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Adam Kucharski Profile picture
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18 May, 4 tweets, 2 min read
In real-time, epidemic data streams are patchy, delayed, biased and often contradictory. That's why scientists use terms like 'realistic possibility', 'medium confidence' etc. Uncertainty is inevitable (although will reduce over time) - and yet decisions still need to be made. 1/
B.1.617.2 has been spreading fast in some areas, and people are working hard to disentangle causes & quantify exactly what it means for wider transmission. Control measures are now both going in (e.g. testing, vaccination) & being relaxed, which making analysis even trickier. 2/
For more info, people like @kallmemeg @arambaut @jburnmurdoch @erikmvolz @TWenseleers have been posting some useful summaries recently: 3/
And some wider sources for information and analysis on variants globally: 4/4

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More from @AdamJKucharski

Adam Kucharski Profile picture
11 May
Have recently been thinking more about the transmissibility of endemic seasonal coronaviruses... antibody positivity increases sharply at a relatively low age, suggesting high transmissibility in susceptibile populations (below from: nature.com/articles/s4146…). 1/
Older groups have built immunity to seasonal CoVs, but above suggests R0 (i.e. R in fully susceptible population) could be quite high for these viruses. Possibly so high that even stringent measures wouldn't be enough to control in susceptible pop? 2/
If so, it would be yet another reminder of the importance of equitable, fast vaccination globally to reduce the future impact of COVID-19. 3/3
Read 4 tweets
Adam Kucharski Profile picture
5 May
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 (theguardian.com/uk-news/2020/d…), but this didn’t stop the rise of B.1.1.7 across the continent (nytimes.com/interactive/20…). 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/
Read 8 tweets
Adam Kucharski Profile picture
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." thelancet.com/journals/lanin… 2/
And "immune memory in three immunological compartments remained measurable in greater than 90% of subjects for more than 5 months after infection" science.sciencemag.org/content/371/65… 3/
Read 8 tweets
Adam Kucharski Profile picture
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
Adam Kucharski Profile picture
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
Adam Kucharski Profile picture
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 (en.wikipedia.org/wiki/Bayes%27_…) 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

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