Good case numbers in France, Germany yesterday and the Welsh firebreak definitely worked. (What a surprise - reducing person contacts slow transmission). Northern Ireland controlled well but plateauing high -another push needed
Need to see the impact of the English Nov lockdown - next week sometime one should see lower infection numbers after the 7 day ish incubation period from last Friday plus the test and reporting cycles.
Scotland’s Tiering scheme and particular the central belt work also has done well - though I suspect they want to achieve now more decrease in infection
The other thing to be positive about in England is that the push to more local TTI, higher testing, and Tiering before lockdown looked like it was making an impact (not enough to get infections dropping but definitely something)
Trace in particular works better at lower incidence. We now need to improve isolation support and compliance, make all the gains possible on Trace to allow for a bridge through to widespread vaccination (sometime next year hopefully)
Other countries to note - thankful that Austria has pulled restrictions. Spain continues to be rather opaque to me (numbers so hard to work out). Belgium has had a insanely tough time but I think is improving (right?)
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Vaccine safety thread - briefing to journalists as much as anyone else as ever, and an offer.
Vaccines are safe. They are safe principally because of the extensive and multiple testing that happens before they are licensed, and that ultimately is due to 100,000s of people who volunteer for trials to assess and quantify safety. I am a scientist and one of these volunteers
"Safe" here of course can never mean never - strange things happen in life, healthcare and biology and like many things we do in life - crossing roads, going biking, drinking wine - we have constantly do things which are safe but have some small risk of something going wrong.
Right. Deep Breath. RT-PCR "false positives" and Ct numbers (again). tl;dr it is complex, but the RT-PCR testing systems deployed across the world are sound and the people who run them report positives are positives and little can be improved obviously.
Context: I am a genomics/genetics + computational biology expert. I know a large number of infectious disease testing experts. I have a COI in that I am a long established consultant for a company (ONT) that makes a new test here; this gives me additional insight
There a number of classes of false positives which don't concern the current debate (eg, sample swaps, lab contamination). To repeat an early point all the people I know in this are paranoid about this, test and check in a rather detailed way and these are looooow.
Small moan about models and parameters in COVID. R - the number of infections each person makes on average is a parameter but it is also something that one can measure. Each infected person has their own "R" - it is a count - and one could in theory measure all these little Rs
This R is both something one can measure and the average R, or the distribution of R is often components of model. It is a "real measurable thing" and it is "part of our COVID models".
You might want to model other things; one is the distribution of how R varies between people/events. A sensible choice is a negative binomial. Often people use k as a parameter (in fact, in other uses, they often use the letter r as a parameter, but this would be confusing!)
Again, a briefing for journalists, this time on vaccine trials, types of vaccine, efficacy and safety.
(Context: I am a two-steps expert away from vaccine development; I am a one-step away from clinical trials; I am an expert on genetics + computational data science. I am, finally, on one of these clinical trials as a participant)
Standard context: SARS_CoV_2 is an infectious virus that causes a nasty disease, often leading to death, in a subset of humans. It will continue to be a massive issue to manage until we either have good enough vaccines or good enough treatments for the disease.
It's great to see this paper on scaled up Cactus graphs (Progressive Cactus) - on 600 vertebrate genomes - from the great team lead by @BenedictPatennature.com/articles/s4158…
This paper is a very much a methods paper, but I hope Benedict and colleagues will also dive into the data - I don't think we have use the realised ancestor reconstructions (reminds me of the older Enredo days - also with Benedict!). There is a treasure trove in there
Just repeat evolution I think is fascinating here, but also niche-loss pseudogenes for example.
A riff on data, models and intervention in a COVID world.
When you don't have data, you absolutely need models - it helps you understand what could happen, good and bad scenarios and what you need to measure to understand more thing. With no - or little - data, models are king.
When you have data, the role of models change. In fast moving epidemic even working out what is happening *right now* is complex; your data is coming in different streams, it has biases (which change over time), technical issues (also time dependent) >>