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**Consequences / Re: COVID-19**

« **on:**April 02, 2020, 05:49:06 AM »

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2

Testing limitations...

Since the positive rate is steadily climbing while total number of tests has benn flat to falling slightly for several days, the bend in the curve in*new cases* is illusory.

Look at deaths until testing capacity is exanded (assumes competence not in evidence by the Trump admin).

Since the positive rate is steadily climbing while total number of tests has benn flat to falling slightly for several days, the bend in the curve in

Look at deaths until testing capacity is exanded (assumes competence not in evidence by the Trump admin).

3

We happy few wrote: "The lag between infection and case resolution is 23 days (death or recovery)"

Source?

What I've seen is: "...for those who eventually died, the time from symptom onset to death ranged from 2 to8 weeks..."

https://ourworldindata.org/coronavirus#how-long-does-covid-19-last

Let's not try to spread mis-information about this horrific disease, when possible. If you have sources supporting your assertions, please cite them.

I'm building on the paper found by KiwiGriff in post #4899 that found mean time from diagnosis to death was 17.8 days, plus 5 days from infection to symptoms allowing diagnosis.

I agree the wide range for 2-8 weeks makes this calculation a bit simplistic, but the average should suffice.

I am less sure about the time from infection to positive test in SKorea under their aggressive tracing regime. I am purely guessing at 3 days.

The good news is that SKorea's numbers have been growing so slowly that different assumptions about these time spans have little effect on the calculated mortality rate using this method.

The bad news is that 2% mortality is the very best case possible, with the most extensive testing, aggressive tracing, very high compliance with quarantine, etc, etc.... The opposite of Trump's efforts, in other words.

WATF

4

Blu,

Wuhanites who wore masks pre-covid to reduce pollution wore then outside, where the pollution is. And not every location in Hubei was polluted every day, so there must have been plenty of times when public mask wearing was limited to those who felt ill.

Inside, around family, friends , neighbors, coworkers, etc, they did not wear masks unless feeling ill.

No one wore masks while eating in public.

After covid, everyone wears masks outside, same as before , but even more so, even if pollution is low. No one eats in public anymore, and no one works at the remaining essential jobs without a mask, gloves and plentiful hand sanitizer.

Does that answer your question?

Wuhanites who wore masks pre-covid to reduce pollution wore then outside, where the pollution is. And not every location in Hubei was polluted every day, so there must have been plenty of times when public mask wearing was limited to those who felt ill.

Inside, around family, friends , neighbors, coworkers, etc, they did not wear masks unless feeling ill.

No one wore masks while eating in public.

After covid, everyone wears masks outside, same as before , but even more so, even if pollution is low. No one eats in public anymore, and no one works at the remaining essential jobs without a mask, gloves and plentiful hand sanitizer.

Does that answer your question?

5

Let's look at some data.

Iceland and South Korea with most comprehensive testing regimes are most useful, but Iceland has too few deaths to analyze with any statistical confidence, and is still growing exponentially so it has many recent unresolved cases (current doubling time is about 9 days).

South Korea numbers today 31-3-2020:

Deaths: 162 (+4 from yesterday)

Total recovered: 5408 (+180)

Tot conf cases: 9786 (+125)

------------------------------

SK numbers from 20 days previous 11-3-2020:

Deaths: 60

Tot conf cases :7755

------------------------------

This gives us several ways to calculate a mortality rate, based on assumptions informed by the studies and data presented in previous posts.

Assumptions:

1. Given SKorea's aggressive and comprehensive testing and tracing, they have caught and recorded most cases as soon as viral loads are high enough to register.

2. The lag between infection and a positive test is 3 days. (Needs confirmation?)

3. The lag between infection and case resolution is 23 days (death or recovery).

4. This gives us 20 days from case confirmed to cases resolved ( thus my choice of data from 20 days prior

--------------------------

Naive mortality rate 20 days ago = 60/7755 = 0.8%

Naive mortality rate today = 162/9786 = 1.66%

Clearly, the earlier rate was biased low by a flood of new cases during the exponential growth phase that had not yet resolved.

Deaths/(deaths + recovered) = 162/(162+5408) = 2.908%

Same rate from yesterday's numbers = 158/(158+5228) = 2.933%

Pretty stable, but does not reflect the fact that recovery takes longer than death on average, so is probably too high.

Mortality rate calculated from today's total deaths/total cases 20 days ago:

162/7755 =** 2.089%**

This is my preferered number, for now. I'll update it in a few days, and maybe apply it to other countries.

Iceland and South Korea with most comprehensive testing regimes are most useful, but Iceland has too few deaths to analyze with any statistical confidence, and is still growing exponentially so it has many recent unresolved cases (current doubling time is about 9 days).

South Korea numbers today 31-3-2020:

Deaths: 162 (+4 from yesterday)

Total recovered: 5408 (+180)

Tot conf cases: 9786 (+125)

------------------------------

SK numbers from 20 days previous 11-3-2020:

Deaths: 60

Tot conf cases :7755

------------------------------

This gives us several ways to calculate a mortality rate, based on assumptions informed by the studies and data presented in previous posts.

Assumptions:

1. Given SKorea's aggressive and comprehensive testing and tracing, they have caught and recorded most cases as soon as viral loads are high enough to register.

2. The lag between infection and a positive test is 3 days. (Needs confirmation?)

3. The lag between infection and case resolution is 23 days (death or recovery).

4. This gives us 20 days from case confirmed to cases resolved ( thus my choice of data from 20 days prior

--------------------------

Naive mortality rate 20 days ago = 60/7755 = 0.8%

Naive mortality rate today = 162/9786 = 1.66%

Clearly, the earlier rate was biased low by a flood of new cases during the exponential growth phase that had not yet resolved.

Deaths/(deaths + recovered) = 162/(162+5408) = 2.908%

Same rate from yesterday's numbers = 158/(158+5228) = 2.933%

Pretty stable, but does not reflect the fact that recovery takes longer than death on average, so is probably too high.

Mortality rate calculated from today's total deaths/total cases 20 days ago:

162/7755 =

This is my preferered number, for now. I'll update it in a few days, and maybe apply it to other countries.

6

Scenario 3:

There's a chance that warmer weather, plus social distancing, canceling large gatherings, etc will knock down the R0 below 1 temporarily.

Then it comes roaring back in the fall from thousands of loci instead of one single market in China.

Worst case, it doesn't get bad until after the election Nov 3, Trump gets re-elected (I think I threw up a little in my mouth).

There's a chance that warmer weather, plus social distancing, canceling large gatherings, etc will knock down the R0 below 1 temporarily.

Then it comes roaring back in the fall from thousands of loci instead of one single market in China.

Worst case, it doesn't get bad until after the election Nov 3, Trump gets re-elected (I think I threw up a little in my mouth).

7

b.c.

All things being equal, the infected transient workers would start transmitting the virus to vulnerable relatives, and death rates would rise in other provinces to equal Hubei's.

But people respond to new information. Sick people modify their behavior as they learn about the severity of this new virus. It is common for sick people in Asia to wear a mask to protect others, now I am sure this behavior is extra desirable.

So maybe the spread in other provinces was halted before the elderly, cancer patients, etc became exposed to the same extent as in Hubei.

..........

Here's a 2nd idea...

In Hubei, before public awareness of the new virus became widespread, sick people went to the hospital for treatment, shedding virus particles to all the other people in the hospital. Even if medical personnel were wearing PPE, I suspect the other patients were not.

Hospitals are full of old, sick people with pre-existing co-morbidities. The death rate for these people is very high - like 20+%

As better isolation practices were implemented, other provinces avoided this problem, benefitting from knowledge gained in Hubei.

All things being equal, the infected transient workers would start transmitting the virus to vulnerable relatives, and death rates would rise in other provinces to equal Hubei's.

But people respond to new information. Sick people modify their behavior as they learn about the severity of this new virus. It is common for sick people in Asia to wear a mask to protect others, now I am sure this behavior is extra desirable.

So maybe the spread in other provinces was halted before the elderly, cancer patients, etc became exposed to the same extent as in Hubei.

..........

Here's a 2nd idea...

In Hubei, before public awareness of the new virus became widespread, sick people went to the hospital for treatment, shedding virus particles to all the other people in the hospital. Even if medical personnel were wearing PPE, I suspect the other patients were not.

Hospitals are full of old, sick people with pre-existing co-morbidities. The death rate for these people is very high - like 20+%

As better isolation practices were implemented, other provinces avoided this problem, benefitting from knowledge gained in Hubei.

8

...

OK, that is a good idea. However, there are some constraints:

...

Tamino's temperature data start in 1950, but the early years are noisy and I'm reluctant to go back much more than 50 years (1967-ish)

...

So let's split the 1967-2018 period in half, and have two non-overlapping, 25-year periods (1967-1992 and 1993-2018).

...

That gives ECS of 2.3 (early) and 2.8 (late). Not too surprising, since the full period was 2.5.

Now for the million-dollar question: is the increase due to just random noise, or is it the non-stationarity that ASLR alluded to?

...

This is back-of-the-napkin stuff, here, people. But that's what napkins are for, right?

Right, my napkins are practically unreadable by now.

Thanks for the new calculation and graph.

Here are my reactions:

A) The period from the late 1930's to the late 1950's is a rather unique, interesting, and potentially valuable time for consideration in this debate about ECS. Total anthro forcing paused and did not increase at all for 20ish years (1937 to 1958 for example).

B) The formal definition of TCS used in modeling climate requires a very fast rate of increase in CO2 forcing - 1% per year for 72 years to achieve a doubling.

Synthesizing these points, while the forcings paused for 20 years we were not at all observing anything related to TCS, but were slowly evolving towards equilibrium (ECS) for few decades. When forcings resumed their increase, the rate of change in CO2 forcing was far less than 1% per year at the beginning, and is still barely above 1/2% per year now.

So the temperature increased more than it would in a strict TCS model at the beginning of your time period - it was able to get slightly closer to equilibrium. For the second half, the forcing increased faster, and therefore closer to the TCS condition.

So the scatter plot graph of CO2 forcing vs temp has a bias towards higher temps at the left side than it would if TCS were calculated at the full rate of CO2 increase. This means your slope shows a bias towards flatter on the left, and steeper on the right.

Prediction:

If we are foolish enough to continue accelerating our CO2 emission, or if natural sinks break down, and thereby we see CO2 forcings increase at a rate close to 1% per year ...

... Then the ECS calculated by your method would be even higher on the next 25 year segment, as the true TCS conditions are felt.

Next calculation discussion:

Tamino's method of removing non-anthro effects is useful for shorter time spans, and a valuable refinement for your calculation, but over much longer time spans the natural forcings are mostly constant.

Therefore I think it would be interesting to repeat your method on longer time spans, and include pre-1950 data to get a longer term view.

The limitation I discussed above still apply... but it still may be useful to see how it has evolved over longer time spans.

9

Indeed, we all make mistakes, and it was my turn to demonstrate the cliche, apparently!

While you've got the spreadsheet fired up, can I suggest another graph?

How about graphing the ECS calculated from your method (ratio of delta forcings) over time, just as you did above to show the changing ratio of RF(CO2)/RF(total anthro)?

And maybe try varying time period from 30 years rolling windows to larger timespans, too.

While you've got the spreadsheet fired up, can I suggest another graph?

How about graphing the ECS calculated from your method (ratio of delta forcings) over time, just as you did above to show the changing ratio of RF(CO2)/RF(total anthro)?

And maybe try varying time period from 30 years rolling windows to larger timespans, too.

10

Ned,

I absolutely applaud your efforts to highlight the relationship between CO2 and temp, instead of time vs temp. The scatter plot is spot on. I have used the same graph many times when putting denier trolls in their place.

That said, your data do not support your calculation. Specifically, the RCP forcing data you linked to show that the ratio between CO2 forcing and total anthro forcing is not now and has not ever been as low as 0.74.

I believe the error comes from failing to account for non-GHG anthro forcings like aerosols, land-use albedo, cloud albedo due to particulate pollution, etc.

From your data source:

Year 1967

Total anthro forcing (col 4) = 0.6985 W/m^2

CO2 forcing (column 8 ) = 0.7989 W/m^2

Ratio = 1.14

Year 2017

Anthro = 2.398

CO2 = 2.061

Ratio = 0.859

A simple average of the ratios at these 2 end points is almost exactly 1.00, not 0.74

Updating your final conclusion with these corrected values gives us an ECS of 3.37

This is a number fully in line with many other estimates, and even more alarming than the already dangerous value of 2.5.

I absolutely applaud your efforts to highlight the relationship between CO2 and temp, instead of time vs temp. The scatter plot is spot on. I have used the same graph many times when putting denier trolls in their place.

That said, your data do not support your calculation. Specifically, the RCP forcing data you linked to show that the ratio between CO2 forcing and total anthro forcing is not now and has not ever been as low as 0.74.

I believe the error comes from failing to account for non-GHG anthro forcings like aerosols, land-use albedo, cloud albedo due to particulate pollution, etc.

From your data source:

Year 1967

Total anthro forcing (col 4) = 0.6985 W/m^2

CO2 forcing (column 8 ) = 0.7989 W/m^2

Ratio = 1.14

Year 2017

Anthro = 2.398

CO2 = 2.061

Ratio = 0.859

A simple average of the ratios at these 2 end points is almost exactly 1.00, not 0.74

Updating your final conclusion with these corrected values gives us an ECS of 3.37

This is a number fully in line with many other estimates, and even more alarming than the already dangerous value of 2.5.

11

...

For instance there are approximately 26m residential homes in the UK. If we were to put 2Mw/h capacity in each home that is 52 Tw/h of capacity.

...

Erm, just one little problem. at $100 per kw/h that's 10 quadrillion and 400 trillion dollars to deploy. Even at $1 per Kw/h that it $100Trillion

Now let's do that figure for the US.....

Just to inject a little reality into the scale of things...

Nuclear decommissioning looking so expensive now??

NeilT,

I have great respect for your contributions here and in other threads to inject some real calculations to help us grasp the scale of efforts required to wean our civilization from fossil fuels (or die trying, as we seem to be aiming for).

I hope to be able to return the favor and point out that your math seems to be off here.

52 trillion watt hours, 52 TWh, at $100/kWh (or $0.10/Wh) = $5.2 trillion, not $10.4 quadrillion.

Somehow, you are off by a factor of 2,000. Not sure how, exactly, but British vs American usage of trillions may be part of it.

Another way to look at it - if every household had a car with a 100 kWh battery pack, plugged into the grid with Jim Hunt's bi-directional charging equipment, that would be a similar scale of investment in storage capacity. We already replace the auto fleet every decade or two, so we should expect BEV to start taking up some of the storage duties at no additional cost as they replace FF ICEs.

12

Terry, you're off by a factor of 1000 again.

8 GWh = 8,000,000 kWh ... not 8,000 kWh

Therefore 8 GWh is enough for one Tesla semi to travel 4 million miles.

8 GWh = 8,000,000 kWh ... not 8,000 kWh

Therefore 8 GWh is enough for one Tesla semi to travel 4 million miles.

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