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Co-vid 19: What Is and Isn't Known, Discussion and Debate


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31 minutes ago, Ahab said:

i am continually impressed with your knowledge of and reasoning ability regarding this virus, pogi!  I hope you never get it yourself!

Thanks Ahab!  I am fairly confident that I (and my family) already had it in early April.  I'm taking every precaution not to get it again though as, I have good reason to believe that immunity is short-lived. 

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1 hour ago, pogi said:

 I have good reason to believe that immunity is short-lived. 

While there is much yet to learn, recent reports show the opposite is true.

https://www.aol.com/article/lifestyle/2020/08/11/what-we-know-about-covid-19-antibodies-is-changing-heres-whats-new/24587967/

https://www.nytimes.com/2020/07/22/health/covid-antibodies-herd-immunity.html

Edited by T-Shirt
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15 hours ago, The Nehor said:

What "light at the end of the tunnel"?

For one, of fifty states and DC, 40 of them have a negative 14 day growth rate.

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48 minutes ago, T-Shirt said:

Here’s hoping.  
 

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South Korea’s Centers for Disease Control and Prevention investigated 285 of those cases, and found that several of the second positives came two months after the first, and in one case 82 days later. Nearly half of the people had symptoms at the second test. But the researchers were unable to grow live virus from any of the samples, and the infected people hadn’t spread the virus to others.

My question is why symptoms if can’t cultivate virus (not contagious means no live virus?)

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A study published July 15, for example, looked at three different groups. In one, each of 36 people exposed to the new virus had T cells that recognize a protein that looks similar in all coronaviruses. In another, 23 people infected with the SARS virus in 2003 also had these T cells, as did 37 people in the third group who were never exposed to either pathogen.

“A level of pre-existing immunity against SARS-CoV2 appears to exist in the general population,” said Dr. Antonio Bertoletti, a virologist at Duke NUS Medical School in Singapore.

The immunity may have been stimulated by prior exposure to coronaviruses that cause common colds. These T cells may not thwart infection, but they would blunt the illness and may explain why some people with Covid-19 have mild to no symptoms. “I believe that cellular and antibody immunity will be equally important,” Dr. Bertoletti said.

 

Edited by Calm
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29 minutes ago, T-Shirt said:

For one, of fifty states and DC, 40 of them have a negative 14 day growth rate.

End of the tunnel maybe, but with fall coming and schools doing on campus instead of online, if we are seeing the end of one tunnel, good chance we are going to drive back into another one fairly quickly imo. 

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1 hour ago, T-Shirt said:

These reports suggest the opposite might be true.  That would be the best news anybody could tell me if it ends up being true.  This is all really just based on the hope that Covid will generate a healthy and enduring T-cell response. We simply don't know that yet.  Relying on herd-immunity is really just wishful thinking at this point, and the down side is that it would require around 80% of the population to become infected before it could have any effect (without a vaccine). 

I have seen too many verifiable, documented, repeat infections to believe that my cases are extremely rare outliers.  I acknowledge that my experience is very limited in the big picture, but until evidence suggests otherwise, I can't disbelieve what I have personally seen.   

Edited by pogi
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31 minutes ago, Calm said:

End of the tunnel maybe, but with fall coming and schools doing on campus instead of online, if we are seeing the end of one tunnel, good chance we are going to drive back into another one fairly quickly imo. 

Perhaps, but would you rather the current cases be on the rise?  Is the decline in case growth not something by which to be encouraged?

(Edited to avoid the Scott Lloyd grammar lesson)🙂

Edited by T-Shirt
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31 minutes ago, Calm said:

My question is why symptoms if can’t cultivate virus (not contagious means no live virus?)

I don't know what to make of this:

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South Korea’s Centers for Disease Control and Prevention investigated 285 of those cases, and found that several of the second positives came two months after the first, and in one case 82 days later. Nearly half of the people had symptoms at the second test. But the researchers were unable to grow live virus from any of the samples, and the infected people hadn’t spread the virus to others.

It doesn't make sense that someone could be symptomatic and test positive (presence of virus is identified), and not be able to grow live virus from samples.

I haven't read it yet.  I'll have to look into it further. 

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2 minutes ago, pogi said:

These reports suggest the opposite might be true. 

I agree, I should have included the, "might".  Nevertheless, it is encouraging.  I am an optimist and will never understand the doomsayers and the fear-mongers.  I choose to find the bright side of everything.  Even if cases were on the rise and it was determined that one can be reinfected, I would still look at everyday as one day closer to the end of the pandemic.

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21 minutes ago, pogi said:

I don't know what to make of this:

It doesn't make sense that someone could be symptomatic and test positive (presence of virus is identified), and not be able to grow live virus from samples.

I haven't read it yet.  I'll have to look into it further. 

I am glad to hear I am not as ignorant as that led me to believe. Looking forward to hearing clarification. 

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28 minutes ago, T-Shirt said:

Perhaps, but would you rather the current cases be on the rise?  Is the decline in case growth not something by which to be encouraged?

(Edited to avoid the Scott Lloyd grammar lesson)🙂

When it comes to Covid itself, decline in growth is great. I believe in flattening the curve. I am hoping we avoid roller coasting though. I do that enough with other things in my life. 

Edited by Calm
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38 minutes ago, pogi said:

I don't know what to make of this:

It doesn't make sense that someone could be symptomatic and test positive (presence of virus is identified), and not be able to grow live virus from samples.

I haven't read it yet.  I'll have to look into it further. 

This is a more direct link

https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030

oops, not it is not. Note date then as May 19

This May be the pdf version:

https://is.cdc.go.kr/upload_comm/syview/doc.html?fn=159118745823700.pdf&rs=/upload_comm/docu/0030/

I wonder if they mean there was not a new strand of the virus, it was just the original virus and therefore they assumed not a reinfection?  Still reading, may change mind....added:  they are not recommending quarantine of repositive cases, so it seems to indicate no virus is present in that it is not found in a culture. 

Edited by Calm
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11 minutes ago, T-Shirt said:

I agree, I should have included the, "might".  Nevertheless, it is encouraging.  I am an optimist and will never understand the doomsayers and the fear-mongers.  I choose to find the bright side of everything.  Even if cases were on the rise and it was determined that one can be reinfected, I would still look at everyday as one day closer to the end of the pandemic.

Would you classify me as a doomsayer and a fear-monger?  I personally view myself as a cautious optimist who puts evidence before wishful thinking and misinformation. 

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3 hours ago, pogi said:

Your math lost me

Perhaps I can clarify. A test that is 100% accurate  would mean there would NEVER be false positives or false negatives. Because of manufacturing errors or human errors and maybe bad luck , there are a small %age of mistakes. A test that is 97% accurate means that 97% of the time what the test shows is what is true. So we pick a person at random . The law of large numbers says that 15% ( for the sake of argument) of the general population will have covid. So the random person has a 15% chance of having covid regardless. Now we test them and the test is 97% accurate. OK we follow the math as I showed.  

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1 hour ago, T-Shirt said:

I agree, I should have included the, "might".  Nevertheless, it is encouraging.  I am an optimist and will never understand the doomsayers and the fear-mongers.  I choose to find the bright side of everything.  Even if cases were on the rise and it was determined that one can be reinfected, I would still look at everyday as one day closer to the end of the pandemic.

Yeah plus even if someone dies they are likely going to a better place than among us who haven't died yet and who wouldn't rather be dead than here? Either way we're all at least one day closer to the end of the pandemic

image.jpeg.a4948b3d0738f9d1c2e8fd7338315c0d.jpeg

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56 minutes ago, strappinglad said:

Perhaps I can clarify. A test that is 100% accurate  would mean there would NEVER be false positives or false negatives. Because of manufacturing errors or human errors and maybe bad luck , there are a small %age of mistakes. A test that is 97% accurate means that 97% of the time what the test shows is what is true. So we pick a person at random . The law of large numbers says that 15% ( for the sake of argument) of the general population will have covid. So the random person has a 15% chance of having covid regardless. Now we test them and the test is 97% accurate. OK we follow the math as I showed.  

I'm still not following:

7 hours ago, strappinglad said:

What is the chance that a random person tests positive but doesn't have the disease ? 2550/17100 = 14.9%

what is the chance that a person tests negative but actually has the disease ?  450/ 82900 = 0.54 %

The chance of a random person having a false positive test is not 14.9%.   It can't be over 3% when the test is 97% accurate.  A false negative is more likely than a false positive, actually, when only symptomatic people or close contacts are getting tested.

Edited by pogi
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25 minutes ago, Ahab said:

Yeah plus even if someone dies they are likely going to a better place than among us who haven't died yet and who wouldn't rather be dead than here? Either way we're all at least one day closer to the end of the pandemic

image.jpeg.a4948b3d0738f9d1c2e8fd7338315c0d.jpeg

Please retract this.  I never said anything of the sort.  I find this post thoroughly disgusting and nothing but a childish insult.

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1 hour ago, pogi said:

Would you classify me as a doomsayer and a fear-monger?  I personally view myself as a cautious optimist who puts evidence before wishful thinking and misinformation. 

A bit of an alarmist, but not a fear-monger.  It appears to me that what you consider to be misinformation may be a little politically motivated.  But I still respect your opinions.

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8 hours ago, strappinglad said:

What is the chance that a random person tests positive but doesn't have the disease ? 2550/17100 = 14.9%

what is the chance that a person tests negative but actually has the disease ?  450/ 82900 = 0.54 %

 

I think you switched the denominators. The ones who dont have it, but are false positives should be a percentage of a number closer to 85000 surely. 

added looking at it closer:

The 17_____ denominator is not from the group of any random person (which would be 100,000), but the group of those who tested positive, right?

Edited by Calm
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Misread, faulty assumption or something due to mind fog so deleting...

Edited by Calm
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Found a page that deals with this. 

https://brownmath.com/stat/falsepos.htm

Removed quote because better understood if whole page is read.

 

Added their comment about Covid:

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As I write this, on 9 May 2020, it’s unfortunately true that a positive or negative result on a COVID-19 test can’t be interpreted using probability. To answer the questions “I tested positive; what’s the chance I actually have the virus?” or “I tested negative; what’s the chance I have the virus anyway?” you need to know three things:

  • The false positive rate for the test.
  • The false negative rate for the test.
  • The percentage of people taking the test w/ho actually have the disease.

We didn’t know any of those when I first wrote this section, but three and a half weeks later (28 May 2020) we’re getting some ideas. The FDA has published estimated sensitivity and specificity in EUA Authorized Serology Test Performance. (Remember that the false positive rate is 100% minus the specificity, and the false negative rate is 100% minus the sensitivity.) And the CDC is telling us, in the Test Performance section ofInterim Guidelines for COVID-19 Antibody Testing:

In most of the country, including areas that have been heavily impacted, the prevalence of SARS-CoV-2 antibody is expected to be low, ranging from <5% to 25%, so that testing at this point might result in relatively more false positive results and fewer false-negative results.

In some settings, such as COVID-19 outbreaks in food processing plants and congregate living facilities, the prevalence of infection in the population may be significantly higher. In such settings, serologic testing at appropriate intervals following outbreaks might result in relatively fewer false positive results and more false-negative results.

(But remember that what matters is not the prevalence of a disease in the population, but the proportion among tested people who actually have the disease.)

 

Edited by Calm
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2 hours ago, pogi said:

I'm still not following:

The chance of a random person having a false positive test is not 14.9%.   It can't be over 3% when the test is 97% accurate.  A false negative is more likely than a false positive, actually, when only symptomatic people or close contacts are getting tested.

It depends on what does "97% accuracy" mean.  Generally, a test has a "sensitivity" (true-positive) rate and a "specificity" (true-negative) rate.  Both of those rates are rarely the same.  It looks like strappinglad is giving an example where both the sensitivity and the specificity are 97%.  And his numbers are correct in that case.  But even if you give different sensitivity and specificity rates, you'll get those odd results.

The reason in why it is over 3% is because the 3% is the accuracy of the test in determining if you have the disease (technically the sensitivity rate) or if you don't have the disease (the specificity rate).  So, if I have a positive result, 3% of the time, I might actually not have the disease.  But if I have a negative result, 3% of the time, I might actually have the disease.  If the disease is in 15% of the population, then what is the chance that I actually have the disease?  If the population happens to be 100,000 (just for easy numbers), then only 15,000 people have the disease.  So, if you test a person with the disease, you'll get a positive result for 14,550 people (97% of the 15,000 infected people).  But if you test a person without the disease, you'll get a positive result for 2,550 people (3% of the 82,450 uninfected people).  So the total number of people that will have a positive result is 17,100 but only 14,550 actually have the disease.  The other 2,550 people (which is 14.9%) don't have the disease even though they tested positive.  So, for a random person who tests positive, they have a 14.9% of not having the disease.

These odd results get a lot worse when the infection rate of the disease is lower.  Extremely rare diseases have severe problems with these false positives.  Imagine only 1% of the population is infected and the test is 99% accurate.  So, in a population of 100,000, then only 1,000 people have the disease.  If you test someone with the disease, you'll get a positive result for 990 of them (99% of 1,000).  If you test someone without the disease, you'll get a positive result for 990 of them (1% of the 99,000 uninfected).  Which means that out of the 1980 people who test positive, 990 of them (50%) will be false positive.  A test with 99% accuracy will actually have 50% false positives!

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32 minutes ago, Calm said:

Out of a group of 100,000 that are pulled off the street randomly and tested, there are under your (SL) conditions, 2550 who test positive when they do not have the disease (I am assuming you got those numbers right, but not sure)...3% of the 85,000 who do not have the disease who were tested. 
 

That would be 2.55% that a random person (which includes both infected and not infected) pulled off the street tests positive when they are not, if I understand what you are trying to do.  I believe the chance for an infected person to register as not infected is .45%, making a total of 3% error. 

Now it has been decades since I took stats, but I think that is what you are trying to identify.

And I am not sure this is the right way to use the 3% error...separating out infected and not infected.  It may be more prone to false negatives, etc. 

Out of the 100,000 people, 15,000 have the disease (15% of the population).  Out of those 15,000 infected people, 14,250 will test positive with a 97% accurate test.  Out of the 85,000 uninfected people, only 4,250 will test positive (3% false positives if the test is 97% accurate).  So, a total of 18,500 people will test positive.  Out of those 18,500 people that test positive, 4,250 will not have the disease.  4250/18500 is 14.9%.  So, if you randomly test a person and that person gets a positive test, then that person will actually not have the disease 14.9% of the time.

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Posted (edited)

I missed the condition of "who tests positive" because someone who tests positive is not random in my brain, lol. So my brain went in different direction.

Ah well.  I need more sleep...what else is new.

Of course, people are not being randomly tested.  They are being tested if they have the symptoms or have been exposed to those who have the symptoms.  That makes a big difference as noted at the link:

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Gordon MacGregor points out (email dated 27 Jan 2013) one giant unstated assumption here: that people who have the disease and people who don’t have the disease are equally likely to be tested for it. That’s probably true or nearly true for diseases like HIV or Huntington’s, where people with no symptoms are encouraged to get tested and do.

But it’s emphatically not true for diseases where people are typically not tested unless they have symptoms. So really what we need to know is not the prevalence of the disease among the general population — the 0.1% in the example above — but the proportion of people who take the test that actually have the disease.

 

Edited by Calm
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Here's an example of dealing with mammograms: https://www.sciencefriday.com/articles/math-mammograms/

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In 2007, 160 gynecologists were given the following information about the accuracy of mammograms and the prevalence of breast cancer in the population:

  • The probability that a woman has breast cancer is 1 percent (prevalence).
  • If a woman has breast cancer, the probability that she tests positive is 90 percent.
  • If a woman does not have breast cancer, the probability that she nevertheless tests positive is 9 percent.

The physicians were then faced with a multiple-choice question asking them to identify which of the following statements best characterized the chances that a patient with a positive mammogram actually has breast cancer:

A) The probability that she has breast cancer is about 81 percent.

B) Out of ten women with a positive mammogram, about nine have breast cancer.

C) Out of ten women with a positive mammogram, about one has breast cancer.

D) The probability that she has breast cancer is about 1 percent.

The article explains the math in a slightly graphical manner but the answer to the above is C.  Only one woman with a positive mammogram would actually have breast cancer if the three statements are true.  And this is with a test that has a 90% sensitivity and 91% specificity.

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