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Adam Kucharski Profile picture
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29 Jan, 4 tweets, 2 min read
These “COVID rankings” are being widely shared, but I think it illustrates why it’s unhelpful to try and precisely score countries in this way at a specific point mid-pandemic (first 36 weeks in this case)... interactives.lowyinstitute.org/features/covid… 1/
The study uses cases, deaths and testing data to rank countries. But compare the case curves of Cyprus (ranked 5th), Latvia (9th), Uruguay (12th), Singapore (13th) and Finland (17th). Things have changed a lot since first half of 2020: 2/
Or look at Philippines (79th) and Oman (91st) - if we’re judging on COVID metrics alone (as the ranking does), is it really plausible to say they’ve had worse epidemic than Austria (42nd), Ireland (43rd), Portugal (63rd) and UK (66th)? 3/
It’s crucial to learn from international examples, but comparisons are a complex task in an ongoing pandemic, and these kinds of arbitrary single-point-in-time scores probably obscure more than they reveal. 4/4

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More from @AdamJKucharski

Adam Kucharski Profile picture
24 Jan
Two (currently unclear) factors that will shape COVID dynamics over coming years:
A. Impact of vaccines on reducing transmission (i.e. whether or not vaccine-driven elimination feasible)
B. Global evolutionary risk (i.e. range of possible new variants) 1/
A: If vaccines don't substantially reduce onwards transmission, then even if 100% population vaccinated, could still see outbreaks if other measures lifted (although widespread vaccination would still reduce disease impact from such outbreaks): 2/
B: We've seen new variants can reduce ability of post-infection immune responses to neutralise virus (e.g. below). The frequency & diversity of emergent variants will affect how much of a problem this is, and what it might mean for vaccine updates: 3/
Read 4 tweets
Adam Kucharski Profile picture
19 Jan
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/
Read 4 tweets
Adam Kucharski Profile picture
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
Adam Kucharski Profile picture
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
Adam Kucharski Profile picture
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
Adam Kucharski Profile picture
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

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