This paper gives an excellent description of how to do this and includes an Excel/R tool that you can download and apply yourselfhttps://academic.oup.com/aje/article/178/9/1505/89262 …
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So, we know what R is and how to calculate it What does it MEAN? This is a bit complicated, because the reality is that no measure is useful in isolationpic.twitter.com/zcBlyciiDc
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If the R is less than 1, on average people there are fewer new infections than existing ones, which means the epidemic is on the decline I.e. R=0.9, 100 infections today becomes 90 infections tomorrow
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If the R is greater than 1, the epidemic is growing and there are more new cases than existing ones R=1.5, 100 infections today becomes 150 infections tomorrow
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BUT this is very dependent on your initial numbers Yesterday, Australia had R = ~0.9 with only 21 new cases, while the UK had R = ~0.85 with nearly 4,000 new cases
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To an extent, R gives you an idea of the DIRECTION of the epidemic, without telling you much about how a country or area is actually doingpic.twitter.com/0PxWyUKrNr
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It's also RETROSPECTIVE, which means you can only have a decent idea of R a few days or even a week after the fact (this is based on the serial interval) New infections tomorrow means that the R may be higher today than you thought!
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All of this is why it is very hard to see how you could base fast-moving policy decisions on R (it's also a bit of a pain to calculate)
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If you ARE going to use R as a metric on which to base policy, it's probably a good idea to include a bunch of other metrics as well This is a good thing - more data is always useful!
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Replying to @GidMK
More data is by no means always useful! It's only useful if you have a theoretical model for your response. If you don't, and you're just piling on more data, you're lost.
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Fair point, I think I was more aiming for "don't ever use a single metric to guide responses" than "include every datapoint possible"
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Replying to @GidMK
Govts think the latter is the solution to every problem...
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