"The new answer to a 77-year-old problem"

😭

https://t.co/hm9NoaU4nr
https://t.co/8fKDiKjSWc
https://t.co/jkaicC1F2x
https://t.co/PpxWT4Jef4
https://t.co/cGQyg9kuOA
https://t.co/2wwPQa8CQ5
https://t.co/7yNW4KgCPi
You've come this far and you're thinking... oh... but plot the difference...

Ok. https://t.co/ifP2RNpLIn
Or this. https://t.co/gwF3u4BVGQ
I will say, box plots / dynamite plots are a terrible way to display data and too many people still use them. But people make a lot of poor choices in their figures. Since you've come this far I'll share this ultimate useless figure... enjoy Figure 1... https://t.co/qI1RUtK0Fn

More from Science

Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).

Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.


You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.

The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.

In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.

The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.

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