A few people have asked "do new variants mean vaccines won't work"? Important to avoid simple categories of 'works' and 'doesn't work'. Some variants may alter the extent of protection (and some probably won't) and question is whether this change matters (and at what scale)... 1/
A change in the virus won't necessarily mean change in all aspects of protection. For example, it might increase post-infection/post-vaccination probability of infection or extent of infectiousness by some amount, but not the extent of disease. 2/
In such an example, expected individual-level disease outcomes wouldn't change, but at population-level, transmission might persist for longer than a simple SIR dynamic would predict (and hence standard definition of vaccine herd immunity threshold won't necessarily apply). 3/
Data about variants are still emerging, and exact impact on different aspects of protection at different scales currently unclear. But as with all control measures, need to resist assuming that vaccination either goes in a 'solves everything' or 'does nothing' box. 4/4

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More from @AdamJKucharski

10 Jan
There's been a bit of confusion about the shape of some of the ONS modelled infection estimates, and subsequent updates to the curves - even from people who spend a lot of time looking at COVID data. So what might be doing on? A thread... 1/
First, a disclaimer: I don't work on the ONS infection survey, so these are just my independent observations, based on my reading of the methods and grappling with similar datasets in the past (so don't @ me as if it's my model/graphs!) 2/
The ONS infection survey involves random sampling of UK households (more here: ons.gov.uk/peoplepopulati…). This generates an individual-level dataset with characteristics like age & location, as well as test result (e.g. positive/negative) 3/
Read 15 tweets
3 Jan
A few people have correctly pointed out that theoretical tradeoff below could be different in longer term if no vaccine available. Given vaccine on horizon in UK, I focused on timescale of weeks because that will be a crucial period. But let's explore some broader scenarios... 1/
Suppose control measures can get R=0.6. We can calculate expected total number of infections = N/(1-R), where N is current infections. So if 10k initial infections, would expect 25k overall, but 100k if virus 50% more transmissible (i.e. R=0.9). 2/
Next, suppose control can get R=0.8. In this scenario, 50% increase in transmission (R=1.2) tips epidemic into exponential growth. So we go from declining outbreak to one that sweeps uncontrolled through population. Hence 50% increase could mean many many fold more infections. 3/
Read 5 tweets
29 Dec 20
Below report also includes data on secondary attack rate for old UK variants vs new variant VOC 202012/01:
Secondary attack rate measures transmission risk per-contact, so above suggests difference between groups spreading old and new variant isn't down to one group simply having more contacts. This is consistent with data from our recent pre-print (cmmid.github.io/topics/covid19…)
In other words, it seems the new variant VOC 202012/01 has a different ’T’ to the old one.
Read 4 tweets
28 Dec 20
Why a SARS-CoV-2 variant that's 50% more transmissible would in general be a much bigger problem than a variant that's 50% more deadly. A short thread... 1/
As an example, suppose current R=1.1, infection fatality risk is 0.8%, generation time is 6 days, and 10k people infected (plausible for many European cities recently). So we'd expect 10000 x 1.1^5 x 0.8% = 129 eventual new fatalities after a month of spread... 2/
What happens if fatality risk increases by 50%? By above, we'd expect 10000 x 1.1^5 x (0.8% x 1.5) = 193 new fatalities. 3/
Read 5 tweets
4 Dec 20
How different would the global dynamics have been if COVID-19 had instead been a pandemic flu virus with similar fatality rate? A few thoughts... 1/
There are differences between pandemic flu and SARS-CoV-2, of course. In absence of control, serial interval for SARS-CoV-2 (science.sciencemag.org/content/369/65…) is longer than flu (ncbi.nlm.nih.gov/pmc/articles/P…), and evidence SARS-CoV-2 transmission more clustered (sciencedirect.com/science/articl…) 2/
The susceptibility profile may also be different. In flu pandemics, susceptibility is often concentrated in younger groups (pubmed.ncbi.nlm.nih.gov/20096450/) - for COVID-19, severity/susceptibility concentrated in older groups (e.g. nature.com/articles/s4159…). 3/
Read 7 tweets
27 Nov 20
Some locations in Tier 3 had evidence of rising epidemics before November lockdown; others were declining. Same for Tier 1 & 2 – some were rising; some were declining. How come? There are three likely explanations... 1/
First, things like population demography, household structure, and nature of local industry will influence social interactions and hence transmission potential. As a result, baseline R may just be slightly lower in some locations. 2/

Second, high levels of infection will lead to some accumulation of immunity (in short-term, at least). Unlikely it's enough to go back to normal without outbreaks, but could be enough for control measures that would get R near 1 in spring to now get R below 1. (Data from ONS) 3/
Read 7 tweets

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