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Rt Covid-19 — See how quickly #COVID19 is growing in each US state
An up-to-date tracker of how fast COVID-19 is spreading in each state from the founders of Instagram.
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Love that the whole thing is a python notebook when Kevin first shared it a week and half ago or so 😃 Might love this main screenshot/graph with Mike and Kevin more hahahaha— I'm guessing that's a not a post-SIP photo @mike_krieger?
I would love to see this data overlaid on an Esri Story map to see correlation between the Rt values and other values like, income, primary political party, etc.
I think this is brilliant and will no doubt help so many people. I'm curious about something: in the email announcing this (approx 1 hour ago) it was stated that Georgia was the highest. And now Georgia is somewhere in the middle. How often do these numbers get refreshed, and how much do they fluctuate in a given hour?
Very interesting and thank you for sharing the Jupyter notebooks, interesting to get a look at the modeling behind it.
Such nice data viz, I hate to feel "excited" due to the subject matter, but this strikes me as well done.
I wish I understood the algo behind it ... can someone explain it a bit for me? Especially how this statement holds true "Absolute testing levels should not affect this algorithm much". Appreciate it.
This is something I've been looking for: a tracker for the "R", or growth rate, of infections. When the R dips to below 1, infections will decrease, and then stop. Also, you can estimate the eventual total number of cases to be about 2X the number of cases at the point the R hits 1. This site is amazing!
if Rt is calculated off the back of the number of reported infection growth since day '0' , then this models accuracy will be affected by the numbers tested. To reach an Rt value in lieu of low testing numbers an assumptions, compensation or extrapolation would need to have entered to model and therefore render it open to inaccuracies. I would not use the outputs form the model to make any serious decisions.
Can't see how any updates or non-updates to the volume of testing will not affect this models accuracy
Wow, this is amazing! I read a bit the blog articles, and math behind in the Jupyter Notebook. Makes me want to learn stats again. Thanks for sharing :)
When doing decision making, choosing the right KPI is key. Too many numbers and you fast don't know what to do. The proposed Rt metric makes sense to me.
Interesting. Curious how you are handling the fact that many that had covid did not get tested and are not reflected in the govt numbers. #jupyter #TODO
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