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Adam Kucharski Profile picture
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16 Mar, 4 tweets, 1 min read
Is “we couldn’t have predicted the emergence of B117” a scientifically accurate statement? 1/
I’d argue it depends whether statement is interpreted in general or specific terms. “We couldn’t have predicted the possibility of a phenotypically distinct SARS-CoV-2 variant” is clearly inaccurate (given some adapation would have been involved in its original emergence)... 2/
...but “we couldn’t have predicted a variant emerging when it did in autumn 2020 with B117’s specific characteristics” is entirely reasonable (especially as our knowledge of its characteristics is still developing). 3/
I suspect interpretation will depend on the identity of the speaker and (un)generosity of the listener. From variants to pandemic viruses, knowing that pathogen evolution can happen isn’t the same as being able to predict what characteristics will emerge and when. 4/4

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More from @AdamJKucharski

Adam Kucharski Profile picture
11 Mar
Slogans aside, there are three broad approaches to COVID countries can take from now on:
A. An R<<1 approach
B. An R<1 approach
C. An R>1 approach

Let's break them down... 1/
A. An R<<1 approach means keeping R as low as possible with stringent measures until no local transmission. E.g. this is what Auckland and Melbourne did recently in response to a handful of new cases. 2/
B. An R<1 approach means keeping epidemic declining, although transmission may still continue for a long time as measures are relaxed. As coverage increases, vaccination could also 'buy' some additional reduction in R & allow more reopening under such an approach. 3/
Read 5 tweets
Adam Kucharski Profile picture
1 Mar
What could happen next with novel variants like P.1 in the UK? There are four possible scenarios. A short thread with some thoughts... 1/
Scenario A: R<1 for both dominant B.1.1.7 variant and other variants of concern like P.1. This is likely situation we're currently in, but staying there is conditional on slow relaxation of control & substantial reduction in infectiousness via vaccines. 2/
However, even if R<1 for a novel variant, there could still be a lot of cases to come before outbreak ends, depending on value of R: 3/
Read 9 tweets
Adam Kucharski Profile picture
14 Feb
I sometimes see people making the mistaken assumption that once a group that make up X% of COVID hospitalisations/deaths are vaccinated, it will reduce hospitalisations/deaths by the same %, even if control measures are lifted. There are two main problems with this... 1/
First, there is a trade off between level of infection in the population and risk reduction through vaccination. Disease outcomes (e.g. hospitalisations/deaths) can broken down into the following: new infections x average-risk-per-infection... 2/
If we remove 50% of the hospitalisation risk within a population through vaccination, for example, but have a large increase in level of infection, it could mean no reduction (or even an increase) in overall hospitalisations... 3/
Read 5 tweets
Adam Kucharski Profile picture
11 Feb
If you want to come up with a prior* for the possible long-term dynamics of SARS-CoV-2, there are two recent papers that I think are useful...

(*Prior: en.wikipedia.org/wiki/Prior_pro…) 1/
First, this pre-print by @eguia_rachel & @jbloom_lab showing that the seasonal coronavirus 229E can undergo 'antigenic drift' to partially escape immunity over time. biorxiv.org/content/10.110… 2/
As with influenza, they find a 'ladder-like' phylogenetic tree, suggesting that new variants emerge, become dominant, then are gradually replaced by subsequent new variants. (Influenza A/H3N2 below right from: nature.com/articles/natur…) 3/
Read 7 tweets
Adam Kucharski Profile picture
9 Feb
Suppose we have a SARS-CoV-2 variant that is inherently more transmissible, and another that is more likely to reinfect people who've previously developed immunity. Which will spread more easily? A thread... 1/
We know we can measure transmission using R, but it helps to break R down into four components - duration, opportunities, transmission probability and susceptibility - or 'DOTS' for short. As below describes, R = D x O x T x S. 2/

For example, if have a variant (call it V1) that is inherently better at transmitting during social interactions, it would mean an increase in 'T'. If it was 50% more likely to transmit per contact, we'd replace 'T' with '1.5 x T'... 3/
Read 8 tweets
Adam Kucharski Profile picture
8 Feb
Over the coming months, many countries will move from the left to the right in this plot... (from: science.sciencemag.org/content/303/56…) 1/
Specifically, many will move from high COVID-19 prevalence but little prior immunity (& hence little advantage for variants that can escape this immunity to some extent), to lower prevalence and higher immunity (& hence more advantage for variants that can escape immunity) 2/
As you can see, the highest rate of adaptation (labelled '3' in the plot below) occurs during the intermediate phase, when there is still enough transmission to generate new variants as well as enough immunity to create an advantage for variants than can evade this immunity. 3/
Read 5 tweets

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