COVID thoughts from London, after a yesterday's day of slow cricket; TL;DR - the pandemic is reasonably predictable except for human behaviour on contacts; Europe will be likely navigating a complex winter; US (still) needs to vaccinate; The world needs more vaccines.
Context: I am an expert in genetics and computational biology. I know experts in viral genomics, infectious epidemiology, immunology and clinical trials. I have some COIs - I am a longestablished consultant to Oxford Nanopore and was on the Ox/Az vaccine trial.
Reminder: SARS-CoV-2 is a respiratory human virus where a subset of people infected get a horrible disease, COVID, often leading to death. Left unchecked many people would die and healthcare systems would have overflowed.
Having waited for vaccines over 2020, suppressing transmission via a mixture of lowering contacts, mask wearing, well executed track and trace, border controls and harsh contact prevention - lockdowns, (each country a different mix), we now have multiple effective vaccines.
The simple most important thing to do globally is to ensure that ideally everyone, and most importantly everyone at risk of disease (older) or high transmission (healthcare workers) get the vaccines. Simple to say, hard to do.
These vaccines dramatically lower the chance of going into hopsital (about 10-20 fold) and further deaths (perhaps another 2 fold on top). With the most recent variant (Delta), the vaccines somewhat reduce transmission, but frustratingly less than previous variants.
As expected the protection from transmission and somewhat hospitalisation wanes over time; this seems stronger for the Pfzier/BioNTech (but there might be floor of this waning similar to Ox/Az), and argues for booster vaccines for at risk individuals, probably regularly.
Israel provides the strongest evidence so far that boosters work; more controlled randomised trials will come through soon from the UK; it seems likely that booster (hopefully fractional doses are ok to extend the dose/person ratio)
The pandemic is more predictable than the discussion around it make out; basically there are 4 major factors - 1) vaccination/vaccine effectiveness, 2) previous infection 3) human contact patterns/behaviour and 4) infection response behaviour (eg isolation), all age-stratified.
Once one can estimate these factors the progression of the pandemic seems to fit pretty large scale models well. It's been interesting for example how the UK's hospitalisation admissions have been bang in the middle of the UK's modelling group (SPI-M) central probability band.
(note; most models wont necessarily have each of these factors separated - for example human behaviour via contacts and post-infection behaviour often lumped together; I've separated them because these two areas are distinct interventions/policy).
This predictability makes some of the missteps / inability to execute all the harder to witness. The most obvious here is lower vaccine take up, in particular in the at risk age groups, which is the main reason in my view the US has had such an awful Delta wave in the US South.
A contrast here is France, where seeing high level of vaccine hesitancy in the spring of 2021 implemented stronger campaigns and also explicit incentives (Pass Sanitaire) clearly made a difference to French vaccination levels to the benefit of French citizens.
For the at risk age groups this is not at, say, Danish, Irish or British levels, but it is so much better than expected from May in France. It is an example in my book of a good piece of policy targetting one of the most important variables in the system.
I wish we had age-stratified vaccination data in Germany (does anyone know of a crowdsourced or survey based view?) as the public released data I think is only aggregated in massive age buckets.
However, Europe is likely to have a complex autumn as the rather drawn out "exit waves" have to happen - the exit wave being the infection that happens as restrictions are relaxed/removed. These have started/in progress in some places (UK for example) but...
The long summer holidays in Europe means less work contacts, no school contacts and easier outdoor living. Come autumn all these things reverse and it is hard to know precisely where these things land. In effect, one needs to prepare for quite a wide range of infection rates.
The landscape is quite different by country now; for example, the UK has a high level of previous infection (seroprevelance is amazingly high) but a high level of people in hospital now with a stretched health service - perhaps one doubling away from crisis -
In contrast Germany has far lower admissions and hospitalisation levels (and a "deeper" hospital capacity than the UK) but with far less previous infection and possibly more at risk unvaccinated people (see above for not knowing the precise details). Higher risk, lower baseline.
Going across the Atlantic, the heartbreaking accounts from US Hospitals in the South, the madness of people thinking that horse deworming treatment is a COVID prevention when there is a well tested and documented prevention (vaccines) is ... awful to see.
It is good that hospitalisations in the US have plateaued somewhat, but from afar I still worry about the fundamentals of not enough vaccination in the US (and not due to supply). I don't think the Delta wave is over in the US.
Australia and New Zealand also have a complex summer coming up; both need to flip quickly from control via borders to control via vaccines and borders, and it looks like Australia is in the more precarious state (in both axes). I hope vaccination drives can go quicker.
Outside of the developed world the simple problem is large vaccine supply. I am not an expert nor know experts here, so I find it hard to provide insight beyond the obvious that more vaccination is good.
I was recently reminded by the chinese folk saying, made famous by Deng Xiaoping of "crossing the river by touching the stones" - feel your way forward. I feel this autumn and winter will be much like this in Europe.
We are a long way across this river, but not out of it, and the otherside will be different from where we came from. As ever, be friendly and generous to people around you as we cross.
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As we enter yet another period of COVID uncertainity over outcomes (due mainly to human behaviour - what does "baseline/new normal" contacts look like in an European Autumn/Winter) a reminder about models. There are at least 3 different types; explanatory, forecast and scenario.
Explanatory - usually retrospective data to fit an understanding of the world (say infection->hospitalistion/not->death/discharge) for time series. Examples: excess deaths attributable to COVID, vaccine efficacy models and biological properties of variants.
Forecast - fit an up to date time series to understand outcomes in the near future, sometimes just to understand "now" (hence "nowcasting"). Examples: R rate and near time extrapolation; hospitalisation capacity near term management (often not public).
My annual reminder; if you propose doing large scale data gathering and analysis, just a *one sentence* power calculation, or "we have confidence this approach can provide robust results due to similar work of XXX in system YYY with similar sample sizes".
Why is this important in a grant? As a reviewer wont be able to fully check your power calculation (usually) but I do want to see that you are honest with *yourself* about whether the stats are going to work out. Too few samples, expected weak effects => it's never going to work
If your power calculation (which will always be pulling numbers out of the air for effect size etc; such is science) says its very unlikely you will find a credible result then... you need to reset your goals.
COVID thoughts from London as back to school and work looms for England. TL;DR Vaccines work; Delta is our hardest test; the real question is how fast can we vaccinate the planet but many developed countries are running serious, largely avoidable, risks now and coming months
Context: I am genetics and computational biology expert. I know experts in viral genomics, infectious disease epidemiology, clinical trials and immunology. I have some COIs: I am a long standing consultat to Oxford Nanopore and was on the Ox/Az vaccine trial.
Reminder: SARS-CoV-2 is a respiratory virus, distributed by both droplets and floating aerosols, which sometimes causes a delibating disease, COVID, mainly in older and overweight people. If left unchecked, many people would die and many more suffer long term health issues.
Dear journalists / editors covering COVID / this delta wave. Some of you are ... great (genuinely) - its not easy out there crafting a path thru information, speculation+ crankiness. But others... time to up your game. Here are some rookie mistakes in describing what is going on:
1. Please please stop with the % vaccinated in hospital. This is genuinely a meaningless statistic. It is just bonkers wrong to quote it. Trivially if a population is 100% vaccinated then 100% of the people in that population's hospital will be vaccinated.
What you want is something surprisingly tricky to calculate; the counterfactual of how many people should be in hospital if no vaccine. Thankfully there is an easy way of doing this which is referring back to the Alpha wave (wave 2/3 depending on counting system in each country)
Had another moment of "well, yes, but people *are* different" and "you geneticists use continental groups in your analysis" as we skirted around discussions of ethnicity / race in health impacts. TL;DR Partially correct but the underlying mindset that ethnicity=genetics is wrong
Let's deal with the correct things first. Yes, people are different partly (sometimes mainly) due to genetics. Visibly, eg height, weight, hair colour, skin colour, smoking habits + invisibly, eg cholesterol levels, heart trabeculation levels, likelihood of getting breast cancer
Some of these visible differences we integrate into the gestalt assessment of ourselves and others for ethnicity, as represented by self identified ethnicity boxes which people tick, eg "Black British, White English, British Indian, British xxx", gloriously variable by society
Ah. I love the smell of freshly baked data/analysis, well controlled false discovery rate (QQ plot) and just ... so many results. Which of the thousands of beautiful stars in the sky does one pull out to discuss? Biology is so endless and wonderful in its detail...
... to alter (butcher?) a passage from a far far wiser and more thoughtful man than me....
It is interesting to contemplate a tangled set of genetic results, associated to both well known genes and entirely anonymous regions of the genome, stories from physiology of old and hints of new insights, and to reflect ...