Why doesn't it make sense? Well, the reproduction rate is the average number of infections that each person who is sick will cause over the LIFETIME OF THEIR DISEASE In other words, over 2 weeks of infection, you might cause another 2.5-3 infections on average
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But we can't see into the future, so we don't actually know how many people the current crop of infections will go on to infect themselves Which brings us to the modelling. Gotta love statspic.twitter.com/4MyseEBip3
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So, it's impossible to get the 'true' R at any point in time, but using a range of serial intervals as well as the number of cases and a statistical model, we can get an idea of what R may have been at previous points in time
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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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