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/
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As an example, suppose current R=1.1, infection fatality risk is 0.8%, generation time is 6 days, and 10k people infected (plausible for many European cities recently). So we'd expect 10000 x 1.1^5 x 0.8% = 129 eventual new fatalities after a month of spread... 2/

2:40 PM · Dec 28, 2020

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What happens if fatality risk increases by 50%? By above, we'd expect 10000 x 1.1^5 x (0.8% x 1.5) = 193 new fatalities. 3/
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Now suppose transmissibility increases by 50%. By above, we'd expect 10000 x (1.1 x 1.5)^5 x 0.8% = 978 eventual new fatalities after a month of spread. 4/
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The above is just an illustrative example, but the key message: an increase in something that grows exponentially (i.e. transmission) can have far more effect than the same proportional increase in something that just scales an outcome (i.e. severity). 5/5
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Replying to @AdamJKucharski
Replying to @BallouxFrancois
Below, a tree of ~200k genomes of the data available on 21/12/2020. Emergences of N501Y are shown in purple ('UK lineage' in red). N501Y arose multiple times in different lineages, with the first observation dating back to April. 2/ Figure credit: Damien Richard and @LucyvanDorp
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Replying to @AdamJKucharski
Sorry for a super naive question.. What does the fifth power next to R stand for? Is it that one month stands for 5 generations of 6 days?
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Replying to @AdamJKucharski
Sorry for a super naive question.. What does the fifth power next to R stand for? Is it that one month stands for 5 generations of 6 days?
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Replying to @AdamJKucharski
You don't need to make assumptions about R and generation time if you use the daily % growth rate, which you can estimate from weekday-adjusted reported cases. It is currently 4% (average over last 20 days). Then adjust this up by 50% to get your comparison.
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Replying to @AdamJKucharski
System overload 💥
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