data by country
and for some subdivisions of country, and also access to the infection
and death curves here:
https://www.worldometers.info/coronavirus/
These infection and death curves have in and of themselves periods of
increasing acceleration and periods of decreasing acceleration. There
are many families of mathematically defined equations, such as the
logistic equation that can be used to find the best fit parameters to
the data and for statistical tests. For the mathematically inclined:
https://en.wikipedia.org/wiki/Logistic_function
Skeptics have looked at these infection and death curves to see where
the timing of implementation and relaxation of various mandates fall on
the curve, and visually compared them to equivalent places without
mandates. My impression is the results are murky, at least at first
sight. As has been stated earlier, formal analyses should somehow
incorporate the "preexisting" parameters of these curves due to other
factors (are they known?) and the timing of the intervention. Simply
analyzing the rates of growth before and after a change in policy may
be a first step, but it could lead to incorrect conclusions due to
position on the epidemic curve. If a mitigation policy change occurs
at the time the curve is about to or already started the reduction in
acceleration, then the finding of an effect is incorrect. Similarly,
if a relaxation of policy happens right before the acceleration, when
other places with no relaxation have a similar acceleration, the
conclusion is incorrect.
A bee analogy to the discussion would be to try to assess the effects
through time of an anitviral treatment by monitoring only treated and
colonies looking at individual workers testing positive for the virus.
One could infer improvements due to the treatment (before and after),
but if one is not aware of the cycles of varroa infestation, the
outside factor of a vector, one could make the incorrect conclusion.
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