There’s still uncertainty about how much protection various COVID vaccines give against certain variants of concern (e.g. B.1.351 identified in SA & P.1 in Brazil). So where will new real-life evidence on vaccine effectiveness against variants come from? A few thoughts...1/
First we need to be clear what type of protection we're talking about (see below: ) – protection against infectiousness will shape transmission dynamics, whereas protection against severe disease will influence outcomes like hospitalisations and deaths. 2/
Much of the evidence to date about different forms of protection against variants has come either from lab studies of immune responses or secondary data from vaccine trials. Both are useful, but also have some limitations... 3/
Neutralising antibodies correlate with reduced infection risk (…) and vaccine efficacy against disease (…) but may not always give full picture about breadth of long-term immune response (e.g.…)... 4/
Meanwhile, vaccine trials typically focus on any symptomatic cases, so secondary analysis of minority variants may have small sample size (…), particularly if looking at rarer outcome like hospitalisation or harder-to-measure outcomes like infection. 5/
If we want to know how well vaccines work against variants of concern in real-life, we really need to look at places where: 1) that vaccine is being widely used, 2) there is a relatively high risk of infection with that variant. So where might these places be? 6/
Israel has relatively high vaccine coverage (mostly Pfizer) and circulation of B.1.351, albeit with case levels quite low now. However, this has still enabled some early insights into patterns of protection post-vaccination: 7/
We'll likely see similar studies from various parts of the US soon, as P.1 and other variants of interest/concern continue to circulate (e.g.…) 8/
In east of France there is considerable B.1.351 circulation (…) as well as a mix of AZ and other vaccines (…), so worth watching emerging data from here. 9/
There have been some small UK outbreaks of B.1.351, which may also provide some early indications - but of course, the better the UK suppresses these variants, the more it will have to rely on signals from elsewhere. 10/

Vaccine studies in places like Brazil are also providing insights into effectiveness of vaccines like CoronaVac in places where variants like P.1 are dominant, e.g.… 11/
Some countries are also using reactive study designs to understand effectiveness, like a geographically targeted campaign following a B.1.351 outbreak in Austria (…), which bears some similarities to Ebola ring vaccination trial (…) 12/
There are also some ongoing vaccine trials that could provide insights into efficacy against variants (e.g.…), although as noted above, will harder to estimate effects against rarer outcomes like severe disease. 13/
The above is a non-exclusive list, but hopefully gives an indication of what to be watching out for. And finally, some more details on the methods being used to evaluate vaccine effectiveness in real-life conditions:… 14/14

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More from @AdamJKucharski

14 Apr
If populations are highly vaccinated, we'd expect a higher proportion of future cases to have been previously vaccinated (because by definition, there aren't as many non-vaccinated people around to be infected). But what sort of numbers should we expect? A short thread... 1/
In above question, there are a lot of things happening conditional on other things happening (e.g. probability cases have been vaccinated), which means we can use Bayes rule (…) to work out the proportion of cases that we'd expect to have been vaccinated. 2/
If we want to know the probability of event A given event B, or P(A|B) for short, we can calculate this as

P(A|B) = P(B|A) P(A)/ P(B)

There are a couple more mathsy tweets coming up, so hold on as then we'll get back to the real-life implications. 3/
Read 9 tweets
6 Apr
The debate around tracking infection/vaccine status for events is reminiscent of last year’s debate around privacy & contact tracing apps. Ultimately, the better countries' ability to track where infection is/isn’t, the lower their COVID risk will be. 1/
If people don’t want to collect/use data in this way, they need to accept the trade off will be a higher COVID risk in the community (or more disruptive measures to prevent that risk). 2/
Many countries have implicitly chosen to introduce stay-at-home orders or live with higher numbers of cases rather than use detailed surveillance (e.g. to identify infections linked to superspreading events or enforce quarantine). 3/
Read 4 tweets
24 Mar
This is an interesting perspective on Taiwan (& glad it mentions data/privacy), although I'd like to see more references to what local officials were actually saying about approach in real-time, rather than what UK-based researchers later say it was:… 1/
E.g. from April 2020: "Covid-19 is becoming flu-like. It means that since it is highly contagious with many mild or asymptomatic cases, and can be transmitted through droplets and contaminated areas, we won’t get rid of this virus totally."… 2/
Taiwan has implemented several innovative, effective measures against COVID-19, but it will harm our ability to plan for the next pandemic if we don't look fully at how countries were interpreting - and acting on - available evidence in real-time. 3/
Read 4 tweets
16 Mar
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/
Read 4 tweets
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
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

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