COVID-19

Empirical results from serological tests come from 3 studies ("EpiCoV study" [1] and "SpF study" [2], and Carrat et al. [3]) are the most important evidence for testing the model and are presented in Extended Data Figure 6.

This issue still remains, worse now than before.

Recent trends in COVID-19 cases does not look great. Stay vigilant out there

covid, reinfection

All 3 groups showed a reduced viral load after re-infection. It was still present in the high and medium exposure groups 1 day after re-infection, but not detectable in the low-exposure group.

Viral load was not detected after 6 days in all 3 exposure groups. These levels were all much higher in the initial infection

covid, reinfection

The second paper performed a similar experiment. They had 3 groups with different levels of initial viral exposure (group 1 = high infection concentration, group 2 = medium, group 3 = low). Viral load was detected after infection for each group

covid, reinfection

Viral load after initial infection was found in various tissues. However, in the re-infected group, viral load was not detected in any of the same tissues.

This suggests the infection and re-infection were both done properly and that the virus was cleared and no longer present, even after re-infection

covid, reinfection

From the first paper, you can see changes to body weight and temperature after each infection.

All subjects showed increased viral load after initial infection. The group that was re-infected did not show an increased viral load after re-infection.

The group that was re-infected did show a change in immune cell counts after re-infection whereas the monkeys that weren't re-infected showed no changes in the same time

This is a great flowchart on thinking about data-driven stories, and scientific thinking more generally

pudding.cool/process/pivot-con

Another excellent article from The Pudding

So we observe a single value $x$ of a random variable $X$ whose distribution is $\mathcal{X}$.
If we make multiple observations of separate individuals, then we would have observations $x^(i)$ of the random variables $X^(i)$, each of which are distributed as $\mathcal{X}$.

The observed mean would be $\bar{x}$, which is an observation of the random variable $\bar{X}$, whose distribution is $N(\mathbb{E}[X], \mathbb{V}[X])$, by the Central Limit Theorem.

Epstein, Harvard, Nowak

"Epstein was a frequent visitor to the PED offices... But based on the records we reviewed, it appears that Epstein visited PED more than 40 times between 2010
and 2018:"

"Epstein was routinely accompanied on these visits by young women, described as being in their 20s, who acted as his assistants"

Epstein, Harvard, Nowak

"Professor Nowak acknowledged, however, that Epstein played a role in helping Nowak obtain the following unrestricted gifts, which permitted Nowak to continue to maintain office space at One Brattle Square"

Did the same graphic designer do work for both JetBrains and the Francis Crick Institute? Logos are remarkably similar in style

Their final figure sums up their results quite nicely! Please share with your colleagues, if relevant

Some good examples of why making Dockerfiles isn't always straightforward

pythonspeed.com/articles/docke

As someone who uses for managing all my environments, I've noticed since v4.4 how much slower the entire process takes, especially since I need the and -forge channels.

It's nice to see Anaconda address the issue, offer tips for improvement, provide an explanation why there are speed issues, list next steps for improvement, and deal with the open source community

anaconda.com/understanding-and

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