One subtlety of below issue that’s worth highlighting – targeted travel bans (as opposed to near-total border closures) have played out in much the way we’d have expected pre-COVID, delaying rather than stopping local epidemics. A few thoughts… 1/

In December, many countries reactively banned travel from the UK (…), but this didn’t stop the rise of B.1.1.7 across the continent (…). 2/
As noted by @firefoxx66 at the time, targeted bans can delay introductions, but this will be of limited use if measures aren’t also in place to deal with (undetected) local circulation: 3/
Similarly, Japan enacted strict border measures in response to variants in December 2020 (…) but this hasn’t stopped local outbreaks of such variants (…). 4/
Focusing on a specific country of origin can also distract from growing domestic outbreaks, e.g. in early 2020 when travel to China was part of the COVID case definition in the US:… 5/
Countries can either choose very early strict closures/quarantine, or accept restrictions will only delay the problem, which will then need to be dealt with locally. And clearly this choice is about more than just science in the long run. 6/
There was some analysis of border closures pre-COVID (e.g. below from New Zealand), but many countries have both in planning and in practice instead opted for reduced travel volumes – with the expected results. 7/

Clearly it's important to understand why border closures weren't on the table in many global health discussions pre-COVID, but it's also worth thinking about why they still aren't on table for some places - and, as vaccination grows, how long they should remain for others. 8/8

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More from @AdamJKucharski

28 Apr
How long does immunity to SARS-CoV-2 last (and how long might it last in future)? A few thoughts... 1/
We now have data from several cohort studies showing responses can last over a period of several months at least. E.g. "Based on data currently available, a rapid decline of SARS-CoV-2 IgG seropositivity or neutralising capacity has not been seen."… 2/
And "immune memory in three immunological compartments remained measurable in greater than 90% of subjects for more than 5 months after infection"… 3/
Read 8 tweets
26 Apr
A reminder that to estimate COVID vaccine effectiveness, we need to compare risk in unvaccinated and vaccinated groups in same population. Here are a couple of common mistakes to watch out for... 1/
You can't get an estimate of effectiveness by simply comparing how many people have been vaccinated and how many cases/hospitalisations there have been in this group (because, of course, if there's no local COVID transmission, you'd always estimate a 100% effective vaccine). 2/
Nor can you just look at what proportion of cases have been vaccinated, because effectiveness will also depend on what proportion of the population have been vaccinated. 3/

Read 4 tweets
20 Apr
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/
Read 14 tweets
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

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