
Phase-space model to forecast COVID-19 infection peak for U.S. states - HorizonXP
https://covid.arena-ai.com/
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neaden
"We are physicists, computer scientists and mathematicians, not
epidemiologists or virologists. Therefore this should NOT be viewed as
predictions on the crisis, but rather as a technical tool to test various
theories and assumptions in order to make more informed decisions."

This really should be at the top in bold. This is not a prediction, do not
treat it like one, this is just a thought experiment.

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pranade
Thanks for the feedback. 100% agree, we will make that bolder and bigger so
it's super clear

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arthurcolle
It doesn't render properly on mobile btw.

Safari on iOS

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Izkata
Likewise Firefox on Android.

(Page jumps around when I try to scroll, graphs are off the edge of the screen
with no horizontal scrollbar, and sliders don't do anything)

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shawnz
> We looked at each country’s trajectory in “phase space” – where we looked at
> the evolution of the daily case growth rate as a function of the number of
> cases (rather than as a function of time). Each country has followed a very
> similar trajectory in this space, where the disease begins on a theoretical
> SIR curve but then veers off course and decelerates after government
> intervention.

This has also been done here:
[https://aatishb.com/covidtrends/](https://aatishb.com/covidtrends/)

Accompanying minutephysics video:
[https://www.youtube.com/watch?v=54XLXg4fYsc](https://www.youtube.com/watch?v=54XLXg4fYsc)

Previous discussion:
[https://news.ycombinator.com/item?id=22715920](https://news.ycombinator.com/item?id=22715920)

This page provides results by country rather than by US state.

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pranade
Great references -- thanks!

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shawnz
No problem. Thank you for working on this.

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nimish
Interestingly the I_c(t) cumulative cases is all you need to solve the SIR
model. S is an exponential decay of that, R is linearly proportional, and I
ends up being the difference of the population N and (S+R) based on the S+I+R
= N relation. A straightforward integrating factor.

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thorwasdfasdf
Yesterday, Governor Coumo said it almost looks like New York was close to a
peak but that it was still too early to tell because there's not enough data,
yet.

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Causality1
What concerns me is the repeated mentioning of using training data from China,
when we suspect the data they've released is inaccurate.

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X6S1x6Okd1st
Could you give a good source that discusses how the data is thought to be
inaccurate and how it may be adjusted for?

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Causality1
Aside from any and all statistical and analytical arguments made by people
much smarter than me, the two things which cast the most doubt on their
numbers are a massive drop in the number of registered cell and landline
phones, and the fact they actively suppressed their SARS numbers in 2003.

[https://www.ft.com/content/efdec278-6d01-11ea-9bca-
bf503995c...](https://www.ft.com/content/efdec278-6d01-11ea-9bca-bf503995cd6f)

[https://www.nytimes.com/2003/04/21/world/the-sars-
epidemic-e...](https://www.nytimes.com/2003/04/21/world/the-sars-epidemic-
epidemic-china-admits-underreporting-its-sars-cases.html)

[https://www.theepochtimes.com/the-closing-of-21-million-
cell...](https://www.theepochtimes.com/the-closing-of-21-million-cell-phone-
accounts-in-china-may-suggest-a-high-ccp-virus-death-toll_3281291.html)

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simias
We've been seeing these models pop up for more than a month now, do we have
any hindsight on the accuracy of earlier simulations?

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jacquesm
They are as stretchable as elastic so accuracy is not even a goal. It's more
like a live spreadsheet with nice sliders, I don't see any attempt to fidelity
worth taking seriously.

