patience_and_focus

Programming is a very good case study. Webex has been around for 20 years. Skype has been around for 15 years. Yet the image of a lone programmer coding away on a Hawaii beach is (mostly) a myth. Yes there are those, but very small percentage of overall number. If anything despite the new age tools, co-location was driving innovation in the software world. Now there is always an argument that webex and skype were clunky. But they were better than nothing during early-mid 90's, and still the co-lation of software engineers into city centers accelerated after that.

Agree in the short term. Disagree in the medium to long term (> 5 yrs). The powerful economic forces that have been making large cities grow even larger, especially over the last 30-40 years (shall I hazard to say at least a 100 years) are not going anywhere. This is a bump in that process. Large cities like London survived bouts of plague in the distant past. Modern cities like SF and NYC survived spanish flu, HIV etc. Asian cities like Hong Kong, Shanghai, and Singapore survived SARS (far more deadly than this current SARS-CoV2) and grew nevertheless in the last 20-25 years. That doesn't mean there won't be damage and bankruptcy in real-estate tied to offices in the next 1-5 years. But I don't see long term trends changing, especially when this virus does not hit the young and urban crowd hard. The old folks living in cities or nearby crowded suburbs may move out at faster pace. Frankly that is desirable because at least in Bay area housing and construction is depressed partly due to NIMBY supporting older folks and outdated zoning laws. Young people are far more receptive of changing zoning and allowing construction of both residential and office buildings.

Clearly many in the white house have not gotten this memo and are setting a different example for their supporters to emulate.

Of course masks are good. I was also highly in favor of masks early in this thread. You just can't keep saying that masks aren't being pushed because of WHO. It's also because of Trump, who won't even wear one, who won't use his influence and reach to convince people to wear them (one of the main powers of the presidency is explaining things to citizens and persuading them of something), and who didn't order emergency production of PPE months ago despite talking about it repeatedly, and has greatly contributed by his words and actions to politicizing them (should seatbelts and condoms and vaccines also be politicized? so stupid). It's not like other countries can't and haven't pushed masked whatever the WHO said early on, or if they listened at first, haven't later course-corrected rather than kept going in the wrong direction. Hallmarks of a good investor (and should I dare say someone who is good at anything else) is to realize their mistakes if they have been made (they don't even have to publicize them) and course correct when facts change on the ground. Can't say that with the current white house - https://www.cnbc.com/2020/05/21/trump-doesnt-wear-coronavirus-mask-to-ford-plant.html

This is what I call crappy "data analysis". This person who wrote this bloomberg article will fail any data analysis 101 course. Here are just a couple of the many issues - (a) The article says - "But, as our next chart shows, there’s little correlation between the severity of a nation’s restrictions and whether it managed to curb excess fatalities — a measure that looks at the overall number of deaths compared with normal trends." Where the hell is correlation coefficient value? And the p-value? And the chart is NOT an X versus Y plot of stringency score versus deaths. The stringency score used for correlation was at what time (since stringency concept changes over time)? Is the correlation a time series auto-correlation or point correlation? A claim is made in the above sentence about "little correlation", but no numbers to back it up. (b) Here the article conveniently provides an X vs Y plot for severity of restrictions score vs economic activity and shows a trend line. Where the hell is correlation coefficient value? And the p-value? Just looking at that graph I can tell you that correlation for this will be crappy as well (both coefficient and p-value). The trend line is just smoke and mirrors. This is obfuscation 101 when someone posts a trend line but no numbers associated. Then of-course, there are numerous question like why certain countries in Europe were left out in the charts for this article etc. It requires a good journal paper level rigor, not this mess of an article. Seems to me that author had a theory in mind and simply massaged/ scrubbed the original source data to prove a point. The original data is here - https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker

We have crappy diets, resulting in first world problems like high blood pressure, diabetes etc. In rural China and rural Africa, people have 110/70 blood pressure well into their old age. In the western world, with processed foods and high salt and high sugar, we have extremely high blood pressure and diabetes rates very early in life. Our bodies don't fight off viruses as well as healthy people. My view is it's largely diet based. All, the above and second and third world countries also have reporting issues, plus the epidemic has not run its. course yet. There are reports of bad situations in Ecuador (Quito) and Brazil but numbers are hard to come by. While these and other points mentioned above are all valid, I highly doubt they can fully explain the huge discrepancy between the developed and the developing countries. And especially considering the big factor that should make situations worse in developing countries -- their lack of good healthcare systems. I do wonder whether how some countries do not care much about this virus and this is being reflected in recognizing/reporting the COVID death numbers. My wife's coworker (they work in healthcare), who has families in Bangladesh, told her yesterday that while COVID is spreading there, people are more worried about going hungry than the virus. Suppose your people, media, and government do not really recognize this virus as anything novel or serious... In such countries, even if people die due to COVID or related illness, they might not warrant much attention and won't be tracked like some doomsday counter. In that sense, is COVID another "first world problem"? Yup. I think this is on the money. Media coverage and fear mongering have made this what it is. Its now widely recognized that this was here much earlier than some people thought, and guess what? Life was totally normal and folks got on with their normal business and the economy was humming along just fine. So yes, its a shame we manufactured a horror story and certainly did impair parts of the economy, probably unnecessarily. But, in other news. Shanghai Disney tickets sold out. RCL is reporting normal booking volume for 2021, guess not everyone is living under a table in their basement. Huh? Maybe I am missing something but the denominator for per capita death rate is simply total population of a country (https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/). So China and India will show a small number for a very long time unless this is allowed to spread unhinged. I know there is general distrust of death numbers (numerator) attributable to covid but even if/when they start reporting "correct" ballpark numbers in the numerator, the denominator is what will skew and influence interpretation. This is really a very bad metric to compare countries on some kind of effectiveness or other theories on why deaths are higher because of the effect of denominator that has no direct relevance to deaths. It is simply an accident of population growth trajectory. Here it is without the denominator. I see a similar pattern, still. Now we just need to divide this by the approx # of days since first 1000 detected infections to get account for different starting points of large scale community spread. And why 1000? Because we need a reasonably large reported number to make sure community spread is well underway. We can pick another number here but has to be consistent across the board. For example Brazil will be early April, India will be starting in mid April whereas US will be in early to mid-Feb if I remember correctly. Slowly we are converging towards the correct way to look at things across countries using this metric. Even with this, it won't answer all questions. It will answer how quickly deaths rose (and plateaued) in each country post start of community spread. P.S: Just to be clear, this will be a lagging indicator in economic metric paralance.

We have crappy diets, resulting in first world problems like high blood pressure, diabetes etc. In rural China and rural Africa, people have 110/70 blood pressure well into their old age. In the western world, with processed foods and high salt and high sugar, we have extremely high blood pressure and diabetes rates very early in life. Our bodies don't fight off viruses as well as healthy people. My view is it's largely diet based. All, the above and second and third world countries also have reporting issues, plus the epidemic has not run its. course yet. There are reports of bad situations in Ecuador (Quito) and Brazil but numbers are hard to come by. While these and other points mentioned above are all valid, I highly doubt they can fully explain the huge discrepancy between the developed and the developing countries. And especially considering the big factor that should make situations worse in developing countries -- their lack of good healthcare systems. I do wonder whether how some countries do not care much about this virus and this is being reflected in recognizing/reporting the COVID death numbers. My wife's coworker (they work in healthcare), who has families in Bangladesh, told her yesterday that while COVID is spreading there, people are more worried about going hungry than the virus. Suppose your people, media, and government do not really recognize this virus as anything novel or serious... In such countries, even if people die due to COVID or related illness, they might not warrant much attention and won't be tracked like some doomsday counter. In that sense, is COVID another "first world problem"? Yup. I think this is on the money. Media coverage and fear mongering have made this what it is. Its now widely recognized that this was here much earlier than some people thought, and guess what? Life was totally normal and folks got on with their normal business and the economy was humming along just fine. So yes, its a shame we manufactured a horror story and certainly did impair parts of the economy, probably unnecessarily. But, in other news. Shanghai Disney tickets sold out. RCL is reporting normal booking volume for 2021, guess not everyone is living under a table in their basement. Huh? Maybe I am missing something but the denominator for per capita death rate is simply total population of a country (https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/). So China and India will show a small number for a very long time unless this is allowed to spread unhinged. I know there is general distrust of death numbers (numerator) attributable to covid but even if/when they start reporting "correct" ballpark numbers in the numerator, the denominator is what will skew and influence interpretation. This is really a very bad metric to compare countries on some kind of effectiveness or other theories on why deaths are higher because of the effect of denominator that has no direct relevance to deaths. It is simply an accident of population growth trajectory.

The market has provided ample opportunity for those who missed this in March to cash out with the S&P now where it was in October 2019. There should be no valid excuse for these people if they lose their shirts. https://www.wsj.com/articles/coronavirus-turmoil-free-trades-draw-newbies-into-stock-market-11588158001 “I feel like everything that I buy, I watch pretty closely and if it’s something that’s not doing well, I’ll generally try to put [that money] into something that is doing well instead,” https://www.bloomberg.com/news/articles/2020-04-29/firemen-and-romance-writers-faces-of-a-fierce-rebound-in-stocks “I’m a complete noob when it comes to stocks,” the mother of high school senior twin boys said while sheltering at home. “It’s not thousands and thousands of dollars that I invested, but it’s a start. We’ll see what happens. I hate to say it, but it’s like gambling, isn’t it?” More accounts were opened and dollars invested at E*Trade in the first quarter than in any prior full-year period, according to a company statement. The brokerage added 329,000 retail accounts and over $18 billion in net retail assets. Haha who would have thought that having everyone stay at home and enjoying free trades supports the stock market. Seems to me that we need everyone to stop working via a forced lockdown to get the SP500 to new heights. Apparently, the economy isn’t needed any more. What if some version of it is actually true? That is, stock market is sort of decoupled from main street as most of the stock ownership is primarily in the hands of top 20% in the income distribution. And these are the people least affected by the current events. The most affected areas are retail, restaurants, certain low wage factory jobs, and other low income service jobs requiring people to people interactions relative to other professions. And this will be true irrespective of lockdown or no lockdown because in each scenario, people are going to avoid interactions hence disproportionately affecting the same group of workers.

+1 to all the points above. Particularly #4 (it is called antibody dependent enhancement). I think people have no understanding of what kind of hurdles we may run into. Although as far as I know animal studies to date have not shown this effect for SAR-CoV2. I also think outbreaks that kill so many people will have negative psychological effect on everyone (even though the mortality will be highest in older people). It will be a drag for larger parts of the economy for some time to come (retail, hospitality, tourism and travel, restaurants, energy).

Remdesivir preliminary data from two of the many clinical trials going on - https://www.statnews.com/2020/04/29/gilead-says-critical-study-of-covid-19-drug-shows-patients-are-responding-to-treatment/ Some highlights - Study conducted by National Institute of Allergy and Infectious Diseases (head to head with placebo) - "The preliminary data showed that the time to recovery was 11 days on remdesivir compared to 15 days for placebo, a 31% decrease. The mortality rate for the remdesivir group was 8%, compared to 11.6% for the placebo group; that mortality difference was not statistically significant." Study conducted by China (head to head with placebo). Very similar findings to the above study - "In the China study, also published Wednesday in the Lancet, investigators found that remdesivir did not significantly improve the time to clinical improvement, mortality, or time to clearance of virus in patients with serious COVID-19 compared with placebo. There was a 23% improvement in time to clinical improvement for remdesivir compared to placebo, but the difference was not statistically significant. " And the stock market went up!

You seem to have left out one of the other principal reasons for flattening the curve in the first wave, which is simply to buy time. The hope is that we will have more tools available than simply hand washing and physical distancing, the same tools we we had hundreds of years ago. A good analogy is to think of it as putting a patient in to a medically induced coma briefly in hopes that treatment will improve in the interim. The number of interventions large and small that can be developed in three months is often also underestimated buy those who oppose flattening the first wave. Those are all fair points. I'm just not sure if the public is aware of this as the main reason. Perhaps the message would be too grim if they were told, "we are just buying time, hoping to find a cure...and it could take over a year". That is where good leadership shines. Look at Germany and what Merkel said when cases were exploding. SHe was very somber but firm. They also executed afterwards on a plan. And look at South Korea and Taiwan (in terms of communication and follow-up action). These countries did not have all the same prescriptions but they did follow-up effectively to ramp up testing/tracing and public policy. I think we fail to imagine that good and honest communication followed by effective action builds confidence in general population and is equally important. Sadly, US in not in the same boat yet.

Good find. Those graphs are telling. Definitely not "like the Flu". And the sudden spikes reveal that this virus was not "spreading for months" in these regions, but rather there was a sudden surge in cases. Many assume the denominator of CFR is underestimated, but the numerator is also underestimated in many measuring attempts (a lot of amateurs don't account in the delay from onset to death for example). This is another indicator we may be missing certain deaths from covid (eg. deaths at home). Hard to parse through these things, but excess mortality is definitely occurring for all causes. For one, when hospitals in hard hit areas are full of covid patients, other patients will experience worse healthcare...which can increase mortality in those groups. A lot of noise and uncertainty with a pandemic that looks an order of magnitude deadlier than the Flu. In my book, that means precaution is warranted. +1 That is the one of the important points of flattening the curve as much as possible. Because when hospitals are hit hard, the deaths due to other causes spike simply because lack of resources / fear of going to hospital, etc, etc. One can argue that even though these subjects did not get Covid, they died as a secondary effect of exponential rise in Covid infections. Other reasons of-course is to be able to ramp up testing, expand capacity for covid related supplies, develop plan and co-ordination etc when we start opening up. On another note, Denmark has decide to pay ~ 75% of every citizen's income upto a large sum until shutdown is slowly lifted. https://www.theatlantic.com/ideas/archive/2020/03/denmark-freezing-its-economy-should-us/608533/ https://www.forbes.com/sites/mortenjensen/2020/03/31/how-denmark-is-navigating-through-the-coronavirus/#346d6019fc7e

According to the latest WHO information there is no evidence getting infected with Covid will provide immunity to future infections. That's why they warned against "immunity passports". I agree, but they are gambling on this. If we can’t get immunity an vaccine very likely won’t work reliably either and we are in deep trouble with 2-3 million excess dead in the US alone after this has run its course. That is unless we found ways to reduce the IFR rate, which I think is somewhat likely, but by how much? I also think we are gambling on this without explicitly stating so. At least in parts of the US like NYC we probably reach herd immunity before any vaccine is coming to the market one way or another. I think people are getting confused again. There are multiple related but distinct things going on here. (a) Immunity is not strictly binary - there are grades of it for each person (strength of immune response - a quantitative measure). Some people elicit strong immune response, other have weak response resulting in poor immunity. (b) There is also a time dependency depending on the virus, for example flu needs new immunity every year, whereas measles immunity lasts lifetime. We do not know yet where SARS-CoV2 falls in terms strength or time component. © Having a positive antibody test without symptoms is even more complicated. If we assume the test is not a false positive (a lot of antibody tests have been shown to totally useless in terms of accuracy), then many of these tests give a binary answer. But from (a) we know that strength (quantitative measurement) is important. (d) Herd immunity is highly dependent on (a) being high in strength in general population and (b) immunity lasting a long time. We really do not know how this will play out in terms of biology. I think the economic opening should be planned accordingly with this uncertainty taken into account. There are multiple reasonable plans to do a roll out (for example - https://www.gatesnotes.com/media/assets/media/files/Pandemic-I-The-First-Modern-Pandemic.pdf), but it need co-ordination at the federal level with states following general strategy with some flexibility. States can have some leeway for sure, but it can't be the wild wild west it is right now.

I love the confidence! Even though you are so consistently proven wrong, you still post with gusto! What is the Infection Fatality Rate of the flu? Nobody actually believes it is 0.67%, do they? This comprehensive review shows ~10 deaths per 100,000 H1N1infections: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/ So comparing your bogus numbers for CV to these bogus numbers for H1N1, CV is 67 times more deadly than the flu! I love the stupidity! the 21% positive antibody test results on NYCers (3000 person sample) is the best DATUM we have on creating the correct denominator of the mortality per infection rate. what is your problem, Larkin? https://www.statnews.com/2018/09/26/cdc-us-flu-deaths-winter/ I agree that this is a meaningful study. It’s large enough and reasonably random. There might be a bias in just choosing people outside vs at home but it is likely not a strong one. 0.67% morbidity is ~7x deadlier than the flu though. The flu kills between 10-50k annually and infects ~30M (roughly ) so thats in the 0.1% ballpark. In addition, it’s much more infective. Why do we keep quoting an article from Sept 2018 with initial estimates when we have better numbers now on CDC website. https://www.cdc.gov/flu/about/burden/2017-2018.htm The 2017-2018 season was 61000 deaths with 45 million infections (NOT counting asymptomatic subjects) which puts the death rate at 61000 / 45000000 = 0.13%. Again, estimates suggest that # of asymptomatic flu patients are ~ 2.5-3 times that number which means the death rate is more close to 0.033 to 0.05%.

Okay, this is the last time I will correct you for a few weeks. The experts prediction of both mild/asymptomatic cases and IFR are accurate. Maybe it is your lack of expertise that is the issue (for example not knowing the difference between IFR and CFR)? The original model presented by the White House estimated 1.5M-2.2M deaths, if there was no mitigation. Guess how many deaths there would be assuming your 0.67% "mortality rate" and an attack rate of 70% (rough estimate of infection rate needed for herd immunity)? 328M * 0.7 * .0067 = 1.5M Seems pretty accurate to me. Maybe the experts know more about this pandemic than Cerzeca? Something doesn't seem right though, so help me understand. The same White House model projected that 100,000 and 240,000 would die with the mitigation measures in place. Are they speculating that there will be a vaccine available before the US reaches the attack rate? Another possible explanation -- If you flatten the curve, you might be able to avoid some deaths due to the healthcare system overflow, but I expect that the area under the curve would still be similar (compared to when you have a spike). So are they saying that they would save an order of magnitude of lives by keeping the health care system below the threshold? Part of my problem with these projections is that there is no transparency or explanation whatsoever... The reason for the discrepancy is that in the white house models and what we see is that in those models they assumed a 50% compliance with the shelter in place/whatever you may call them orders. What was observed is that the compliance rate is around 90%. So big miss on the assumption there. You basically have the numbers coming below the model because the quarantine is working way better because the people are behaving much, much better than assumed. They are taking it more seriously than the government assumed and that is making the quarantine much more effective. No, that doesn't address my question. I'm not questioning the discrepancy between the projection and actual data here (although that is also important). I'm curious how the experts came to project a much lower number of deaths with the mitigation in place. KCLarkin suggested that the projection of 1.5M-2.2M deaths was based on: 328M * 0.7 * .0067 = 1.5M assuming that people die until the US reaches the herd immunity, without any mitigation measures. But how is the number reduced to 100,000-240,000 with the mitigation measures? Does this mean we do not reach the herd immunity in this case? Then, why would the death number stop at 100,000-240,000? Are they banking on a potential cure or vaccine? If so, have they indicated this at any time? There are other factors that are modeled in mitigation other than compliance such as reasonable ramp up of testing and tracing to bring the overall R0 down during and after the shelter in place is slowly removed. Clearly experts were wrong on how dysfunctional the policy response will be even after 2 months of this. Those measures would slow down the infection rate, not the total number of deaths. Unless you get a vaccine or cure, the number of deaths would simply get stretched out over time. The area under the flattened curve can still the same as a sharp one. So the projections must have taken account some scenario that the virus would be mostly irradicated. Or not? None of the journalists ever asked this question?? Maybe the projections are time bound? What is the time period then? I think a lot of people should pay attention to other than Faux news and do a proper DD into what is actually being done and reported. These model (one by white house and other) have been very well reported. And they get updated as new data comes in - http://covid19.healthdata.org/united-states-of-america http://www.healthdata.org/covid/data-downloads https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf

Okay, this is the last time I will correct you for a few weeks. The experts prediction of both mild/asymptomatic cases and IFR are accurate. Maybe it is your lack of expertise that is the issue (for example not knowing the difference between IFR and CFR)? The original model presented by the White House estimated 1.5M-2.2M deaths, if there was no mitigation. Guess how many deaths there would be assuming your 0.67% "mortality rate" and an attack rate of 70% (rough estimate of infection rate needed for herd immunity)? 328M * 0.7 * .0067 = 1.5M Seems pretty accurate to me. Maybe the experts know more about this pandemic than Cerzeca? Something doesn't seem right though, so help me understand. The same White House model projected that 100,000 and 240,000 would die with the mitigation measures in place. Are they speculating that there will be a vaccine available before the US reaches the attack rate? Another possible explanation -- If you flatten the curve, you might be able to avoid some deaths due to the healthcare system overflow, but I expect that the area under the curve would still be similar (compared to when you have a spike). So are they saying that they would save an order of magnitude of lives by keeping the health care system below the threshold? Part of my problem with these projections is that there is no transparency or explanation whatsoever... The reason for the discrepancy is that in the white house models and what we see is that in those models they assumed a 50% compliance with the shelter in place/whatever you may call them orders. What was observed is that the compliance rate is around 90%. So big miss on the assumption there. You basically have the numbers coming below the model because the quarantine is working way better because the people are behaving much, much better than assumed. They are taking it more seriously than the government assumed and that is making the quarantine much more effective. No, that doesn't address my question. I'm not questioning the discrepancy between the projection and actual data here (although that is also important). I'm curious how the experts came to project a much lower number of deaths with the mitigation in place. KCLarkin suggested that the projection of 1.5M-2.2M deaths was based on: 328M * 0.7 * .0067 = 1.5M assuming that people die until the US reaches the herd immunity, without any mitigation measures. But how is the number reduced to 100,000-240,000 with the mitigation measures? Does this mean we do not reach the herd immunity in this case? Then, why would the death number stop at 100,000-240,000? Are they banking on a potential cure or vaccine? If so, have they indicated this at any time? There are other factors that are modeled in mitigation other than compliance such as reasonable ramp up of testing and tracing to bring the overall R0 down during and after the shelter in place is slowly removed. Clearly experts were wrong on how dysfunctional the policy response will be even after 2 months of this.

who needs to extrapolate to the rest of the country, for goodness sakes!!! apples to apples. NYC is the epicenter of the crisis, and because major media is NYC-centric, the mass hysteria was exported. why does the governor of michigan go stalinesque? because she wants to look like she is on top of things like cuomo. so of course this doesnt have to be extrapolated nationwide, because NYC's experience isn't the nation's experience. this 20% antibody positive rate makes covid less deadly than the flu. and if this result doesnt comport with how you want to think, then just call it a bad test. an inhale some more sand NYC: 11,267 deaths divided by 21% of 8,000,000 people=mortality rate of 0.67%. just like the flu. WRONG Reported flu mortality is 0.1% (https://www.cdc.gov/flu/about/burden/2018-2019.html) in 2018-2019 season (~34000 of 35 million died). This number does *NOT* take into account asymptomatic subjects. See my earlier post about it. UK flu study: Many are infected, few are sick https://www.cidrap.umn.edu/news-perspective/2014/03/uk-flu-study-many-are-infected-few-are-sick Original study in the Lancet: https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(14)70034-7/fulltext 75% of flu cases are asymptomatic, because only those who show symptoms are tested. Which means that if 10000 are reported to have flu after testing and 0.1% of them die (10 patients / 10000), when asymptomatic subjects are taken into account the denominator is closer to 40000 implying deaths of 10 / 40000 = 0.025%. Last time I checked 0.67 / 0.025 = 26X.

Yeah, models aren't perfect, but I don't really see an alternative. Is there one? Make decisions based on the most pertinent observations? E.g., the fact that COVID-19 has significantly higher death rates in older people. In general, scientists should inform decision-makers primarily based on observations/experiments, not prediction models. People are getting delusional regarding these models... thinking that they can accurately predict the future if the models are built by scientists. The only thing that is delusional is to believe that people who have spent all their lives in this area (doctors in the area of infectious diseases, diagnostic, vaccine and drug development experts, and yes, epidemiologist) make decisions solely based on models and not update their hypothesis based on hard data on the ground.

While we are talking about how this virus is a curveball and we keep learning new things, here is another one - Alarmed as COVID patients' blood thickened, New York doctors try new treatments https://www.reuters.com/article/us-health-coronavirus-usa-blood/alarmed-as-covid-patients-blood-thickened-new-york-doctors-try-new-treatments-idUSKCN22421Z In many subjects showing up in hospitals, no typical symptoms of covid-19 but stroke or kidney complications. "At Mount Sinai, nephrologists noticed kidney dialysis catheters getting plugged with clots. Pulmonologists monitoring COVID-19 patients on mechanical ventilators could see portions of lungs were oddly bloodless. Neurosurgeons confronted a surge in their usual caseload of strokes due to blood clots, the age of victims skewing younger, with at least half testing positive for the virus." “I’ve never seen any other viruses causing that,” Jabbour said.

https://www.nytimes.com/2020/04/19/us/coronavirus-antibody-tests.html Testing for serological tests for antibodies is one of the hardest things to get right, especially with now touted finger prick (think Theranos). These are "analog" tests (as opposed to digital PCR tests for testing viral RNA to determine positive for SAR-Cov2). I have worked in this space for 15 years now and it is pretty clear that most people do not realize how difficult protein measurements are as opposed to DNA/RNA measurements. If not done right, they will have unacceptable false positive and negative rates. The article above clearly points to that risk. Given that at a lot of testing kits and materials in US are coming from China and not being vetted, I can only imagine what kind of misinformation about antibodies is being given out. Quotes - "The Food and Drug Administration has allowed about 90 companies, many based in China, to sell tests that have not gotten government vetting, saying the pandemic warrants an urgent response. But the agency has since warned that some of those businesses are making false claims about their products; health officials, like their counterparts overseas, have found others deeply flawed." "For example, Britain recently said the millions of rapid tests it had ordered from China were not sensitive enough to detect antibodies except in people who were severely ill. In Spain, the testing push turned into a fiasco last month after the initial batch of kits it received had an accuracy of 30 percent, rather than the advertised 80 percent. In Italy, local officials have begun testing even before national authorities have validated the tests."

Thanks for clarifying. Since you mention expertise on an internet forum, I assume you are a bio-statistician and run/consult for these kinds of trials? First, my apologies, I was not trying to be confrontational, in-fact was trying supportive of what you were trying to explain and add to it. But it came out very different. Full disclosure, we consult on clinical trials and other aspects of pharma/biotech business but not in the capacity of card carrying clinical design statistician. But sitting in some of the meetings where they discuss the trial design really helps me understand the complexity, limited information and very aggressive deadlines people in this field have to work with. Hence my deference to them. They surely get it wrong sometimes but their batting average will put anyone else to shame if they tried to do it a few times without the expertise.

Cherzeca, its an important question, but this already happens with other life threatening illnesses. I don't think its unethical to give a placebo if you *don't know* that the drug works. If it doesn't work then both people getting the drug and getting the placebo are going to have statistically similar outcomes. Thats the point of the trial: to see *if it works*. It would be unethical to charge for a drug which doesn't work, or has bad side-effects/interactions. Thats what doctors did for centuries, and it has only recently improved. Lets keep the improvement. Empirical evidence is very important, probably more than theory, as proved by the story of the doctor who advocated hand washing and ended up in a mental asylum since no one would believe him. https://www.npr.org/sections/health-shots/2015/01/12/375663920/the-doctor-who-championed-hand-washing-and-saved-women-s-lives Other have considered the ethics, and the current scenario is the first place where they say a placebo trial is justified. From: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844122/ Ethical analysis and international ethical guidance permit the use of placebo controls in randomized trials when scientifically indicated in four cases: (1) when there is no proven effective treatment for the condition under study; This is why not having expertise is so not helpful. Control group is not necessarily placebos. People have figured out trial designs in difficult diseases (e.g. cancer) decades ago. In broad strokes, the control is frequently standard of care, which is not placebo in these cases but whatever is the standard of care in that field at that point in time. For covid, they will not stop current treatment (using whatever they are trying to currently use). They will keep all the underlying prescribed treatments exactly the same between treated vs control group *except* one variable, the new drug that is being tested being the only treatment factor that is different. Also has to be randomized (for subjects) and double blinded because, you know, doctors and drug companies also have biases. And surprise, there are cowboy doctors who do not know much about trial design (it is a specialty field which also needs expertise) but nevertheless make big claims with flawed trial design data (the french doctor who published paper on hydroxychloroquine). P.S: Standard of care doesn't mean some drug, it can be devices (e.g. ventilator) or palliative care in the absence of no proven treatment options.

This is interesting. Netherlands used policies more or less intermediate compared to places like Austria on one side and Italy on the other with, as expected, more or less intermediate results. Netherlands has also produced interesting work on influenza vaccine effectiveness and is a good relative European student in terms of historical flu vaccinate rates. It looks like (picture developing) that the CV does behave (intrinsic features) similarly to the influenza virus, with the main difference being that the population tends to have a much lower natural immunity to it and there is no vaccine, not even a partially effective one. Reasonable extrapolation of data in the Netherlands suggests that the eventual death rate from CV (with some social distancing and other basic measures) will look like (compare to a reasonable degree) the typical death rate for influenza, had there been a 0% rate of vaccination. What society is doing is basically trying to adapt (with various levels of 'success') to this new and evolving reality. @LC Thank you for supplying the link for the European monitoring of mortality with seasonal variations. Apologies. To my own embarassment upon re-reading this I made a primary school calculation error here (my only defence: it was early). 3100 out of 500 000 obviously isn't 0.06%, but 0.6%, which makes quite a bit of difference here, Still not quite the 3% some are saying, but definitely not flu percentages either. https://www.thelancet.com/action/showPdf?pii=S1473-3099%2820%2930243-7 See Table 1. Estimated CFR 1.38%, >10x deadlier than the Flu. The best CFR from published studies is in the vicinity of 1% which is 10x deadlier than Flu. The 3% antibodies is not good news for feasibility of herd immunity as you note. British government backed off of that strategy quickly... "However, after further adjusting for demography and under-ascertainment..." what does this mean? oh, that the so-called scientists fudged their data to get the results they wanted, is what this means. garbage in, garbage out. good enough for Dalal though. Dalal, first of all from Lancet study the number to be used is " Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age." Their case fatality ratio is 1.38 & Infection fatality ratio is 0.66 indicates they are assuming 1.38/0.66 =2 infected for each case that came to testing level. However all the four studies now published in last two days (NYC pregnant women, Boston homeless, China asymptomatic study and Netherlands blood donors) indicates that it is more than 1 asymptomatic for each symptomatic. The 1.38 case fatality ratio has to be adjusted for the new asymptomatic vs symptomatic ratio. That is fine. But seems like comparing this to flu numbers to confirm pre-existing biases is quite prevalent. So in order to keep comparisons apples to apples we should be adjusting flu numbers as well for the asymptomatic vs symptomatic ratio. The reported flu numbers do not do that. This will reduce corresponding flu numbers significantly. See below UK flu study: Many are infected, few are sick https://www.cidrap.umn.edu/news-perspective/2014/03/uk-flu-study-many-are-infected-few-are-sick Original study in the Lancet: https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(14)70034-7/fulltext

It's an interesting article, making a pretty compelling case for reopening the world except for large gatherings. I'm curious what an epidemiologist would think of it. No its not. It relies on the first and second points at the end of the article heavily for it to work. 1. TEST the population extensively to isolate asymptomatic carriers. 2. TRACE contacts and maintain quarantine for those who have tested positive. None of these were done anywhere properly except in Singapore, Hong Kong and to some extent in South Korea, as the article states. Then the article goes on to say that "better late than never" and proposes to open up the society simultaneously with scaling up (1) & (2). The problem is that with epidemics is all about outpacing the virus and what works in the beginning stop working later until we get ahead of the virus. Since (1) and (2) were not done earlier in US (utter failures), one has to slow down the spread of virus to buy time for testing and tracing to catch-up the otherwise exponential spread. Only when the testing and tracing has been established to be efficient, we can slowly open up. Nobody is arguing that we should not open up, but it would be foolish to do it when we cannot outpace the virus spread with test and trace. We are nowhere close.

Always beware of self-congratulatory praise but policy action in the right direction can make a significant difference: https://www.theportugalnews.com/news/portugal-quicker-to-take-action-than-italy-spain-and-uk/53552 However, urban population rate is somewhat higher in Spain and it appears (looking at evolving data, SARS and others) that the density of international traffic (travel and trade) matters. The % of direct trade with China is higher in Spain. What many people in this forum and in other places forget is that for a complex system (social, economic and biology) there will always be many contributing factors that are in many cases closely intertwined. The above example and COVID-19 related deaths due to other pre-existing conditions are good examples. That should not make us interpret that some of these measures being prescribed by experts (such as imposing shelter-in-place early) are not effective. In-fact, by imposing shelter-in-place early on in the pandemic affects other variables described above in a positive way. It becomes less likely that international travelers will go to a region where there is such shelter imposed, trade to that regions will go down as well, etc. So the argument of what is the *real* cause and effect is not always helpful, there will be many. The goal should be to come up with as short a list of policy prescriptions as possible that will address most of these factors.