I'm glad to see people coming together to look at coronavirus data. 

As a non-epidemiologist who is also exploring covid19 data I found this 
article 
<https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8>
 
[1] a timely reminder that our responsibility is heightened with these 
projects, and scales as our work is more widely seen. We have to take care 
that our visualizations and analyses don't misrepresent the situation. This 
line summed it up for me:

>Just because you *can* perform a mathematical function on a set of health 
statistics doesn’t mean you *should*.

As non-subject-matter-experts this is a time to be humble. Of course we 
should still look at the data, but we must be careful. I'm particularly 
skeptical now of any work that assumes that case reports in certain 
countries (e.g. the US) are within even two orders of magnitude of the 
ground truth.

[1] 
https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8


On Tuesday, March 17, 2020 at 9:29:53 PM UTC+1, Daniel Slutsky wrote:
>
> Hello everybody.
>
> We are organizing an online Clojure hackathon for studying COVID-19 data.
>
> Please mark your preferred dates:
> https://twitter.com/scicloj/status/1240010550555353088
>
> Wishing you good health and better times.
>

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