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/
Finally, suppose R=1.2. If we use a simple SIR model, we can calculate final epidemic size (F) by solving log(1-F) = R x F . So if R=1.2, this would mean 31% infected. If R=1.2x1.5 = 1.8 (i.e. 50% more transmissible), we'd expect 73% infected. 4/
These are simple illustrative examples, but key point is that small differences in transmissibility are particularly important when we're near the epidemic threshold (i.e. R=1) - which is where many European countries currently are. 5/5

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More from @AdamJKucharski

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
25 Nov 20
Relaxing UK COVID-19 control measures over the Christmas period will inevitably create more transmission risk. There are four main things that will influence just how risky it will be... 1/
We can think of as epidemic as a series of outbreaks within households, linked by transmission between households. This is particularly relevant over Christmas, given school holidays and some workplace closures. 2/
We can also think of R in terms of within and between household spread. If the average outbreak size in a household is H, and each infected person in household transmits to C other households on average, we can calculate the 'household' reproduction number as H x C. 3/
Read 8 tweets
22 Nov 20
Some people are interpreting the below study as evidence that people who test positive without symptoms won't spread infection, but it's not quite that simple. A short thread on epidemic growth and timing of infections... 1/
If we assume most transmission comes from those who develop symptoms, there are 2 points where these people can test positive without having symptoms - early in their infection (before symptoms appear) & later, once symptoms resolved (curve below from: cmmid.github.io/topics/covid19…) 2/
So if people test positive without symptoms, are they more likely to be early in their infection or later? Well, it depends on the wider epidemic... 3/
Read 8 tweets

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