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?
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/
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:
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/
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/
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/
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/
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/