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California’s Roadmap to Modify the Stay-at-Home Order [pdf]

gov.ca.gov

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Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#51
post #30

This entire plan is based on models that do not align with what we are seeing. The graph on slide two[1] of the official CA govt plan shows hospitalizations With Intervention rising exponentially, exceeding our hospital capacity in early June. The actual new infection data shows that the rate of new infections has been decreasing since at least 4/4 [2], meaning that the graph of active hospitalizations should be a be…

Why should infections follow a bell curve? Shouldn't it be the derivative of a the logistic curve, which should be approximately exponential on both ends. I don't see how a quadratic should show up in the exponent; either mathematically or epidemiologically.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#52
It's worth looking at Denmark, Austria, Czechia and other European countries that have already started loosening restrictions this week. Like California, they locked down fast and flattened the curve. Now, Denmark is opening school and all three (plus Germany and parts of Italy) are opening smaller non-essential shops.

I haven't found great info on how well these countries have implemented contact tracing, but they do have per capita testing rates much higher than the US (and vastly higher than California).

Here's an interesting article that describes some of the specific precautions Denmark is taking as they open up some schools tomorrow: https://www.thelocal.dk/20200413/schools-and-day-care-centre...

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#53
post #30

This entire plan is based on models that do not align with what we are seeing. The graph on slide two[1] of the official CA govt plan shows hospitalizations With Intervention rising exponentially, exceeding our hospital capacity in early June. The actual new infection data shows that the rate of new infections has been decreasing since at least 4/4 [2], meaning that the graph of active hospitalizations should be a be…

Additionally, notice that the orange data points (actual hospitalizations) are significantly under the "With Intervention" plot. So why even include the slide? Clearly the assumptions behind the "With Intervention" plot do not match reality TODAY, let alone in a few weeks.

Not a good sign if people are making actual decisions based on this.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#54

From indicator #1: > How prepared is our state to test everyone who is symptomatic? Another view is that opening the economy should involve testing people who are NOT symptomatic. Quoting from an MIT Technology Review article ( https://www.technologyreview.com/2020/04/08/998785/stop-covi... ): > The key, says Romer, is repeatedly testing everyone without symptoms to identify who is infected. (People with symptoms sho…

> Another view is that opening the economy should involve testing people who are NOT symptomatic.

Testing people with symptoms is a waste of tests. When tests are in short supply assume everyone with symptoms is positive, isolate them and test their contacts who don't have symptoms. As of last week, Boston Medical Center had a 44% positive rate on the tests they were running on symptomatic people. They could find more positive cases per test by testing people without symptoms.

https://www.medrxiv.org/content/10.1101/2020.03.27.20043968v...

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#55
post #42

Earlier quoted context omitted.

> The graph on slide two[1] of the official CA govt plan shows hospitalizations With Intervention rising exponentially It does not. The exponential curve, per the graph legend, is what would be expected without intervention.

The light blue curve, labeled "With Intervention", is an upward-facing concave curve. Even if it were linear, it does not align with the observed data referenced in the link labeled [2].

OK, looking at the new cases curve on your covid-19.direct page. As presented, you can't really see what the green curve is doing. If you remove the black, total curve, the green new case curve is visible but it seems to be going up and down fairly inconclusively. Until 4/1, it looks like exponential growth. After that, it goes down and then up. Sure, the optimistic view is it's plateaued or going downward and I hope that's true but counting on just twelve days of data seems foolish.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#56
post #30

This entire plan is based on models that do not align with what we are seeing. The graph on slide two[1] of the official CA govt plan shows hospitalizations With Intervention rising exponentially, exceeding our hospital capacity in early June. The actual new infection data shows that the rate of new infections has been decreasing since at least 4/4 [2], meaning that the graph of active hospitalizations should be a be…

Additionally, notice that the orange data points (actual hospitalizations) are significantly under the "With Intervention" plot. So why even include the slide? Clearly the assumptions behind the "With Intervention" plot do not match reality TODAY, let alone in a few weeks. Not a good sign if people are making actual decisions based on this.

My former physics professors would have not approved of a graph like this. D+ on the lab at best.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#57
post #47
post #45

Thanks for sharing this. The first slide throws two pretty big hurdles to reopening - testing and contact tracing. While Google and Amazon are working on contact tracing, it's not clear when it'll be widely implemented or when tests will become prevalent. If the stay-at-home order is conditioned on the implementation of those two indicators, then we may be waiting for a while.

Contact tracing requires shoe leather not tech.

Either works. South Korea applied tech pretty effectively.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#58
post #42

Earlier quoted context omitted.

The light blue curve, labeled "With Intervention", is an upward-facing concave curve. Even if it were linear, it does not align with the observed data referenced in the link labeled [2].

OK, looking at the new cases curve on your covid-19.direct page. As presented, you can't really see what the green curve is doing. If you remove the black, total curve, the green new case curve is visible but it seems to be going up and down fairly inconclusively. Until 4/1, it looks like exponential growth. After that, it goes down and then up. Sure, the optimistic view is it's plateaued or going downward and I hope…

"If you remove the black, total curve, the green new case curve is visible but it seems to be going up and down fairly inconclusively."

The best fit line of new cases after 4/4 is a linear function with a definitively negative slope. It is conclusively not a function that contributes to the upward, concave blue line on the state's graph.

Re: California’s Roadmap to Modify the Stay-at-Home Order [pdf]

#59
post #30

This entire plan is based on models that do not align with what we are seeing. The graph on slide two[1] of the official CA govt plan shows hospitalizations With Intervention rising exponentially, exceeding our hospital capacity in early June. The actual new infection data shows that the rate of new infections has been decreasing since at least 4/4 [2], meaning that the graph of active hospitalizations should be a be…

Why should infections follow a bell curve? Shouldn't it be the derivative of a the logistic curve, which should be approximately exponential on both ends. I don't see how a quadratic should show up in the exponent; either mathematically or epidemiologically.

You're pointing out a great ambiguity in the state's graph. Is the hospitalization line a cumulative count of all hospitalizations? Or hospitalizations at any given time? Since the state's hospital capacity is related to hospitalizations at a given time, and not cumulative, I'm assuming the fit of that graph would be a bell curve with a max, followed by a decrease. People either die or leave the hospital.
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