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AdamJKucharski's profile
Adam Kucharski
Adam Kucharski
Adam Kucharski
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@AdamJKucharski

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Adam KucharskiVerified account

@AdamJKucharski

Mathematician/epidemiologist at @LSHTM. @WellcomeTrust fellow and @TEDFellow. Author of The Rules of Contagion. Views own.

kucharski.io
Joined January 2012

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    1. Adam Kucharski‏Verified account @AdamJKucharski 3 Jan 2021

      Adam Kucharski Retweeted Adam Kucharski

      A few people have correctly pointed out that theoretical tradeoff below could be different in longer term if no vaccine available. Given vaccine on horizon in UK, I focused on timescale of weeks because that will be a crucial period. But let's explore some broader scenarios... 1/https://twitter.com/AdamJKucharski/status/1343567425107881986 …

      Adam Kucharski added,

      Adam KucharskiVerified account @AdamJKucharski
      Why a SARS-CoV-2 variant that's 50% more transmissible would in general be a much bigger problem than a variant that's 50% more deadly. A short thread... 1/
      Show this thread
      3 replies 62 retweets 154 likes
      Show this thread
    2. Adam Kucharski‏Verified account @AdamJKucharski 3 Jan 2021

      Adam Kucharski Retweeted Adam Kucharski

      Suppose control measures can get R=0.6. We can calculate expected total number of infections = N/(1-R), where N is current infections. So if 10k initial infections, would expect 25k overall, but 100k if virus 50% more transmissible (i.e. R=0.9). 2/https://twitter.com/AdamJKucharski/status/1324662478316408833?s=20 …

      Adam Kucharski added,

      Adam KucharskiVerified account @AdamJKucharski
      If the reproduction number drops below 1, an epidemic won't disappear immediately. But how many additional infections will there be before it declines to very low levels? Fortunately there's a neat piece of maths that can help... 1/
      Show this thread
      2 replies 3 retweets 34 likes
      Show this thread
      Adam Kucharski‏Verified account @AdamJKucharski 3 Jan 2021

      Next, suppose control can get R=0.8. In this scenario, 50% increase in transmission (R=1.2) tips epidemic into exponential growth. So we go from declining outbreak to one that sweeps uncontrolled through population. Hence 50% increase could mean many many fold more infections. 3/

      4:51 AM - 3 Jan 2021
      • 6 Retweets
      • 41 Likes
      • MaximusDerrick Nico Vallone Marty Jérôme L. DaisySirius An Gelic DiogoAprato Luigi Giuseppe Scrosati Dr Kevin Purcell 🇬🇧🇮🇪🇺🇸 😷💉💉💉
      1 reply 6 retweets 41 likes
        1. New conversation
        2. Adam Kucharski‏Verified account @AdamJKucharski 3 Jan 2021

          Finally, suppose R=1.2. If we use a simple SIR model, we can calculate final epidemic size (F) by solving log(1-F) = R x F . So if R=1.2, this would mean 31% infected. If R=1.2x1.5 = 1.8 (i.e. 50% more transmissible), we'd expect 73% infected. 4/

          2 replies 7 retweets 52 likes
          Show this thread
        3. Adam Kucharski‏Verified account @AdamJKucharski 3 Jan 2021

          These are simple illustrative examples, but key point is that small differences in transmissibility are particularly important when we're near the epidemic threshold (i.e. R=1) - which is where many European countries currently are. 5/5

          4 replies 15 retweets 90 likes
          Show this thread
        4. End of conversation

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