Time for another round of "How's the most transmissible SARS-CoV-2 variant doing in Denmark?"

If this one gets a foothold, its growth in time will become representative of covid19 count growth in general, as was the case for B.1.1.7.

New blog post: Burau and the infinite parking garage.

On a fun open problem in mathematics, easy to explain and implement an algorithm for, deceptively hard to find the solution to.


Fraction of B.1.1.7 in Denmark is /down/ since last week. This is spooky. B.1.525 enough to account for some 4-5% of week 11 cases.

Initial week 10 data for B.1.1.7 situation in Denmark available. Counts doubling roughly every 3 weeks. Reopening in process.

Fresh B.1.1.7 counts. BOTE doubling rate ~2-3 weeks. Signs B.1.1.7 causes 60% more admissions/case, which gets us back to December levels fairly soon. Reopening in process.

More week 9 data for Danish B.1.1.7 counts. Puts us straight into the worst end of the previously reported confidence interval for week 9.

Initial information from week 9. This one could be worse for once!

Updated estimated number of COVID cases caused by B.1.1.7 variant in Denmark. Note: Week 8 data incomplete, actual count will likely rise further.

Estimated number of COVID cases caused by B.1.1.7 variant in Denmark keeps climbing. Meanwhile Danish govt initiates reopening.

Certainly not Texas levels of crazy but electricity spot prices did see some spikes of high highs around here during the cold.

Best part about state.gov/biographies/donald-j is how the time (but not the date) seems to be changing all the time.

Numerical instability in the simulation?

More seriously, what do these folks mean when they say "70% more contagious"? Are we talking about how it affects what the exponential rate would behave like in the wild? That e.g. what would otherwise be a situation with a 0.9 growth rate should now be a 1.53 growth rate instead?

Tænker du på det samme som mig B1? Det tror jeg faktisk, B117! Det er pandemitid!

I was a bit surprised to see that comma.ai explicitly values programming competition participants in recruiting, but seeing geohot feature on adventofcode.com/2020/leaderbo explains a lot.

Back of the envelope: So, according to a more or less randomly picked internet source [0], 1 mink causes about 10 kg of CO2eq emissions over its lifetime. So, culling 15 million of them will save us about 150000 tons CO2eq per year (not counting production of whatever people will wear instead). Compare that with the Danish target of reducing emissions by 20 million tons CO2 per year by 2030. Did we just get about 1% closer to that target?

[0]: cedelft.eu/publicatie/the_envi

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