Originally Posted by Bwana:
Once again, don't come in this thread with some kind of political agenda, or you will be shown the door. If you want to go that route, there is a thread about this in DC.
Originally Posted by Dartgod:
People, there is a lot of good information in this thread, let's try to keep the petty bickering to a minimum.
We all have varying opinions about the impact of this, the numbers, etc. We will all never agree with each other. But we can all keep it civil.
Thanks!
Click here for the original OP:
Spoiler!
Apparently the CoronaVirus can survive on a inanimate objects, such as door knobs, for 9 days.
California coronavirus case could be first spread within U.S. community, CDC says
By SOUMYA KARLAMANGLA, JACLYN COSGROVE
FEB. 26, 2020 8:04 PM
The Centers for Disease Control and Prevention is investigating what could be the first case of novel coronavirus in the United States involving a patient in California who neither recently traveled out of the country nor was in contact with someone who did.
“At this time, the patient’s exposure is unknown. It’s possible this could be an instance of community spread of COVID-19, which would be the first time this has happened in the United States,” the CDC said in a statement. “Community spread means spread of an illness for which the source of infection is unknown. It’s also possible, however, that the patient may have been exposed to a returned traveler who was infected.”
The individual is a resident of Solano County and is receiving medical care in Sacramento County, according to the state Department of Public Health.
The CDC said the “case was detected through the U.S. public health system — picked up by astute clinicians.”
Officials at UC Davis Medical Center expanded on what the federal agency might have meant by that in an email sent Wednesday, as reported by the Davis Enterprise newspaper.
The patient arrived at UC Davis Medical Center from another hospital Feb. 19 and “had already been intubated, was on a ventilator, and given droplet protection orders because of an undiagnosed and suspected viral condition,” according to an email sent by UC Davis officials that was obtained by the Davis Enterprise.
The staff at UC Davis requested COVID-19 testing by the CDC, but because the patient didn’t fit the CDC’s existing criteria for the virus, a test wasn’t immediately administered, according to the email. The CDC then ordered the test Sunday, and results were announced Wednesday. Hospital administrators reportedly said in the email that despite these issues, there has been minimal exposure at the hospital because of safety protocols they have in place.
A UC Davis Health spokesperson declined Wednesday evening to share the email with The Times.
Since Feb. 2, more than 8,400 returning travelers from China have entered California, according to the state health department. They have been advised to self-quarantine for 14 days and limit interactions with others as much as possible, officials said.
“This is a new virus, and while we are still learning about it, there is a lot we already know,” Dr. Sonia Angell, director of the California Department of Public Health, said in a statement. “We have been anticipating the potential for such a case in the U.S., and given our close familial, social and business relationships with China, it is not unexpected that the first case in the U.S. would be in California.”
It is not clear how the person became infected, but public health workers could not identify any contacts with people who had traveled to China or other areas where the virus is widespread. That raises concern that the virus is spreading in the United States, creating a challenge for public health officials, experts say.
“It’s the first signal that we could be having silent transmission in the community,” said Lawrence Gostin, director of the World Health Organization Collaborating Center on National and Global Health Law. “It probably means there are many more cases out there, and it probably means this individual has infected others, and now it’s a race to try to find out who that person has infected.”
On Tuesday, the CDC offered its most serious warning to date that the United States should expect and prepare for the coronavirus to become a more widespread health issue.
“Ultimately, we expect we will see coronavirus spread in this country,” said Nancy Messonnier, director of the CDC’s National Center for Immunization and Respiratory Diseases. “It’s not so much a question of if, but a question of when.”
According to the CDC’s latest count Wednesday morning, 59 U.S. residents have tested positive for the new strain of coronavirus — 42 of whom are repatriated citizens from a Diamond Princess cruise. That number has grown by two since Messonnier’s last count Tuesday, although the CDC was not immediately available to offer details on the additional cases.
More than 82,000 cases of coronavirus have been reported globally, and more than 2,700 people have died, with the majority in mainland China, the epicenter of the outbreak.
But public health leaders have repeatedly reminded residents that the health risk from the novel coronavirus to the general public remains low.
“While COVID-19 has a high transmission rate, it has a low mortality rate,” the state Department of Public Health said in a statement Wednesday. “From the international data we have, of those who have tested positive for COVID-19, approximately 80% do not exhibit symptoms that would require hospitalization. There have been no confirmed deaths related to COVID-19 in the United States to date.”
CDC officials have also warned that although the virus is likely to spread in U.S. communities, the flu still poses a greater risk.
Gostin said the news of potential silent transmission does not eliminate the possibility of containing the virus in the U.S. and preventing an outbreak.
“There are few enough cases that we should at least try,” he said. “Most of us are not optimistic that that will be successful, but we’re still in the position to try.”
Originally Posted by Donger:
* Looks over at Bowser, and squints disapprovingly again *
LOL. *looks at Donger, points at my crotch*
I will own that I took that video to mean something more than it was, possibly. Maybe even go as far as falling for some confirmation bias I found in the words of those two doctors. It's clear that more testing needs to be done before we "conclusively" can state numbers on the scale they are suggesting.
However, if their lead detractor Dr. Carl Bergstrom of the University of Washington has anything else to say than "They've used methods that are ludicrous to get results that are completely implausible" I would be all ears. In fact, I think he really should explain how he came to that conclusion, especially in the face of what's being accused of the two doctors.
Also, let the record show that the Orange County Register is the one that took it political by pointing out how the two doctors were going on to Laura Ingraham's show, and how their story were making the rounds on "right wing media". Additionally, the article goes on to allege the two doctors are in this for fame and fortune, but never really shows WHY what they're saying is indeed not factual outside the lack of more wide spread testing.
Not sure if these two are 100% correct or incorrect, but it sure is telling how the media subtly tries to paint them in a certain light. [Reply]
Originally Posted by banyon:
Pretty simple 10x the Flu CFR I showed is .026 x 10 = .26
.26 < 1.47
Or divide the 1.47 by 10 and you get .147
.147 >> .026
Again what denominator did you want me to use?
Where’s your math on the under 65 comparison?
Quit pretending you’ve seriously looked at the numbers in your comparison or show the math or don’t bitch about mine.
You are obviously a lawyer and not a mathematician...
You need to change the denominator, not the comparable.
441,000 x 10 = 4,410,000
6501/4,410,000 = .0014 or .014%
That number doesn't even extrapolate out those with underlying health issues... Which is the majority of those under 65 that die...
Again, you used 38 million as the number who had the flu in fall/winter of 2017/2018 based on the CDC, but Covid-19 with a higher R0 is only 4.4 million right now... Doesn't add up. [Reply]
Originally Posted by Bowser:
LOL. *looks at Donger, points at my crotch*
I will own that I took that video to mean something more than it was, possibly. Maybe even go as far as falling for some confirmation bias I found in the words of those two doctors. It's clear that more testing needs to be done before we "conclusively" can state numbers on the scale they are suggesting.
However, if their lead detractor Dr. Carl Bergstrom of the University of Washington has anything else to say than "They've used methods that are ludicrous to get results that are completely implausible" I would be all ears. In fact, I think he really should explain how he came to that conclusion, especially in the face of what's being accused of the two doctors.
Also, let the record show that the Orange County Register is the one that took it political by pointing out how the two doctors were going on to Laura Ingraham's show, and how their story were making the rounds on "right wing media". Additionally, the article goes on to allege the two doctors are in this for fame and fortune, but never really shows WHY what they're saying is indeed not factual outside the lack of more wide spread testing.
Not sure if these two are 100% correct or incorrect, but it sure is telling how the media subtly tries to paint them in a certain light.
I can tell you why they're wrong:
They implemented selection bias to draw their conclusions. Those most likely to go to a coronavirus clinic are those most likely to test positive for COVID. Without random sampling of the public (which is done for other diseases, mind you), inferences I draw from that information are inherently flawed. They claimed that because 6.6% of their patients were positive for COVID, that 12% of the population in California was positive.
Let's say that I wanted to test for the prevalence of black lung in the country as a whole. Should I test people in the Pacific Northwest, or people in West Virginia? What are the flaws of doing one vs. the other? In reality, I should randomly sample enough of the population for a representative sample, not just one group.
If I test nothing but coal miners, what conclusions might I draw from the severity of black lung that are incorrect?
We also know from hard data in New York that the IFR is far above the lower bounds of their conclusions anyway, because more than 0.1% of the entire population in NYC has already died of COVID-19, and that's with far less than 100% of the city being infected.
Originally Posted by Bowser:
LOL. *looks at Donger, points at my crotch*
I will own that I took that video to mean something more than it was, possibly. Maybe even go as far as falling for some confirmation bias I found in the words of those two doctors. It's clear that more testing needs to be done before we "conclusively" can state numbers on the scale they are suggesting.
However, if their lead detractor Dr. Carl Bergstrom of the University of Washington has anything else to say than "They've used methods that are ludicrous to get results that are completely implausible" I would be all ears. In fact, I think he really should explain how he came to that conclusion, especially in the face of what's being accused of the two doctors.
Also, let the record show that the Orange County Register is the one that took it political by pointing out how the two doctors were going on to Laura Ingraham's show, and how their story were making the rounds on "right wing media". Additionally, the article goes on to allege the two doctors are in this for fame and fortune, but never really shows WHY what they're saying is indeed not factual outside the lack of more wide spread testing.
Not sure if these two are 100% correct or incorrect, but it sure is telling how the media subtly tries to paint them in a certain light.
Originally Posted by DaFace:
Do you have a better one you'd prefer? I use it because it's one of the few kinds of models that most laypeople interact with on a regular basis. If you'd like, I could instead expound on the virtues of economic impact modeling, price sensitivity modeling, conjoint studies, max-diff analysis, market mix modeling, and the like.
Just because the average layperson "interacts" with weather models more often than they do epidemiological ones does not mean you're doing anyone a favor by using for analogous purposes even if they're both based on proven mathematical concepts and both deal with hard sciences.
For one, I'm going to hazard a guess we have a relatively higher level of understanding of human and viral biology along how they influence the other than we do with meteorology. The average layperson might be able to see the intuition of a forecast calling for a severe thunderstorm since they can correlate it with air pressure changes, windspeeds and cloud cover if they were to pay attention to it and correlate it with every time they paid attention to a forecast which also called for a thunderstorm
The average layperson probably has just as much interaction with 'economic' models since supply and demand 'forces' along with a few other basic economic principles are straightforward -and in many cases, even intuitive- but good luck finding someone who doesn't have a trained background in econometrics understanding the intuition of a model that, for example, predicts counter-intuitive examples like where oil futures have a negative value or where an increase in supply X and a static rate of Y demand does not yield an increase in P. Meteorological, epidemiological and economic models all have almost ironclad mathematical proofs underlying them but that doesn't mean the reasons why one field's model winds up being grossly inaccurate and the others aren't.
Further, you keep going back the weather analogy to clown posters critical of the models. Sure. The virus weathermen said a storm was coming and they were right, but likening the Imperial College / earlier iterations of the IMHE models of a viral outbreak which would be analogous to multiple Category 5 hurricanes hitting the Gulf and Atlantic coasts within the same week and then going so far as to forecast that they persist for months after initial landfall without ever dropping in intensity only for one or two severe tropical depressions only to tell everyone affected by the resulting mass migration and economic disruption with haughty smugness "well they were right about a tropical storm hitting somewhere along the North American coast, weren't they?" [Reply]
Originally Posted by tk13:
That's the biggest flaw with the "just make people with health issues quarantine themselves" while the rest of the world goes back to normal. There's going to be a lot of people on the list that need to stay home.
I am pretty sure that # is less than 97% of the US population that has been under some form of lockdown to-date. [Reply]
Originally Posted by 'Hamas' Jenkins:
I can tell you why they're wrong:
They implemented selection bias to draw their conclusions. Those most likely to go to a coronavirus clinic are those most likely to test positive for COVID. Without random sampling of the public (which is done for other diseases, mind you), inferences I draw from that information are inherently flawed. They claimed that because 6.6% of their patients were positive for COVID, that 12% of the population in California was positive.
Let's say that I wanted to test for the prevalence of black lung in the country as a whole. Should I test people in the Pacific Northwest, or people in West Virginia? What are the flaws of doing one vs. the other? In reality, I should randomly sample enough of the population for a representative sample, not just one group.
If I test nothing but coal miners, what conclusions might I draw from the severity of black lung that are incorrect?
We also know from hard data in New York that the IFR is far above the lower bounds of their conclusions anyway, because more than 0.1% of the entire population in NYC has already died of COVID-19, and that's with far less than 100% of the city being infected.
I can tell you: they are 100% incorrect.
I follow, and thanks for the spelling it out like that.
So, following this line of thought, is it necessary to keep the entire country on lockdown or should we allow cities/states to open according to the numbers they've individually collected in regards to their regions? That might be an entirely too basic of a question and feel free to flame me if you think I need it. I'm just trying to find the light at the end of the tunnel, here. [Reply]
Originally Posted by Bowser:
I follow, and thanks for the spelling it out like that.
So, following this line of thought, is it necessary to keep the entire country on lockdown or should we allow cities/states to open according to the numbers they've individually collected in regards to their regions? That might be an entirely too basic of a question and feel free to flame me if you think I need it. I'm just trying to find the light at the end of the tunnel, here.
There are a few issues of uncertainty:
1) The antibody tests that are out there are fraught with issues, which means that assessing levels of immunity in the community is going to be next to impossible
2) The rapid tests still have issues, which can further asymptomatic spread.
I think the general point of view shared by most in public health is this:
If you can demonstrate two straight weeks of declining positive tests as a percentage of overall tests and hospitalizations, then you can point to being past the peak. However, we must also consider the height of the peak. Things are obviously quite a bit worse in NYC and Detroit than Montana.
In areas where hospitalizations have remained low and are declining, you can probably begin a slow re-opening. But if I did the same thing in Detroit right now, I'm only going to make things worse because my underlying baseline of infection is still so high (but not so high for herd immunity to come into play).
That's why there's real concern about places like Georgia and Florida. There isn't sufficient data to point to a decline in hospitalizations or % positive cases, which means that opening up there now may just result in a spike that undid all of the work of the first lockdown. [Reply]
Originally Posted by Discuss Thrower:
For one, I'm going to hazard a guess we have a relatively higher level of understanding of human and viral biology along how they influence the other than we do with meteorology. The average layperson might be able to see the intuition of a forecast
Are you talking about "we" as in human collective knowledge or "we" as in laypeople? I don't think it's accurate at all to say we have a better understanding of biology than we do of meteorology, but maybe I'm misunderstanding your point. If we did, we'd have a much better idea of what kinds of treatments were going to help this kind of thing.
Originally Posted by :
Further, you keep going back the weather analogy to clown posters critical of the models. Sure. The virus weathermen said a storm was coming and they were right, but likening the Imperial College / earlier iterations of the IMHE models of a viral outbreak which would be analogous to multiple Category 5 hurricanes hitting the Gulf and Atlantic coasts within the same week and then going so far as to forecast that they persist for months after initial landfall without ever dropping in intensity only for one or two severe tropical depressions only to tell everyone affected by the resulting mass migration and economic disruption with haughty smugness "well they were right about a tropical storm hitting somewhere along the North American coast, weren't they?"
Again, maybe I'm missing your point here, but I don't think this is an accurate comparison at all. IHME was high in the beginning due to the issues Hamas described above, but even then it was only predicting, what, 150k deaths? Since then, it's been pretty accurate.
This idea that it's been a horrible model and that no one should trust it keeps hanging on, but I'm not sure why. But really, the real issue with all of this is that we have tons of people who don't know shit about either modeling or about how clinical trials work drawing these wild conclusions based on their limited understanding.
A single model is likely to be flawed. A single study is likely to be flawed. We shouldn't get either too down or too excited about any of them. It's only through a thorough process that we can vet the models and studies, replicate the good ones to see if the results hold, and then start to gain confidence that we actually know WTF is going on. [Reply]
Originally Posted by DaFace:
Are you talking about "we" as in human collective knowledge or "we" as in laypeople? I don't think it's accurate at all to say we have a better understanding of biology than we do of meteorology, but maybe I'm misunderstanding your point. If we did, we'd have a much better idea of what kinds of treatments were going to help this kind of thing.
Again, maybe I'm missing your point here, but I don't think this is an accurate comparison at all. IHME was high in the beginning due to the issues Hamas described above, but even then it was only predicting, what, 150k deaths? Since then, it's been pretty accurate.
This idea that it's been a horrible model and that no one should trust it keeps hanging on, but I'm not sure why. But really, the real issue with all of this is that we have tons of people who don't know shit about either modeling or about how clinical trials work drawing these wild conclusions based on their limited understanding.
A single model is likely to be flawed. A single study is likely to be flawed. We shouldn't get either too down or too excited about any of them. It's only through a thorough process that we can vet the models and studies, replicate the good ones to see if the results hold, and then start to gain confidence that we actually know WTF is going on.
Originally Posted by O.city:
So according to that we’ve peaked?
Just to be clear, that's the original March 25th model, so definitely use the website to see the latest. But yes, they predicted we'd peak in mid-April, and the current model still agrees with that.
IHME had some really weird things going on initially at the state level, but it's actually been pretty damn accurate at the national level. [Reply]
Originally Posted by 'Hamas' Jenkins:
I can tell you why they're wrong:
They implemented selection bias to draw their conclusions. Those most likely to go to a coronavirus clinic are those most likely to test positive for COVID. Without random sampling of the public (which is done for other diseases, mind you), inferences I draw from that information are inherently flawed. They claimed that because 6.6% of their patients were positive for COVID, that 12% of the population in California was positive.
Let's say that I wanted to test for the prevalence of black lung in the country as a whole. Should I test people in the Pacific Northwest, or people in West Virginia? What are the flaws of doing one vs. the other? In reality, I should randomly sample enough of the population for a representative sample, not just one group.
If I test nothing but coal miners, what conclusions might I draw from the severity of black lung that are incorrect?
We also know from hard data in New York that the IFR is far above the lower bounds of their conclusions anyway, because more than 0.1% of the entire population in NYC has already died of COVID-19, and that's with far less than 100% of the city being infected.
Originally Posted by DaFace:
Just to be clear, that's the original March 25th model, so definitely use the website to see the latest. But yes, they predicted we'd peak in mid-April, and the current model still agrees with that.
IHME had some really weird things going on initially at the state level, but it's actually been pretty damn accurate at the national level.
As someone who has followed this very closely, you're 100% right. It's unfortunate that they were very, very off on some states. But nationally it has done a very good job.
