COVID COVID-19 Coronavirus Containment Efforts

[morrobay]

Looking like natural selection , the virus evolving for more infectious and less lethal .

 

[OmCheeto]

Looking like natural selection , the virus evolving for more infectious and less lethal .

I think it's more likely that there weren't enough tests available in the beginning to count all the cases.
Comparing @Ygggdrasil 's "cases per million" chart to my "deaths per million" chart, aligning the USA July-August humps(circled in magenta) via the vertical axis shows that a significant portion of cases were missed in the first phase. The shapes of the curves should ideally be identical.

Attempting to track the case fatality ratio, it looks as though Norway did the best job in the beginning, with France doing the worst, and everyone ending up kind of in the same neighborhood.

 

[atyy]

I think it's more than a little silly. People are making decisions based on this, decisions that affect other people's lives.

NYC has a population of 8.4 million. They have seen 24,000 deaths. If you take the 0.26% CDC "best estimate" (0.4% fatality rate if symptomatic, and 65% symptomatic) you find that 110% of the population is infected.

And yes, I know that the 0.26% has its uncertainties. Replacing 35% with 20%, as the previously posted meta-study reported, turns this to 90%. But qualitatively, it looks like yes, they are hosing down the ashes. Pretty much anyone who could catch it has caught it.

The antibody positivity rate is about 25%, which is not consistent with the estimate of 90%.
https://www.nytimes.com/2020/08/19/nyregion/new-york-city-antibody-test.html
https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.page

 

[Vanadium 50]

The antibody positivity rate is about 25%, which is not consistent with the estimate of 90%.

That is true. You have my numbers. You can decide which one you think is wrong: the population, the number of deaths, or the fatality rate.

If you say, it's the fatality rate, though, be aware that there are 7M positive tests in the US. If you make the fatality rate 4x higher to explain New York, you would expect no fewer than 700,000 deaths in the US, not less than 200,000.

If your answer to that is "just you wait", you can look at hospitalizations.

 

[atyy]

That is true. You have my numbers. You can decide which one you think is wrong: the population, the number of deaths, or the fatality rate.

If you say, it's the fatality rate, though, be aware that there are 7M positive tests in the US. If you make the fatality rate 4x higher to explain New York, you would expect no fewer than 700,000 deaths in the US, not less than 200,000.

If your answer to that is "just you wait", you can look at hospitalizations.

I would guess the fatality rate. In Singapore the fatality rate is 0.05% (about 27 deaths, 57000 confirmed cases), so the 0.3% of the CDC is an average across different populations and times.

 

[mfb]

That is true. You have my numbers. You can decide which one you think is wrong: the population, the number of deaths, or the fatality rate.

If you say, it's the fatality rate, though, be aware that there are 7M positive tests in the US. If you make the fatality rate 4x higher to explain New York, you would expect no fewer than 700,000 deaths in the US, not less than 200,000.

If your answer to that is "just you wait", you can look at hospitalizations.

You missed a factor 10 somewhere. A 1% infection fatality rate would mean 2.4 out of 8.4 million got it in NYC, that's a bit over 1/4 and consistent with antibody tests. It would also mean the 200,000 US deaths come from 20 million cases, i.e. the US found one in three cases overall. Currently the US has ~700 deaths per day, which would come from 70,000 cases per day, which is a factor 2 higher than the confirmed case rate. No conflict here either.

That's not taking into account that treatment has improved: The infection fatality rate in NYC was worse than the IFR in states that had their outbreaks later.

 

[Rive]

Looking like natural selection , the virus evolving for more infectious and less lethal .

This kind of thing is expected at the long run, but right now there are still several strains competing without any of them becoming dominant, while the IFR dropped almost everywhere simultaneously. Not likely that this change is about genetic change.

A more likely explanation is that it's the work of some kind of common trait of all the strains what was not recognized before.

 

[atyy]

https://www.thejakartapost.com/life...y-provide-some-immunity-against-covid-19.html
Study suggests dengue may provide some immunity against COVID-19
Pedro Fonseca

https://www.medrxiv.org/content/10.1101/2020.09.19.20197749v1
How super-spreader cities, highways, hospital bed availability, and dengue fever influenced the COVID-19 epidemic in Brazil
Miguel A. L. Nicolelis, Rafael L. G. Raimundo, Pedro S. Peixoto, Cecilia Siliansky de Andreazzi

https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30158-4/fulltext
Covert COVID-19 and false-positive dengue serology in Singapore
Gabriel Yan, Chun Kiat Lee, Lawrence T M Lam, Benedict Yan, Ying Xian Chua, Anita Y N Lim, Kee Fong Phang, Guan Sen Kew, Hazel Teng, Chin Hong Ngai, Li Lin, Rui Min Foo, Surinder Pada, Lee Ching Ng, Paul Anantharajah Tambyah

The paper by Yan et al was mentioned earlier in the thread. It showed that a person with COVID-19 could be mistakenly diagnosed for dengue on the basis of an antibody test for dengue.

The new preprint by Nicolelis et al suggests that having had dengue may be protective against SARS-CoV-2 infection, on the basis that in Brazil, COVID-19 rates are lower in places that had dengue outbreaks.

 

[atyy]

This kind of thing is expected at the long run, but right now there are still several strains competing without any of them becoming dominant, while the IFR dropped almost everywhere simultaneously. Not likely that this change is about genetic change.

A more likely explanation is that it's the work of some kind of common trait of all the strains what was not recognized before.

Hopefully it has dropped, because there have been improvements in how the disease is treated, as well as more resources available for treatment. However, is there data to show that the IFR has dropped?

Initial IFR estimates ranged from 0.3% to 1% averaged across age groups. Estimated IFR was lower than 0.3% for younger people.
https://www.who.int/docs/default-so...ation-reports/20200219-sitrep-30-covid-19.pdf
https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext

Some estimates in India are suggesting an IFR of 0.1%. It is not yet clear why, but one factor may be because the median age in India is about 28, whereas the median age in the China and the USA is about 38.
https://www.hindustantimes.com/indi...-know-today/story-mtwod5mI80yuQoWEHMPZSJ.html
https://www.bloombergquint.com/coro...-exceptionalism-may-not-explain-low-mortality
"Firstly, the age-adjusted IFR is not orders of magnitude lower in India, Juneja said: “If you were to adjust for age, the IFR for Spain or even Wuhan would be around 0.2% and what we are seeing is 0.1%,” he said. Underlying immunity, he said, could potentially explain some of this gap."

 

[morrobay]

This kind of thing is expected at the long run, but right now there are still several strains competing without any of them becoming dominant, while the IFR dropped almost everywhere simultaneously. Not likely that this change is about genetic change.

The D614G on the S-protein is the dominate strain now since May. And the glycine replacement of aspartic acid at AA 614 has increased transmission/infectivity with higher virus lodes. As well as more effective
adhesion to ACE2 receptors. (salt bridges, ionic or hydrogen bonds between respective amino acids).

 

[Vanadium 50]

You missed a factor 10 somewhere.

Where? My quations are number infected = number dead / fatality rate and fraction infected =number infected / total population. number infected = 24000/0.0026 = 9.2M. Do you doubt this?

Fraction infected = =number infected / total population = 9.2M/8.4M = 110% Do you doubt this? (The calculation, not the outcome)

I don't see a factor of ten anywhere.

 

[aheight]

Looking like natural selection , the virus evolving for more infectious and less lethal .

Looks like it to me (Washington Post link): covid evolving

In general, scientists would expect natural selection to favor mutations that help the virus spread more effectively — since that allows it to make more copies of itself — but not necessarily ones that make it more virulent. Killing or incapacitating the host would generally not help the virus spread to more people.

As would I. Really, it's all a matter of survival and reproductive success.

 

[Vanadium 50]

I would guess the fatality rate. In Singapore the fatality rate is 0.05%

The problem with that explanation is that if you make the fatality rate lower, you make the NYC incidence higher and the mismatch between antibody tests and inferred incidence rates gets even more discrepant.

So far, I think the following explanations have been suggested:

1. The disease has mutated to be less deadly. This is problematic as
   1. There hasn't been much time
   2. Coronaviruses mutate slowly.
   3. THIS Coronavirus mutates slowly
   4. We have a full RNA sequence of the virus, and would know if there were two varieties, one more dangerous than the other, and there have been no reports of such
   5. Even if there were a new strain, the old strain would still be there
2. Hospital care has improved since late spring. This is inconsistent with "there is no cure and medical care revolves around relieving symptoms.".
3. I can't do arithmetic. Fair enough. Show me where.
4. Different demographics. There is something to this, as half the fatalities have been in nursing homes, and obviously those fatalities are not in the population now. It's not this simple because the nursing-home population also has an inferred infection rate near 100% (so adding it or removing it can't change the bottom line) but maybe there's something more subtle. To take an extreme case, if my antibody sample is driven by college students, I have decoupled the antibody testing rate from the fatality rate. I haven't caused them to drift apart, but they are no longer tied together.

Let me propose two other possibilities. I don't think either is right, but I don't think we have evidence against them.

1. There are two (or more) strains, both equally contagious and equally dangerous, but only one shows up on the antibody test.
2. Antibodies only persist for 6-8 weeks post infection. People can get reinfected (and we do have some examples of that). The decrease in positivity we are seeing in NYC is not driven by testing a healthier population, but is a real change over time.

 

[mfb]

Where?

In your claim that a factor 4 higher IFR would be in conflict with overall US data. If you take an IFR of 0.0026 then New York had more sick people than people, which is obviously a nonsense conclusion. If you take an IFR of 0.01, however, everything fits.

If you say, it's the fatality rate, though, be aware that there are 7M positive tests in the US. If you make the fatality rate 4x higher to explain New York, you would expect no fewer than 700,000 deaths in the US, not less than 200,000.

That's where you got factor 10 wrong somehow. You would not expect 700,000 deaths from 7 M positive tests with an IFR of 1%. You would expect at least 70,000, and indeed the US has more than 70,000 deaths.

Hospital care has improved since late spring. This is inconsistent with "there is no cure and medical care revolves around relieving symptoms.).

Treatment is not binary. Treatment can (and did) improve without a miracle cure.

I can't do arithmetic. Fair enough. Show me where.

My second post where I do now.

 

[Vanadium 50]

That's an argument that the CDC 0.26% number is wrong. That's a position that's defensible, but should be attacked on it's merits.

 

[atyy]

The problem with that explanation is that if you make the fatality rate lower, you make the NYC incidence higher and the mismatch between antibody tests and inferred incidence rates gets even more discrepant.

I was suggesting the fatality rate was higher.

 

[atyy]

Let me propose two other possibilities. I don't think either is right, but I don't think we have evidence against them.

1. There are two (or more) strains, both equally contagious and equally dangerous, but only one shows up on the antibody test.
2. Antibodies only persist for 6-8 weeks post infection. People can get reinfected (and we do have some examples of that). The decrease in positivity we are seeing in NYC is not driven by testing a healthier population, but is a real change over time.

https://www.bmj.com/content/370/bmj.m3325
There could be false negatives (but I would guess it's not due to viral variants), since test sensitivity (lack of false negatives) is about 90% for the commonly used tests in the UK, and can be lower than 90% depending on when the person is tested, as it takes time for antibodies to build up after a person gets infected. There is evidence for the possibility that antibodies decrease after 6-8 weeks, but we can look at the data for earlier in the year.

NYC reached about 18000 deaths (13000 confirmed, 5000 probable) by May 2, and 20000 deaths (15000 confirmed, 5000 probable) by May 15.
https://www.cdc.gov/mmwr/volumes/69/wr/mm6919e5.htm
https://www.pix11.com/news/coronavirus/latest-coronavirus-updates-in-new-york-friday-may-15-2020

NYC antibody positivity rates were about 20% by May 2.
https://www.governor.ny.gov/news/am...-announces-results-completed-antibody-testing

Let's take 15000 deaths, and 25% positivity rate (to account for false negatives in the antibody testing), and an NYC population of 8400000. This gives an IFR of (15000 x 100%)/(0.25 x 84000000) = 0.7%. So it is quite reasonable that the IFR was higher than 0.3% in the early stage of the outbreak in NYC.

 

[mfb]

That's an argument that the CDC 0.26% number is wrong. That's a position that's defensible, but should be attacked on it's merits.

CDC's number being too low is more plausible than New York having more sick people than people. And yes, as discussed before, we were studying how the numbers work out if the CDC number is too low. All the US numbers fit very nicely if we assume a higher IFR.AstraZeneca, Under Fire for Vaccine Safety, Releases Trial Blueprints
Interesting article about their vaccine. Not so much about what the title says, but they have a bit of information about the two mystery patients. Two cases of transverse myelitis in the vaccine group, a relatively rare disease that can be associated with infections. One of the patients has MS which can cause it as well, for the other patient we don't know.

 

[atyy]

I can't do arithmetic. Fair enough. Show me where.

Neither @mfb nor I understand why you said that 7,000,000 confirmed cases in the US mean at least 700,000 deaths with an IFR of 1%. We think that 7,000,0000 confirmed cases mean at least 70,000 deaths if the IFR is 1%.

Overall an IFR of 1% may be a bit high, but given that the NYC health system was overwhelmed in the early stages, it seems plausible that IFR in the early stages of the NYC outbreak was higher, similar to how the confirmed case fatality rates in Hubei (where the Wuhan health system was initially overwhelmed) were 5X higher than outside of Hubei. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30746-7/fulltext

 

[russ_watters]

...but given that the NYC health system was overwhelmed in the early stages...

I don't think the evidence supports that. The media played-up busy hospitals and harried staff, but NYC added substantial emergency capacity, which went almost completely unused.
https://www.navytimes.com/news/your...s-nyc-having-treated-fewer-than-200-patients/

https://www.google.com/amp/s/abc7ny.com/amp/coronavirus-nyc-update-corona-virus-cases/6142109/

https://www.militarytimes.com/news/...orkers-are-going-straight-into-nyc-hospitals/

 

[atyy]

I don't think the evidence supports that. The media played-up busy hospitals and harried staff, but NYC added substantial emergency capacity, which went almost completely unused.
https://www.navytimes.com/news/your...s-nyc-having-treated-fewer-than-200-patients/

https://www.google.com/amp/s/abc7ny.com/amp/coronavirus-nyc-update-corona-virus-cases/6142109/

https://www.militarytimes.com/news/...orkers-are-going-straight-into-nyc-hospitals/

Your links don't support that hospitals were not overwhelmed. The extra personnel were used, and some extra capacity was used.

https://www.militarytimes.com/news/...orkers-are-going-straight-into-nyc-hospitals/
"About 200 doctors, nurses, respiratory therapists and others are working in New York’s medical centers, where bed space has not been overwhelmed, but where hospital-acquired Coronavirus cases have sidelined civilian staff. "

https://www.navytimes.com/news/your...s-nyc-having-treated-fewer-than-200-patients/
"The Javits Center, which was initially envisioned as a 2,500-bed field hospital for non-COVID patients, converted to coronavirus-only hospital shortly after going operational. Still, the highest number of patients treated at the convention center at one time topped out at close to 500."

Another piece of evidence that the hospital system was stretched is that some instituted ventilator sharing, which is not usual practice. https://www.nbcnewyork.com/news/cor...to-buy-time-for-coronavirus-patients/2363049/

 

[russ_watters]

Your links don't support that hospitals were not overwhelmed. The extra personnel were used, and some extra capacity was used.

Well not for nothing, but your first link explicitly states that the hospitals were not overwhelmed.

Using some of the emergency capacity doesn't mean all the hospitals totally filled-up first. My understanding is the emergency capacity was for non-COVID patients or those who were convalescing after they were out of the woods. It's part of a re-shuffling of resources designed in part to prevent future capacity issues (along with cancelling regular appointments and non-emergency surgeries). Here's a more specific article on the subject:
https://www.propublica.org/article/how-americas-hospitals-survived-the-first-wave-of-the-coronavirus

“We have 53,000 hospital beds available,” Cuomo, a Democrat, said at a briefing on March 22. “Right now, the curve suggests we could need 110,000 hospital beds...”

...But when New York hit its peak in early April, fewer than 19,000 people were hospitalized with COVID-19.

It's fair to say resources (in particular, personnel) were stretched, a small number of hospitals filled to capacity and that there were specific shortages of materials and equipment, but "overwhelmed" has a much worse connotation than that -- a connotation that is necessary to support the argument that a substantially higher death rate occurred because of hospital capacity issues. E.G., you cited a 5x higher IFR in Hubei as an example. You can't get a 5x higher IFR due to "overwhelmed" hospitals without at least a system-wide 80% shortfall in critical needs. Sharing ventilators isn't enough: you'd need to be rationing them to every 5th person who needs them. Or have a smaller overage that causes a systemic collapse. But NYC was nowhere close to full capacity overall.

And the article provides a specific reason that the projections were wrong -- it's the issue we're discussing: the disease is/was nowhere near as severe as the early projections. From the article, it says the CDC initially estimated 11 hospitalizations would be needed per death, then later dropped it to 7, then later to 4. This is almost certainly the same as the CFR issue; the disease is less severe than initially thought because the testing shortage meant we were missing most of the people infected. This almost certainly affected Hubei as well. There may also have been political approach factors at play (e.g., if they quarantined every infected person at field hospitals instead of letting them quarantine at home), but I'm not sure of the details of that.

New York's early testing rate was way low. At their peak in early April they had a 50% positivity rate. Their testing rate was many, many times lower than it should have been and they almost certainly missed the large majority of their infected:
https://coronavirus.jhu.edu/testing/individual-states/new-york
Beginning of April ~20,000 tests per day and 50% positive.

 

[atyy]

It's fair to say resources (in particular, personnel) were stretched, a small number of hospitals filled to capacity and that there were specific shortages of materials and equipment, but "overwhelmed" has a much worse connotation than that -- a connotation that is necessary to support the argument that a substantially higher death rate occurred because of hospital capacity issues. E.G., you cited a 5x higher IFR in Hubei as an example. You can't get a 5x higher IFR due to "overwhelmed" hospitals without at least a system-wide 80% shortfall in critical needs. Sharing ventilators isn't enough: you'd need to be rationing them to every 5th person who needs them. Or have a smaller overage that causes a systemic collapse. But NYC was nowhere close to full capacity overall.

And the article provides a specific reason that the projections were wrong -- it's the issue we're discussing: the disease is/was nowhere near as severe as the early projections. From the article, it says the CDC initially estimated 11 hospitalizations would be needed per death, then later dropped it to 7, then later to 4. This is almost certainly the same as the CFR issue; the disease is less severe than initially thought because the testing shortage meant we were missing most of the people infected. This almost certainly affected Hubei as well. There may also have been political approach factors at play (e.g., if they quarantined every infected person at field hospitals instead of letting them quarantine at home), but I'm not sure of the details of that.

Yes, "stretched" is a better word. And definitely it is a matter of conjecture, whether stretched hospital care contributes to explaining why the IFR in NYC could have been higher than 0.3% in the early phases of the outbreak. Even in Hubei, the 5X is a CFR, not IFR, so after adjustments for different methods of counting cases, it may translate into only a small difference in IFR between Hubei and other parts of China, and the uncertainties are consistent with no difference (https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext).

Interestingly, in Wuhan, it does seem that the extra hospital capacity was used: https://www.reuters.com/article/us-health-coronavirus-china-toll-idUSKBN20P01K.

 

[mfb]

the disease is/was nowhere near as severe as the early projections. From the article, it says the CDC initially estimated 11 hospitalizations would be needed per death, then later dropped it to 7, then later to 4.

The CDC initially estimated a 9% fatality rate for hospitalized cases then updated that to 14%, then to 25%, and that's less severe than the 9%?

 

[russ_watters]

The CDC initially estimated a 9% fatality rate for hospitalized cases then updated that to 14%, then to 25%, and that's less severe than the 9%?

I suppose if you ignore the dropping per case death rate that could be confusing. The entire line has stretched, and the death count is the anchor: we were missing most of the less intense cases. As the pandemic has progressed and the testing rate has increased we have seen fewer hospitalizations and many fewer deaths per case than was initially expected. I really don't understand why people seem to be pretending that the case rates in March/April were accurate and misconstruing the resulting shift in the statistics. Are you expecting an explosion of deaths in Germany in the next few weeks due to the increasing case rate there? Germany peaked around 5,000 cases per day and 200 deaths per day (average over about a week - guestimated) in the spring. Now in the second peak it is seeing around 2,000 cases and 10 deaths per day. Are you expecting the death rate to increase by a factor of 4 in the near future? I don't; I expect that like most other countries, the early severity of the virus (in terms of hospitalizations and deaths) was overestimated due to the low testing rates for people with milder symptoms.

 

[russ_watters]

Yes, "stretched" is a better word. And definitely it is a matter of conjecture, whether stretched hospital care contributes to explaining why the IFR in NYC could have been higher than 0.3% in the early phases of the outbreak.

IMO, it shouldn't be controversial. When the testing rate is known to be extremely low, it shouldn't be controversial that it plays a bigger role in the statistics than a "stretched" medical system.

I really find this truly bizarre that you (not the specific "you", but the general) are trying to stretch a few percent here or there into hundreds of percent. We don't know if single digit, dozens or hundreds of people overall died because they shared ventilators, but we do know that millions of people who should have been tested in New York, alone, were not. Over the past month, the US media anyway has been breathlessly reporting the record daily case rates, without ever reporting (that I have seen) that those case rates are offset by a factor of 5 or 10 from those in March/April due to the higher testing rate.

 

[Rive]

...the disease is less severe than initially thought because the testing shortage meant we were missing most of the people infected.

That does not fit well with the result of some studies from spring where whole societies (!) were tested. Those studies were the ones which established the whole 'half the infected are without symptoms' thing.

BTW is there any studies already about the 'CFR per age group' kind of statistics from summer? I would like to see some.
Without the 'age group' part raw CFR or IFR does not really worth anything.
At least I think so.

 

[atyy]

IMO, it shouldn't be controversial. When the testing rate is known to be extremely low, it shouldn't be controversial that it plays a bigger role in the statistics than a "stretched" medical system.

I really find this truly bizarre that you (not the specific "you", but the general) are trying to stretch a few percent here or there into hundreds of percent. We don't know if single digit, dozens or hundreds of people overall died because they shared ventilators, but we do know that millions of people who should have been tested in New York, alone, were not. Over the past month, the US media anyway has been breathlessly reporting the record daily case rates, without ever reporting (that I have seen) that those case rates are offset by a factor of 5 or 10 from those in March/April due to the higher testing rate.

Let's say 4 people in 1000 die if everyone that dies needs a ventilator and has to share (ie. sharing ventilators is ineffective), and 2 people in 1000 die if everyone who needs a ventilator gets their own (ventilators save 50% of those who need one). Then one would get a 100% increase in fatality rate (from 2/1000 to 4/1000), even taking into account that many do not get tested.

 

[russ_watters]

Let's say 4 people in 1000 die if everyone that dies needs a ventilator and has to share (ie. sharing ventilators is ineffective), and 2 people in 1000 die if everyone who needs a ventilator gets their own (ventilators save 50% of those who need one). Then one would get a 100% increase in fatality rate (from 2/1000 to 4/1000), even taking into account that many do not get tested.

Right, that's my point: we didn't have a 100% ventilator shortage. Ventilator sharing happened, but it wasn't common, much less universal.

 

[mfb]

@Russ: No one questions that many cases were missed in March and April. That's not what the discussion was about. Cases that go to a hospital are severe cases, they are not missed. If these people die more often than estimated before, how is that change in particular making the disease less dangerous? You cited that change as evidence that the danger was overestimated.
If you argue people got less likely to be admitted to a hospital than before - while the disease stays unchanged - that would mean hospitals had to turn down increasingly severe cases. That would mean they are overwhelmed. I don't say that's true, but that's one of the few ways to interpret these numbers without saying it kills more people than expected before.