CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC, so there will be a flattening to a lower peak.



NYC's death toll has been growing at a rate that is more than 10 times higher than Washington State (something one can pick up by visual inspection on a logarithmic chart). That's mostly due to the latter having a primarily suburban lifestyle where social distancing is naturally higher. That's what I was saying about most of the US being a slower ground for the epidemic due to the more isolated nature of the suburban lifestyle.

This might also explain the differential between SF and LA. LA has large swaths of areas with minorities living in denser, multi-generational urban/suburban settings. Compare with the typically smaller households in SF, where you have fewer families and fewer children.

https://www.sfchronicle.com/bayarea/article/Exclusive-Captain-of-aircraft-carrier-with-15167883.php?utm_campaign=CMS%20Sharing%20Tools%20(Premium)&utm_source=t.co&utm_medium=referral

calpoly said:

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Cal88 said:

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CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC, so there will be a flattening to a lower peak.



NYC's death toll has been growing at a rate that is more than 10 times higher than Washington State (something one can pick up by visual inspection on a logarithmic chart). That's mostly due to the latter having a primarily suburban lifestyle where social distancing is naturally higher. That's what I was saying about most of the US being a slower ground for the epidemic due to the more isolated nature of the suburban lifestyle.

This might also explain the differential between SF and LA. LA has large swaths of areas with minorities living in denser, multi-generational urban/suburban settings. Compare with the typically smaller households in SF, where you have fewer families and fewer children.

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Since you analyze data so often I would have expected that you noticed that NY state (not NYC) and CA have different slopes and therefore they do not have similar trajectories.

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Actually, the slopes of the curves from both states are quite close in their more recent segments, almost parallel. The NY State curve starts out further up on the Y-axis and its slope flattened a bit in the last few days, drawing pretty close to the CA slope. I just took out a large triangle and ruler to check and confirm on a large monitor what I've deduced above through visual inspection, that's how we did it in engineering back in the day.

Taiwan to donate 10M masks to Europe and US

This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

* The MIT study found the contamination range is 27 feet (not inside 6 feet) and that the stank floats around in the air for hours....it's basically the Terminator.

Coast Guard: Cruise ships must stay at sea with sick onboard


https://apnews.com/564c86d2b78a6e1b1f0be7451848635a

Carnival still has 6,000 passengers on ships at sea - CBS News


https://www.cbsnews.com/news/carinval-cruise-ship-6000-passengers-at-sea-coronavirus/

*The contrast between Governor Newsom's earlier mercy for a cruise ship and his shelter in place order and Florida's Governor DeSantis qualifies DeSantis to be the Bizarro World Gov. Newsom.

Keeping people on these ships did not work on the Diamond Princess. I thought we learned something from that but evidently not..There were no mass infections in Oakland after The Grand Princess docked. Trump should force Ft Lauderdale to get these people off the ships. The longer they saty the more that will be done when they are finally allowed off. What a fucla!

calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

For those who don't understand what is coming down the road, we are in short supply of PPE equipment and testing has not gone as we were told it would. I can tell you that healthcare workers are having problems getting tested. A healthcare worker who was exposed to someone who was being tested for COVID 19 was originally denied testing when they called their primary treating physician who later called back and said they should be tested. They went to get tested, they were swabbed, and 7 days later they were notified that the test was inconclusive. Apparently the person who was completing the swabbing did not swab appropriately and the results were inconclusive but not to worry, the person's primary doctor said since it was after 7 days and the person showed no symptoms they were good to return to work and issued a return to work document.

The first ER doctor died today due to COVID 19 and we haven't even started to see what the virus will do to those providing care and the effect it will have on hospitals and their staffs

Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

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Bearonthebench said:

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Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

---

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The orange and blue lines in Silver's example above aren't even close to being parallel, the angle between them of over 10 degrees, whereas in the FT chart above the segment representing the last 10 days, which is the last 3 data points, line up almost exactly.

calpoly said:

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Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

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"These slopes from NY and CA from the last 3 data points" This is the problem. You CANNOT throw away data because it does not support you hypothesis.

"I think you're trying just a tad too hard to discredit my analytical skills here calpoly. " No, you are really doing a fine job of displaying your ignorance with respect to data analysis without my help.

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You're making the same mistake Nate Silver has made in his attempt to artificially inflate "red state" metrics by focusing on the higher percentage growth rates in states where the epidemic is in its noisier, embryonic stages, which was a highly misleading approach anda poor case made by Silver:

https://bearinsider.com/forums/2/topics/94392/replies/1739740

The early stages of an epidemic tend to be a lot more noisy and less indicative of the general trend. For example, one infected patient zero member of a parish can end up infecting a dozen fellow parishioners, which would yield a 1,200% growth rate for that population, a rate that is not at all representative of future growth.

Silver tried to argue that states like Alaska or WV, which have had raw growth rates of 166% and 219%over a recent 3 day period were worse off than NY or IL, where the growth rate was 98% and 78%, but were the numbers were hundreds of times higher and the epidemic much further ahead.

Down the line in April we will get a more accurate picture of what the growth rate is in WV or Alaska and will be able to make a more valid comparison. The early stage numbers however are a lot less useful and can actually be misleading, as shown in Silver's misguided approach.

https://fivethirtyeight.com/features/the-coronavirus-isnt-just-a-blue-state-problem/

The growth rates eventually stabilize and become less noisy as time progresses and the number of the infected population grows, reflecting the real nature of the epidemic's "terrain" in each state.

That's why looking more closely at more recent data from the last 10 days, which I did above, is not the exercise in futility that you infer, but actually the best snapshot and indicator of future trend, considering the epidemic was still in its very early stages in early March (especially in CA).

calpoly said:

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Cal88 said:

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CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC, so there will be a flattening to a lower peak.



NYC's death toll has been growing at a rate that is more than 10 times higher than Washington State (something one can pick up by visual inspection on a logarithmic chart). That's mostly due to the latter having a primarily suburban lifestyle where social distancing is naturally higher. That's what I was saying about most of the US being a slower ground for the epidemic due to the more isolated nature of the suburban lifestyle.

This might also explain the differential between SF and LA. LA has large swaths of areas with minorities living in denser, multi-generational urban/suburban settings. Compare with the typically smaller households in SF, where you have fewer families and fewer children.

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Since you analyze data so often I would have expected that you noticed that NY state (not NYC) and CA have different slopes and therefore they do not have similar trajectories.

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There's a difference between showing someone else's work and showing your own work. The former is not analysis.

I won't feel good about the CA trajectory until the private labs clear the 60k test backlog. It could easily result in a doubling of our cases. No other state has that sort of testing problem and our rate of positives to negatives is higher than just about every state (other than NY).

I wonder in particular if the death number is accurate given the delay in testing. Would be helpful if when the backlog does clear, they go back and report all of the data as of the date of test, as opposed to the date the results of the test become known.

Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

---

Cal88, not having an engineering background but pretty good at simpler math, I can see what you are saying that the last three days "slopes" of California and NY graphs are similar. But what I also see is that the baseline of the Y axis is from the time an area has 20 deaths from the virus. So my question is that California has had about 125 deaths (roughly 8 days after baseline) and NY has had about 800 deaths at the same time period. So what am I missing here. Then if it is all "trajectory" at given days, who is to say what California's start of quarantining about a week before NY did will mean in the analysis. Is that predictable, or does it remain to be seen? Thanks. I am trying to make out what I am seeing here lacking the level of math you have had.

Unit2Sucks said:

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I won't feel good about the CA trajectory until the private labs clear the 60k test backlog. It could easily result in a doubling of our cases. No other state has that sort of testing problem and our rate of positives to negatives is higher than just about every state (other than NY).

I wonder in particular if the death number is accurate given the delay in testing. Would be helpful if when the backlog does clear, they go back and report all of the data as of the date of test, as opposed to the date the results of the test become known.

---

I'm waiting for a ballpark estimate of how many in the Bay Area have all ready had it. When my wife and I went to the Oregon v Cal game at Haas on Thursday, January 30, we noticed a lot of students wearing masks (on campus, not at the game). I contracted the weirdest cold I have ever had on February 6. My wife got pretty sick a couple of weeks after that.
It would not be helpful to publicize the fact that a large number of Bay Area residents have had it and recovered because it would harm shelter in place resolve.

OdontoBear66 said:

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Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

---

Cal88, not having an engineering background but pretty good at simpler math, I can see what you are saying that the last three days "slopes" of California and NY graphs are similar. But what I also see is that the baseline of the Y axis is from the time an area has 20 deaths from the virus. So my question is that California has had about 125 deaths (roughly 8 days after baseline) and NY has had about 800 deaths at the same time period. So what am I missing here. Then if it is all "trajectory" at given days, who is to say what California's start of quarantining about a week before NY did will mean in the analysis. Is that predictable, or does it remain to be seen? Thanks. I am trying to make out what I am seeing here lacking the level of math you have had.

---

Graphically speaking, we can expect the tracks between the NY and CA curves to have a somewhat similar shape since they're tracking parallel now, but given that CA is further inside, starting the lockdown at a lower/earlier stage, its log curve should arc lower than the NY curve, so I agree with your intuition here. Same shape, but CA tracking lower on the log curve, so the gap in numbers would roughly stay in the same proportions (a lot lower for CA).

Ultimately though, it's hard to accurately project the path differential between the two states because it's going to be a more regional/local phenomenon centered around urban areas, the state figures being a conglomerate of very different terrains (rural, exurb, suburb, inner cities, dense urban corridors) that will grow at different rates and respond differently to the lockdown. The lockdown should work well in parts of CA that are urban but not very dense, and less so in denser corridors like parts of central SF or LA.

Those parts though are a lot smaller than the NYC urban core. Perhaps there is a density threshold above which it is very hard to contain the pandemic, and large parts of NYC exceed that threshold. On paper CA is more urbanized at 95% vs 88% in NYS, but NYC is far denser. You have for instance a lot more residents in NYC who share laundry facilities or laundromats, NYC has 2.5% of the US population and 12% of US laundromats, NYers shop and travel in very crowded environments.

In addition as U2S said above, there is a lot of uncertainty on the data itself due to the spottiness of the testing, which would account for some "jumps" in the curve, the way it happened with the Wuhan official data earlier last month.

going4roses said:

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I'd inject him with the virus.

going4roses said:

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Look for Mango Mussolini to pardon this guy immediately and appoint him to a high position in FEMA tomorrow.

Fauci's security is stepped up as doctor and face of U.S. coronavirus response receives threats - The Hour


http://www.washingtonpost.com/politics/anthony-faucis-security-is-stepped-up-as-doctor-and-face-of-us-coronavirus-response-receives-threats/2020/04/01/ff861a16-744d-11ea-85cb-8670579b863d_story.html

"Fauci has become a public target for some right-wing commentators and bloggers, who exercise influence over parts of the president's base. As they press for the president to ease restrictions to reinvigorate economic activity, some of these figures have assailed Fauci and questioned his expertise.


Last month, an article depicting him as an agent of the "deep state" gained nearly 25,000 interactions on Facebook meaning likes, comments and shares as it was posted to large pro-Trump groups with titles such as "Trump Strong" and "Tampa Bay Trump Club."

Cal88 said:

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OdontoBear66 said:

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Cal88 said:

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calpoly said:

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Cal88 said:

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This is what I've said: "CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC". This statement, which you are nitpicking here, is correct, the slopes of the NY and CA charts having been very close in the last few days.

I am not just "taking just one segment", I am looking at the last few days of a data set that is less noisy because it is a plot with a 7-day moving average. This is clearly a very dynamic situation, so looking at the most recent segment of the graph is more relevant (and less noisy) than focusing on earlier stages.

---

This is NOT a linear scale so when you compare slopes you have to take that into consideration. A difference in slope on a non-linear scale is a BIG deal. You really don't know how to analyze data and I will leave it at that.

---

These slopes from NY and CA from the last 3 data points are virtually parallel, you can check that yourself by hand on a big monitor, as I have.

The last 3 data points actually cover the number of deaths in CA and NY over the last 10 days, with more weight placed on the middle of that period, and with useful smoothing through the 7-day cumulative tally. rendering those last 3 data points quite significant and less "noisy" as a representation of the current trend and a basis for future projections.




I can show you how to do this by hand if you'd like, we learn that in E36, sophomore year; you get a square and a ruler and slide the square parallel to the line segments over the ruler. You will see that the match between the two curves is very tight, confirming my assessment above from visual inspection.

And yeah we do know what a log scale is, and that small variations in slopes on a log scale result in bigger differences than on a regular scale, but any reasonably clear-minded scientist can definitely see that these two slopes have been trending very close together relative to all the other ones on this chart, you'd have to be an idiot (or a pretty bitter theoretical physicist) to keep denying this.

I think you're trying just a tad too hard to discredit my analytical skills here calpoly. Perhaps you could try to contribute some useful information to this thread and topic, instead of wasting your time and mine indulging in your petty ankle-biting.

---

Cal88, not having an engineering background but pretty good at simpler math, I can see what you are saying that the last three days "slopes" of California and NY graphs are similar. But what I also see is that the baseline of the Y axis is from the time an area has 20 deaths from the virus. So my question is that California has had about 125 deaths (roughly 8 days after baseline) and NY has had about 800 deaths at the same time period. So what am I missing here. Then if it is all "trajectory" at given days, who is to say what California's start of quarantining about a week before NY did will mean in the analysis. Is that predictable, or does it remain to be seen? Thanks. I am trying to make out what I am seeing here lacking the level of math you have had.

---

Graphically speaking, we can expect the tracks between the NY and CA curves to have a somewhat similar shape since they're tracking parallel now, but given that CA is further inside, starting the lockdown at a lower/earlier stage, its log curve should arc lower than the NY curve, so I agree with your intuition here. Same shape, but CA tracking lower on the log curve, so the gap in numbers would roughly stay in the same proportions (a lot lower for CA).

Ultimately though, it's hard to accurately project the path differential between the two states because it's going to be a more regional/local phenomenon centered around urban areas, the state figures being a conglomerate of very different terrains (rural, exurb, suburb, inner cities, dense urban corridors) that will grow at different rates and respond differently to the lockdown. The lockdown should work well in parts of CA that are urban but not very dense, and less so in denser corridors like parts of central SF or LA.

Those parts though are a lot smaller than the NYC urban core. Perhaps there is a density threshold above which it is very hard to contain the pandemic, and large parts of NYC exceed that threshold. On paper CA is more urbanized at 95% vs 88% in NYS, but NYC is far denser. You have for instance a lot more residents in NYC who share laundry facilities or laundromats, NYC has 2.5% of the US population and 12% of US laundromats, NYers shop and travel in very crowded environments.

In addition as U2S said above, there is a lot of uncertainty on the data itself due to the spottiness of the testing, which would account for some "jumps" in the curve, the way it happened with the Wuhan official data earlier last month.

---

Thank you. What you suggest, with some exceptions, is more aligned to what I suspect will be the case.
When one speaks of NY most often it is of NYC, rarely NYS, as you suggest. But then California has so much more rural space in its 1100 mile by roughly 200 mile form. There is nothing in California that is like NYC even thought LA is somewhat similar. But really nothing. LA still functions on the automobile as a base, NYC as the Subway. Get into your auto and you have so many fewer touches with society. Leave your apartment in NYC, and watch out. Is it Subway, Uber, taxi, or heaven forbid: walk. But still with walking you wind up touching so many things. In my 55+ 'hood I worry about touching my exit gate and entry gate on the community. Nothing more. There is one BIG difference between the lust for one's auto and the urbanized Metro.

Professor Henry Higgins said:

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calpoly said:

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Cal88 said:

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CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC, so there will be a flattening to a lower peak.



NYC's death toll has been growing at a rate that is more than 10 times higher than Washington State (something one can pick up by visual inspection on a logarithmic chart). That's mostly due to the latter having a primarily suburban lifestyle where social distancing is naturally higher. That's what I was saying about most of the US being a slower ground for the epidemic due to the more isolated nature of the suburban lifestyle.

This might also explain the differential between SF and LA. LA has large swaths of areas with minorities living in denser, multi-generational urban/suburban settings. Compare with the typically smaller households in SF, where you have fewer families and fewer children.

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Since you analyze data so often I would have expected that you noticed that NY state (not NYC) and CA have different slopes and therefore they do not have similar trajectories.

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There's a difference between showing someone else's work and showing your own work. The former is not analysis.

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I guess it became analysis when he took out his ruler.

Governor Newsom, statesman.

Newsom: 'I Would Be Lying' to Say Trump 'Hasn't Been Responsive to Our Needs'


https://www.breitbart.com/clips/2020/04/01/newsom-i-would-be-lying-to-say-trump-hasnt-been-responsive-to-our-needs/

*Yep, I even keep my eyes on the wackos at Breitbart.

Bearonthebench said:

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Professor Henry Higgins said:

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calpoly said:

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Cal88 said:

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CA is actually on a similar trajectory than NYC, but has a later start to the epidemic than NYC, so there will be a flattening to a lower peak.



NYC's death toll has been growing at a rate that is more than 10 times higher than Washington State (something one can pick up by visual inspection on a logarithmic chart). That's mostly due to the latter having a primarily suburban lifestyle where social distancing is naturally higher. That's what I was saying about most of the US being a slower ground for the epidemic due to the more isolated nature of the suburban lifestyle.

This might also explain the differential between SF and LA. LA has large swaths of areas with minorities living in denser, multi-generational urban/suburban settings. Compare with the typically smaller households in SF, where you have fewer families and fewer children.

---

Since you analyze data so often I would have expected that you noticed that NY state (not NYC) and CA have different slopes and therefore they do not have similar trajectories.

---

There's a difference between showing someone else's work and showing your own work. The former is not analysis.

---

I guess it became analysis when he took out his ruler.

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Thanks guys.