OG doc. AMA on COVID-19

The Stewed Owl - 

mataleo1, I'd be interested in hearing your opinion on the new "Oxford model" if you have time to read it. Seems like either very good news or very very bad news if wrong. 

The latest study is surprisingly encouraging


Yes I had read that. I actually have friends (in Oxford!) who do AI-based epidemiological models. Their conclusions: according to their model, this could turn out to weather the storm quicker than those proposing very stringent isolation. HOWEVER, considering the unknowns there are small chances that this will produce VERY bad results.

Something like (that's how they voiced it):
Oxford: 96% good outcome, 2% bad outcome, 2% catastrophic
Confinement: 95% good outcome, 4% bad outcome, 1% catastrophic

Their conclusions: bad idea. The risks of a catastrophic outcome do not compensate for a significant potential for a quicker recovery

Mountain Medic - 

Hey docs- we are getting information on how long it might survive on cardboard and stainless steel.


 


Any thoughts on waterbourne , watershed viability/transmission?


 


Not trying to be a conspiracy guy, I'm just a rafter and spring run off is coming....


 


There are a few studies, the most famous one being that published in NEJM: https://www.nejm.org/doi/10.1056/NEJMc2004973

aerosols: 3h
Copper: 4h
Cardboard: 24h
Plastic: 2-3d
Steel: 2-3d

Will check for waterbourne, read conflicting data.

1 Like

Someone in another thread said the biggest goal of the shelter in place is to just slow the infection rate of the virus so hospitals don't get overwhelmed and that as this plays out, no matter what, 90% of people will get the virus.

Are we really going to see 90% infection rate, not at once, but over the course of a year?

BJ Penn Forever - 

Someone in another thread said the biggest goal of the shelter in place is to just slow the infection rate of the virus so hospitals don't get overwhelmed and that as this plays out, no matter what, 90% of people will get the virus.


Are we really going to see 90% infection rate, not at once, but over the course of a year?


I find these numbers really high. I think it will be a lot less. In any case, I don't care about that statistic too much.

I care about the absolute number of bad outcomes: deaths, hospitalizations, critical care, intubations, sequelae.

Shelters, social distancing will DELAY transmission rather than prevent it. That flattens the curve and prevents overwhelming hospitals.

mataleo1 -
BJ Penn Forever - 

Someone in another thread said the biggest goal of the shelter in place is to just slow the infection rate of the virus so hospitals don't get overwhelmed and that as this plays out, no matter what, 90% of people will get the virus.


Are we really going to see 90% infection rate, not at once, but over the course of a year?


I find these numbers really high. I think it will be a lot less. In any case, I don't care about that statistic too much.

I care about the absolute number of bad outcomes: deaths, hospitalizations, critical care, intubations, sequelae.

Shelters, social distancing will DELAY transmission rather than prevent it. That flattens the curve and prevents overwhelming hospitals.

Glad to hear those numbers sound high. Just can't deal with the thought of my 70 year old parents battling this illness. Concerned for everyone but of course, they are my primary concern. I see why bad outcomes are your concerns.


Hoping the curve stays flat enough for you guys.


Thanks for taking the time to answer all these dumb questions.


Hoping you are finding the time to sleep, eat, decompress, etc...

hi mataleo1, any guestimate based on current trends and charts, when it would start to flatten in canada? (weeks, months, years?).

yusul - 

hi mataleo1, any guestimate based on current trends and charts, when it would start to flatten in canada? (weeks, months, years?).


The fact that it's such a big country and not densely populated, it could be longer than most in europe (transmission will occur over a longer period). However, you can make the argument that the big cities will be hit hard while smaller towns will be relatively spared.

I'm expecting 6-10 weeks to start seeing cases decreasing. I'm also certain i'll be way off :)

1 Like

Good thread! What’s your take on why Canada is only reporting 1 serious/critical cases out of 2700 active cases - because most haven’t resolved yet? Seems a lot lower than countries with comparable active cases. Thanks for doing this

mataleo1 -
The Stewed Owl - 

mataleo1, I'd be interested in hearing your opinion on the new "Oxford model" if you have time to read it. Seems like either very good news or very very bad news if wrong. 

The latest study is surprisingly encouraging


Yes I had read that. I actually have friends (in Oxford!) who do AI-based epidemiological models. Their conclusions: according to their model, this could turn out to weather the storm quicker than those proposing very stringent isolation. HOWEVER, considering the unknowns there are small chances that this will produce VERY bad results.

Something like (that's how they voiced it):
Oxford: 96% good outcome, 2% bad outcome, 2% catastrophic
Confinement: 95% good outcome, 4% bad outcome, 1% catastrophic

Their conclusions: bad idea. The risks of a catastrophic outcome do not compensate for a significant potential for a quicker recovery
 


My training is in simulations.


 


So these are my opinions, but not discussed or checked by somebody else.


All simulations are limited by your assumptions. Monte Carlo simulations are a standard technique, but the way it's run and parameter value will introduce some errors.  Their biggest assumption is that 1% of people die(or so).


 


One test of a model is how it fits with the real data. In this case, the fit with the UK and Italian data is good, or so they say. When you plot logs, which is what they did, you can get anything to line up. Would need somebody with better knowledge of data to check.


 


The study around be better if they compare it to other reliable data. .
 the east Asian data may not be suitable. Chinese data is trash and South Korea and Taiwan implemented quarantines and such, which their model doesn't account for.  Would be interesting if they tried it out on upcoming New York data.


 


lastly, and this is from being related to doctors, again they plot deaths on a log scale. Normal people think inn term f of linear scales. So the deaths really do jump, even in their model.  It just doesn't look like it on the graph.


 


Also, hospitals are not built for big events. The Boston bomber event was a huge load on the hospitals in Boston, even though it might've been just a few hundred. They are not built for those kinds of spikes. Doctors are not used to wear the Italian doctors are seeing, hence the level of  world ending desperation.


 


To me, it's definitely encouraging. We'll find out soon enough. There are other data points tart might support the idea, like the cruise ship data


 


. We'll find out.


 


 

1 Like
asdf - 
mataleo1 -
The Stewed Owl - 

mataleo1, I'd be interested in hearing your opinion on the new "Oxford model" if you have time to read it. Seems like either very good news or very very bad news if wrong. 

The latest study is surprisingly encouraging


Yes I had read that. I actually have friends (in Oxford!) who do AI-based epidemiological models. Their conclusions: according to their model, this could turn out to weather the storm quicker than those proposing very stringent isolation. HOWEVER, considering the unknowns there are small chances that this will produce VERY bad results.

Something like (that's how they voiced it):
Oxford: 96% good outcome, 2% bad outcome, 2% catastrophic
Confinement: 95% good outcome, 4% bad outcome, 1% catastrophic

Their conclusions: bad idea. The risks of a catastrophic outcome do not compensate for a significant potential for a quicker recovery
 


My training is in simulations.


 


So these are my opinions, but not discussed or checked by somebody else.


All simulations are limited by your assumptions. Monte Carlo simulations are a standard technique, but the way it's run and parameter value will introduce some errors.  Their biggest assumption is that 1% of people die(or so).


 


One test of a model is how it fits with the real data. In this case, the fit with the UK and Italian data is good, or so they say. When you plot logs, which is what they did, you can get anything to line up. Would need somebody with better knowledge of data to check.


 


The study around be better if they compare it to other reliable data. .
 the east Asian data may not be suitable. Chinese data is trash and South Korea and Taiwan implemented quarantines and such, which their model doesn't account for.  Would be interesting if they tried it out on upcoming New York data.


 


lastly, and this is from being related to doctors, again they plot deaths on a log scale. Normal people think inn term f of linear scales. So the deaths really do jump, even in their model.  It just doesn't look like it on the graph.


 


Also, hospitals are not built for big events. The Boston bomber event was a huge load on the hospitals in Boston, even though it might've been just a few hundred. They are not built for those kinds of spikes. Doctors are not used to wear the Italian doctors are seeing, hence the level of  world ending desperation.


 


To me, it's definitely encouraging. We'll find out soon enough. There are other data points tart might support the idea, like the cruise ship data


 


. We'll find out.


 


 


This is super interesting, thank you.

I'm for a game-playing background (chess) so this is really fascinating to me, although I can't say I understand any of it.

Whats your take on remdesivir. This lady was treated with it after Covid-19 resulted in pneumonia and helped her recover.
Isolated case obviously, but positive result nonetheless



https://www.yahoo.com/news/palo-alto-woman-recovers-covid-012006288.html

That Oxford model is interesting but, like they ask at the end of the article, how do you explain the explosion in cases in Italy, Spain, NYC, etc?

mataleo1 - 
asdf - 
mataleo1 -
The Stewed Owl - 

mataleo1, I'd be interested in hearing your opinion on the new "Oxford model" if you have time to read it. Seems like either very good news or very very bad news if wrong. 

The latest study is surprisingly encouraging


Yes I had read that. I actually have friends (in Oxford!) who do AI-based epidemiological models. Their conclusions: according to their model, this could turn out to weather the storm quicker than those proposing very stringent isolation. HOWEVER, considering the unknowns there are small chances that this will produce VERY bad results.

Something like (that's how they voiced it):
Oxford: 96% good outcome, 2% bad outcome, 2% catastrophic
Confinement: 95% good outcome, 4% bad outcome, 1% catastrophic

Their conclusions: bad idea. The risks of a catastrophic outcome do not compensate for a significant potential for a quicker recovery
 


My training is in simulations.


 


So these are my opinions, but not discussed or checked by somebody else.


All simulations are limited by your assumptions. Monte Carlo simulations are a standard technique, but the way it's run and parameter value will introduce some errors.  Their biggest assumption is that 1% of people die(or so).


 


One test of a model is how it fits with the real data. In this case, the fit with the UK and Italian data is good, or so they say. When you plot logs, which is what they did, you can get anything to line up. Would need somebody with better knowledge of data to check.


 


The study around be better if they compare it to other reliable data. .
 the east Asian data may not be suitable. Chinese data is trash and South Korea and Taiwan implemented quarantines and such, which their model doesn't account for.  Would be interesting if they tried it out on upcoming New York data.


 


lastly, and this is from being related to doctors, again they plot deaths on a log scale. Normal people think inn term f of linear scales. So the deaths really do jump, even in their model.  It just doesn't look like it on the graph.


 


Also, hospitals are not built for big events. The Boston bomber event was a huge load on the hospitals in Boston, even though it might've been just a few hundred. They are not built for those kinds of spikes. Doctors are not used to wear the Italian doctors are seeing, hence the level of  world ending desperation.


 


To me, it's definitely encouraging. We'll find out soon enough. There are other data points tart might support the idea, like the cruise ship data


 


. We'll find out.


 


 


This is super interesting, thank you.

I'm for a game-playing background (chess) so this is really fascinating to me, although I can't say I understand any of it.


I hope they are doing the blood tests for antibodies as described in the linked article, if we have a sizable percentage that have been had the virus and come out the other side with immunity, we could get a lot of the population back to work. That would be a best case scenario, though I wouldn't count on it.

If you have a sandwich and someone sneezes or touches the sandwich with the virus and you eat the sandwich, can you get sick?

I know you can get the stomach flu from eating "bad/contaminated" food, but can you get a rhinovirus or coronavirus from eating food?

I know the mouth has a mucous membrane, so I figure the answer is yes, but not sure (I'm always paranoid about eating food left out at work because it might have strange viruses on it from other people).

I'm neither in the "this is all just a bad flu season" camp or the "this is the end of humanity" camp, so I'm not posting this to try to prove a pet theory - I have none. This aligns with the Oxford Model and is probably overly optimistic, but is worth discussing. This from the Wall Street Journal today:

Is the Coronavirus as Deadly as They Say?
MARCH 25, 2020 / BRIANPECKFORD
From The Wall Street Journal

Is the Coronavirus as Deadly as They Say?

Current estimates about the Covid-19 fatality rate may be too high by orders of magnitude.

By Eran Bendavid and Jay Bhattacharya
March 24, 2020 6:21 pm ET

If it’s true that the novel coronavirus would kill millions without shelter-in-place orders and quarantines, then the extraordinary measures being carried out in cities and states around the country are surely justified. But there’s little evidence to confirm that premise—and projections of the death toll could plausibly be orders of magnitude too high.

Fear of Covid-19 is based on its high estimated case fatality rate—2% to 4% of people with confirmed Covid-19 have died, according to the World Health Organization and others. So if 100 million Americans ultimately get the disease, two million to four million could die. We believe that estimate is deeply flawed. The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases.

The latter rate is misleading because of selection bias in testing. The degree of bias is uncertain because available data are limited. But it could make the difference between an epidemic that kills 20,000 and one that kills two million. If the number of actual infections is much larger than the number of cases—orders of magnitude larger—then the true fatality rate is much lower as well. That’s not only plausible but likely based on what we know so far.

Population samples from China, Italy, Iceland and the U.S. provide relevant evidence. On or around Jan. 31, countries sent planes to evacuate citizens from Wuhan, China. When those planes landed, the passengers were tested for Covid-19 and quarantined. After 14 days, the percentage who tested positive was 0.9%. If this was the prevalence in the greater Wuhan area on Jan. 31, then, with a population of about 20 million, greater Wuhan had 178,000 infections, about 30-fold more than the number of reported cases. The fatality rate, then, would be at least 10-fold lower than estimates based on reported cases.

Next, the northeastern Italian town of Vò, near the provincial capital of Padua. On March 6, all 3,300 people of Vò were tested, and 90 were positive, a prevalence of 2.7%. Applying that prevalence to the whole province (population 955,000), which had 198 reported cases, suggests there were actually 26,000 infections at that time. That’s more than 130-fold the number of actual reported cases. Since Italy’s case fatality rate of 8% is estimated using the confirmed cases, the real fatality rate could in fact be closer to 0.06%.

In Iceland, deCode Genetics is working with the government to perform widespread testing. In a sample of nearly 2,000 entirely asymptomatic people, researchers estimated disease prevalence of just over 1%. Iceland’s first case was reported on Feb. 28, weeks behind the U.S. It’s plausible that the proportion of the U.S. population that has been infected is double, triple or even 10 times as high as the estimates from Iceland. That also implies a dramatically lower fatality rate.

The best (albeit very weak) evidence in the U.S. comes from the National Basketball Association. Between March 11 and 19, a substantial number of NBA players and teams received testing. By March 19, 10 out of 450 rostered players were positive. Since not everyone was tested, that represents a lower bound on the prevalence of 2.2%. The NBA isn’t a representative population, and contact among players might have facilitated transmission. But if we extend that lower-bound assumption to cities with NBA teams (population 45 million), we get at least 990,000 infections in the U.S. The number of cases reported on March 19 in the U.S. was 13,677, more than 72-fold lower. These numbers imply a fatality rate from Covid-19 orders of magnitude smaller than it appears.

How can we reconcile these estimates with the epidemiological models? First, the test used to identify cases doesn’t catch people who were infected and recovered. Second, testing rates were woefully low for a long time and typically reserved for the severely ill. Together, these facts imply that the confirmed cases are likely orders of magnitude less than the true number of infections. Epidemiological modelers haven’t adequately adapted their estimates to account for these factors.

The epidemic started in China sometime in November or December. The first confirmed U.S. cases included a person who traveled from Wuhan on Jan. 15, and it is likely that the virus entered before that: Tens of thousands of people traveled from Wuhan to the U.S. in December. Existing evidence suggests that the virus is highly transmissible and that the number of infections doubles roughly every three days. An epidemic seed on Jan. 1 implies that by March 9 about six million people in the U.S. would have been infected. As of March 23, according to the Centers for Disease Control and Prevention, there were 499 Covid-19 deaths in the U.S. If our surmise of six million cases is accurate, that’s a mortality rate of 0.01%, assuming a two week lag between infection and death. This is one-tenth of the flu mortality rate of 0.1%. Such a low death rate would be cause for optimism.

This does not make Covid-19 a nonissue. The daily reports from Italy and across the U.S. show real struggles and overwhelmed health systems. But a 20,000- or 40,000-death epidemic is a far less severe problem than one that kills two million. Given the enormous consequences of decisions around Covid-19 response, getting clear data to guide decisions now is critical. We don’t know the true infection rate in the U.S. Antibody testing of representative samples to measure disease prevalence (including the recovered) is crucial. Nearly every day a new lab gets approval for antibody testing, so population testing using this technology is now feasible.

If we’re right about the limited scale of the epidemic, then measures focused on older populations and hospitals are sensible. Elective procedures will need to be rescheduled. Hospital resources will need to be reallocated to care for critically ill patients. Triage will need to improve. And policy makers will need to focus on reducing risks for older adults and people with underlying medical conditions.

A universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health. We should undertake immediate steps to evaluate the empirical basis of the current lockdowns.

Dr. Bendavid and Dr. Bhattacharya are professors of medicine at Stanford. Neeraj Sood contributed to this article.

1 Like

Stache - That Oxford model is interesting but, like they ask at the end of the article, how do you explain the explosion in cases in Italy, Spain, NYC, etc?

A specific set of circumstances tailor made to spread the virus. Circumstances not at all inline with the rest of the country, i.e., old sick population, Italy. Densely populated, international hub with public transport and people living on top of one another, NYC...

It's not the same in Toledo Ohio, or Ft. Walton Beach Florida. Hell even Los Angeles is managing just fine, a couple deaths every other day, if that.

I think this was mentioned earlier. I'm not negating the very real and serious impacts of imposing quarantines. Foreclosures, collapsing businesses, health issues related to financial loss, suicides, psychiatric illness, social isolation, and plenty of others. These are very real.

These must be balanced with the risks of not doing anything (or limiting our interventions for prevention) which could lead to scenarios that worse than we've seen in Italy. Keep in mind that some or all the financial downfalls above may likely happen if the virus hits us hard.

Neither the pandemic deniers nor the servants of the apocalypse have the answers. Most epidemiologists and public health experts should have considered these issues when proposing guidelines. I really wished we had used more AI modeling to predict what best to do (and to change our course of action quickly if we're on the wrong track). Not easy when facing a novel infectious agent.

No one has the answers, and only hindsight will tell us.

jcblass - 
Stache - That Oxford model is interesting but, like they ask at the end of the article, how do you explain the explosion in cases in Italy, Spain, NYC, etc?

A specific set of circumstances tailor made to spread the virus. Circumstances not at all inline with the rest of the country, i.e., old sick population, Italy. Densely populated, international hub with public transport and people living on top of one another, NYC...

It's not the same in Toledo Ohio, or Ft. Walton Beach Florida. Hell even Los Angeles is managing just fine, a couple deaths every other day, if that.

True, but these are only hypotheses. Why do you explain that Rome wasn't as hardly hit as Milano? Rome's population is more densely populated, is older, and receives more tourists.

Other small towns were hit hard.

mataleo1 - 
jcblass - 
Stache - That Oxford model is interesting but, like they ask at the end of the article, how do you explain the explosion in cases in Italy, Spain, NYC, etc?

A specific set of circumstances tailor made to spread the virus. Circumstances not at all inline with the rest of the country, i.e., old sick population, Italy. Densely populated, international hub with public transport and people living on top of one another, NYC...

It's not the same in Toledo Ohio, or Ft. Walton Beach Florida. Hell even Los Angeles is managing just fine, a couple deaths every other day, if that.

True, but these are only hypotheses. Why do you explain that Rome wasn't as hardly hit as Milano? Rome's population is more densely populated, is older, and receives more tourists.

Other small towns were hit hard.


I would imagine Rome might have more resources, the people more wealthy and in better health than Lombardy, or there is something about this virus, we haven't quite figured out, that makes it pray on some people more than others. However, generally speaking, these outbreaks have been regional and afflicted the elderly and immune compromised. That one variable has been stable throughout and mainly why I think a good % of the country can get back to work in the next week or two.

Obviously, if it is a regional hot spot you don't, but we can start relaxing some of this.

jcblass - 
mataleo1 - 
jcblass - 
Stache - That Oxford model is interesting but, like they ask at the end of the article, how do you explain the explosion in cases in Italy, Spain, NYC, etc?

A specific set of circumstances tailor made to spread the virus. Circumstances not at all inline with the rest of the country, i.e., old sick population, Italy. Densely populated, international hub with public transport and people living on top of one another, NYC...

It's not the same in Toledo Ohio, or Ft. Walton Beach Florida. Hell even Los Angeles is managing just fine, a couple deaths every other day, if that.

True, but these are only hypotheses. Why do you explain that Rome wasn't as hardly hit as Milano? Rome's population is more densely populated, is older, and receives more tourists.

Other small towns were hit hard.


I would imagine Rome might have more resources, the people more wealthy and in better health than Lombardy, or there is something about this virus, we haven't quite figured out, that makes it pray on some people more than others. However, generally speaking, these outbreaks have been regional and afflicted the elderly and immune compromised. That one variable has been stable throughout and mainly why I think a good % of the country can get back to work in the next week or two.

Obviously, if it is a regional hot spot you don't, but we can start relaxing some of this.


I get your points and they are completely valid. But neither you nor I should be making these decisions. It worries me to see calls for "extreme and total isolation" or "a full stop to isolation procedures" without any kind of experience.

Nobody knows for sure, but some know more than others. We've got pretty smart people in this country who do this for a living and I defer to them on how strict isolation should be, how long it should last, and where it should be done.