R0 is a fairly simple idea - on average, the number of people that a person who gets #COVID19 will infect GIVEN NO INTERVENTIONS (100% susceptible)pic.twitter.com/YkR8vZJmgd
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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
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
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
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!
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)
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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