the math is kinda wrong fwiw @dsymetweets. Plot 1.22**n and 1.21**n. Notice that after another day or two with 1.21, your gain is erased. What you buy is not really fewer infections, but time.
(Also these are sigmoids not exp, so the slower rate does converge to the faster one)
The same effect applies to a three phase model of suppression, see spreadsheet later in the thread. Rates 1.3/1.05/0.9 v 1.29/1.04/0.89 over each of three months.
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(The sigmoidal nature of epidemic curves is not particularly relevant in this case - in the UK only a small proportion of the population are getting infected during first wave. But adjust the model if you'd like?)
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1.3**90 = 17,984,638,289 Which I guarantee is more than the number of people who will get sick in any case ;) This is exactly why you should use a sigmoidal to capture the upper bound.
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