[MrAlexey]

I recently had to deal with this problem when building out Covidly (www.covidly.com)

Initially I tried using WHO and JHU, but quickly found their data to be riddled with discrepancies, occasional bugs, and direct contradictions with official statements from various countries.

I ended up aggregating multiple sources (including WHO/JHU/etc), performing some sanity checks to remove outliers, then doing my best to merge the remaining results.

Happy to share this data publicly if there's interest!

[mattlutze]

"Explosive growth" and "Virus is largely out of control" is dangerous risk communication.

How often are you updating the data? If there's manual deconfliction, do you clearly indicate how old data for a country or state/province is, or how accurate the reporting is that your massaged summary comes from?

If you're meaning to put this out in the world as a source of information please get some feedback first from people that do this sort of thing for a living. Inaccuracy or excitable language can do more harm than good in emergencies.

[MrAlexey]

You make a very good point about risk communication. I already had to make a few updates (e.g. hiding the mortality rate) that were causing unnecessary panic. I'll work on optimizing the existing language as well.

Regarding the data, it's updated and processed automatically every 10 minutes.

I really appreciate the feedback! Let me know if anything else stands out to you.

[mattlutze]

Here, I would recommend that mortality rates are not bad. The goal in risk communication is to instill a level of concern equal to the current threat. It's all about context.

If you show infection, death and recovery rates, you have to provide context and help people understand what a thing means.

1-10 scales can make parsing difficult (3 and 4 have the same description right now, for example). Governments, militaries and emergency aid orgs put a lot of effort into color and coding systems.

Give Peter Sandman a Google, and check out his site here:

https://www.psandman.com/

He's an expert in how to talk about scary, hard-to-visualize things (like a viral pandemic).

Also, how old is the data being drawn from, what algorithm do you use to de-conflict the sources, and how do you disclose this to your audience (other than the general about page)? If a source has different refresh rates for countries that it tracks, how are you reflecting that to your audience?

A note, China is missing from your nifty "First 20 days" graph, which maybe you should just call "First 20 days after 200 cases" or something like this to make it clearer what's being tracked.

[snowpalmer]

I would love to see what you're doing here. I'm pulling it from JHU, it's been pretty consistent, but not as up to date as I would like. But I'm looking into aggregating data from other places such as Wikipedia. It would be great if there was more of a group effort here.

I've seen some efforts such as: https://github.com/covid19-data/covid19-data which is looking to separate out the data aggregation from the dashboard. However, they are scrubbing out the state-based information which I rely on.

[mmaroti]

I am a researcher, and I fail to find detailed data, please help! In all datasets we see cumulative confirmed, recovered and death numbers organized by day. We would need culumative confirmed by onset time (first symptoms), and confirmed by test time (the current value). We would also need recovered and death by onset time and confirmation time. Where are these numbers?

[MrAlexey]

Unfortunately the existing public data sets I've seen lack this information. The level of detail for each confirmed case is largely dependent on which country is reporting the data (plus it's often in an unstructured and inconsistent format). I would love to know if you find any data source with more details.