14 ms·

Sure all of these studies are potentially flawed but they're all generally pointing in the same direction. There are many more infections than we know about and

Sure all of these studies are potentially flawed but they're all generally pointing in the same direction. There are many more infections than we know about and the CFR is not anywhere close to the actual IFR. NYC is an outlier with a 21% infection estimate but for the rest of NY (outside NYC metro/Westchester/LI) the estimate is 3.6%. Santa Clara estimate was 3%. LA County estimate was 4%. Seems like a trend is developing.

slg 6y ago
>There are many more infections than we know about and the CFR is not anywhere close to the actual IFR. That is the optimistic takeaway. The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work.
- arcticbull 6y ago
  > The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work. The third option is, when you take into account that it's approximately as bad as the flu for folks under 40, we let out the young and keep the older folks and the vulnerable inside. This will boost our progress towards herd immunity without materially increasing the death counts.
  - slg 6y ago
    I have never heard of any healthy person under 40 dying from purely the flu. COVID-19 is certainly less dangerous to the young than the old, but there are plenty examples of it killing young and otherwise healthy people.
    - TheBlight 6y ago
      See: https://www.cdc.gov/flu/about/burden/2018-2019.html https://www.cdc.gov/flu/about/burden/2018-2019.html Search for "Table 1: Estimated influenza disease burden, by age group — United States, 2018-2019 influenza season"
      - slg 6y ago
        I skimmed through that so please point it out if I missed it, but the numbers don't seem to account for comorbidities. The question was whether COVID-19 is more dangerous than the flu to healthy young people not all young people.
        mlyle 6y ago
        Just because you've not heard about it, doesn't mean it doesn't happen. Seemingly healthy young people die from the flu each year. Since Feb 1, 204 people from 15-34 have been confirmed to have died from COVID-19 in the US. 162 people from age 15-34 have been confirmed to have died from influenza in the same period. I don't think you'll find either statistic broken down by comorbidities.
        slg 6y ago
        You mind sharing the source on those numbers because the total deaths is somewhat meaningless without the number of infections which I imagine is much higher for the flu? Even those absolute numbers show total deaths being just over 25% higher for COVID-19 than the flu.
        mlyle 6y ago
        Table 2: https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm The statement you differed with is "The third option is, when you take into account that it's approximately as bad as the flu for folks under 40," You're ignoring data in order to make a wishy-washy statement that it must be much worse for people under 40 based on anecdotes you hear. I don't agree flu infections were higher for those 2 months, either. Distancing has been spectacularly effective against influenza, since it has a lower Rt in the absence of controls. Under 1% of influenza surveillance tests are positive right now, which is a level usually only seen in the middle of summer. Our best guess for overall infection fatality rate is about 0.3%, double or 3x influenza (because of the very high death rate in the elderly), but COVID-19 deaths overall have been 4x influenza in that time period, indicating that COVID-19 prevalence is higher.
  - qqqwerty 6y ago
    You can't have it both ways. If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work. The fact is, the number of deaths is too high in NYC to be able to cherry-pick your way to an argument that supports your view. Either R0 is really high, and we need to shutdown to prevent it from infecting the entire population in a very short time span, or the IFR is way higher than the flu and we need to shutdown to prevent it from killing a lot of people. The only sane way to open things back up at this point is to implement widespread testing and contract tracing.
    - deleted 6y ago
      [deleted]
    - mlyle 6y ago
      > If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work. No. If you miss a constant percentage of cases, you get the same shape of exponential curve. That is, a virus with an R0 of 2 and 1 case doubling to 2 and 2 cases doubling to 4.... looks the same when you miss 99% of infections and see 1 of 100 infections doubling to 2 of 200 doubling to 4 of 400. This fallacy has been common in the response to this data, but it makes no sense. Large numbers of missed cases shift the curve forward and backward in time, and don't change the shape of it. All of the current findings are still consistent with R0 in the range of 2.0 to 2.5.
      - qqqwerty 6y ago
        > If you miss a constant percentage of cases We are talking about 1 serological study at a single point in time. We have no idea what percentage of cases we were missing before or after that point.
        mlyle 6y ago
        There's no reason to interpret the serological study to think a higher R0 is implied, which is what you said. The only way that missing cases affects estimates of R0 from time series is if we're missing a much bigger proportion of cases now than we were in the past. All the evidence I've seen leans the other way-- more testing, better testing, more cases diagnosed by "presumed" criteria. So, it actually implies the opposite of what you're saying, and argues for lower R0.
- CubsFan1060 6y ago
  I don't think this a binary outcome. Getting closer to herd immunity also makes things better. Take NYC. Now 1 out of 5 people you see can't infect you (assuming having antibodies means immunity). The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%.
  - badfrog 6y ago
    > The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%. It depends on how long the immunity lasts. If it's permanent, this is indeed great. If it lasts only a few months, this 21% won't make any difference for another wave of infections this fall.
    - __blockcipher__ 6y ago
      We use the term "immunity" but we should remember that there is a difference between the presence of active antibodies and the presence of immune "memory cells". The latter hang around long-term, even if the former disappear. So at a minimum if one does develop infection they will recover far sooner and with better outcomes. And likely will reach a far lower peak viral load which might bring down transmission.
      - maxerickson 6y ago
        That is not well established. It's likely enough, but we don't gain long term immunity to every virus that we successfully clear.
        __blockcipher__ 6y ago
        It is well established. https://en.wikipedia.org/wiki/Memory_T_cell https://en.wikipedia.org/wiki/Memory_T_cell You are conflating active circulating antibodies with the memory T cells. Which is literally what my entire comment was about.
        6y ago
- __blockcipher__ 6y ago
  At this point people advocating the position you’re advocating for are in a state of denial (this is my opinion, not a matter of fact, obviously). Your assumption is that we can effectively prevent the majority of the nation from exposure via lockdown. Not only does evidence seem to point against that, but when you do the math on mortality due to suicide and overdose it’s not clear that containment would even save more lives in the long run. Here’s how you can tell people’s philosophical positions: if they talk about fear of a “second wave” they are Containers, since that implies the initial “wave” will not infect the majority; ie the virus is successfully contained (EDIT: See https://news.ycombinator.com/item?id=22961927 https://news.ycombinator.com/item?id=22961927 for the caveat here). Ironically, leaders like Fauci are verbally saying that containment is not the strategy, yet every word that he says and the IMHE model everyone is relying on are all the result of a Containment ideology. The alternative is what I would call Pareto mitigation. The vulnerable portion of the population self isolates, while the rest of us are _allowed_ to resume working and living more or less normally (still no large gatherings presumably). I'd like to take this moment to put out a brief PSA that the serological data coming out, while not 100% reliable, is all telling more or less the same story. Let's look at these IFRs (the second link is CFRs but for Italian healthcare workers who presumably are all getting tested so I'm treating it as a de-facto IFR): https://old.reddit.com/r/COVID19/comments/g4tqvk/dutch_antibody_study_of_blood_donors_reveals_3/fnzkn4k/ https://old.reddit.com/r/COVID19/comments/g4tqvk/dutch_antib... https://old.reddit.com/r/COVID19/comments/g6nmtf/update_on_italian_progression_of_covid19/foatvgv/ https://old.reddit.com/r/COVID19/comments/g6nmtf/update_on_i... (I'm linking to the reddit comments instead of the actual study because they're really nice tables and the links are still there for anyone who wants to double check) As others have said, for those around age 45 or less, Covid is equally or less dangerous than Influenza. And particular for those under 30 the flu is an order of magnitude more deadly at least. In the general population overall, Covid is undeniably more deadly than the flu, but only about 3-5x (and I think 3x personally right now). Recall that the flu is characterized by deaths in the very young and very old, while being less harmful to those "in between", purportedly due to the "cytokine storm" which is a scorched earth reaction of the immune system. Covid is very different, it is extraordinarily deadly to the very old, extraordinarily non-deadly to the very young, and about the same as the flu to those in between. A disease with such a "spiky" (highly variable) mortality rate based on your risk factors is precisely the kind of disease that is most effectively treated with risk-informed self quarantine rather than a national lockdown. Unemployment is correlated with a 2-3x higher chance of suicide, of which perhaps half can be explained away by mental health confounds [1]. There's unique factors in play here - rampant social isolation and widespead fearmongering, propagated even by health experts and "trusted" news sources at times - that lead me to believe that the spike in suicide and overdoses will actually be much higher than predicted by just unemployment alone. We're currently at 50,000 suicides per year in the US as a base rate, it is not unimaginable that we would see at least 50,000 _extra_ suicide deaths attributable to a mixture of lockdown and the general socioemotional environment. -- I haven't even gotten to the philosophical battle of "freedom versus security". I am, ideologically, someone who drank the koolaid and really believes in freedom and civil liberties over "security" (which I view as illusory anyway), but _even just viewed through the lens of reducing mortality_, the evidence is stacking up that lockdown is going to do more harm than good. Is the evidence fully settled? Of course not. But it's shocking to me how many people seem to be operating off of the projected CFR's we had in early February, shouting from the rooftops about "1 in 20" people dying (random recent case in point: https://news.ycombinator.com/reply?id=22952764&goto=threads%3Fid%3D__blockcipher__%2322952764 https://news.ycombinator.com/reply?id=22952764&goto=threads%...). I don't know whether it's just that a large swath of the population already had clinical anxiety which is further magnified by social isolation and social media and news headlines, or whether something else is at play, but I'm very concerned about the state of discourse in the United States right now, and more broadly, the entire world. In fact, ironically I feel a bit luckier to be in the US than some of these other countries because in the US _every_ issue is partisan, which while entirely irrational means that roughly half the country will be in favor of ending the lockdown at any given time (the position I am advocating for, within reason, insofar as hospitals are not overwhelmed), as opposed to other places where you can get given a $1600 ticket for driving a car by yourself, based off of a superstition that _being outside_ causes Covid as opposed to exposure to infected respiratory droplets... -- EDIT: Lastly I should mention that in a perfect world we could have voluntary variolation; I would love to be able to expose myself to a controlled dose of SARS-CoV-2 and self isolate for several weeks to ensure that I can never pass on Covid to someone else. Unfortunately that would be very hard to make a reality due to the political environment, even though I am advocating for it to be totally voluntary. I was heartened to see this recent paper toying with a variant of that approach: https://www.medrxiv.org/content/10.1101/2020.04.12.20062687v1 https://www.medrxiv.org/content/10.1101/2020.04.12.20062687v... (I don't agree with an "Immunity Card" for ideological reasons but I'm glad we have a paper attempting to model it out which does show benefit of voluntary self exposure) [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/pdf/v057p00594.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/pdf/v05...
jnbiche 6y ago
The CFR will be much higher than the IFR. For example, approximate flu IFR is 0.05%.
- Scipio_Afri 6y ago
  Exactly, CFR is always expected to be higher than the IFR. Most people getting sick don't go to the doctor. The CFR is an indication of how many people feel they need to go to the doctor and get treatment due to how severe the illness is. And of those that seek treatment how many die.
- scythe 6y ago
  In comparing the COVID-19 IFR to the flu IFR, it is important to remember that flu vaccines are widely available and limit the spread of influenza. For example, CDC retrospectives for 2018-9 estimate that 35M Americans[1] got influenza over the flu season, or less than 12%. By contrast, the current COVID-19 infection rate in New York (from these data) is already higher than 12%. So COVID-19 has the potential to infect a larger proportion of the population than the flu usually does. (If the CDC data is correct, the flu shot may save around a hundred thousand lives per year. Don't skip it!) 1: https://www.cdc.gov/flu/about/burden/2018-2019.html https://www.cdc.gov/flu/about/burden/2018-2019.html
  - __blockcipher__ 6y ago
    That point is entirely true but doesn't really change the policy implications unless you believe that we can actually contain this thing indefinitely (i.e. that a given individual can avoid being exposed to it indefinitely)
  - themgt 6y ago
    It's actually important to point out, the CDC is very explicit that they are estimating flu illnesses, in their own words "an estimated 34 million people had symptomatic influenza illness" Many people are attempting to extrapolate asymptomatic COVID to estimate an infection fatality rate and then making comparisons to CDC flu data for symptomatic illness, which is completely wrong. Actual flu sero studies suggest a large fraction of the population gets flu every year[1] and IFR is more like 0.02->0.05%, so even taking NYC numbers at face value COVID looks 10-50x more fatal than flu. [1] https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002082 https://journals.plos.org/plosbiology/article?id=10.1371/jou...
Karrot_Kream 6y ago
> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction That's unfortunately not the way statistics works. Combining multiple bad tests just makes the results incorrect or highly uncertain; the devil often is in the details. The Stanford/Santa Clara County study is a good example of how the details can really undermine a study. Things do indeed seem to be converging to the idea that the CFR is not near the IFR, but none of this is new news, and the IFR remains very close to what most epidemiological predictions indicated early last month. If anything, it confirms that COVID is a difficult beast to tame.
- TheBlight 6y ago
  We don't have perfect studies and perfect data but calling these tests "bad" seems slightly unfair. They give us an imperfect but useful snapshot of what's going on. But thanks for the condescension. I'd expect nothing less on HN.
  - CydeWeys 6y ago
    The study is dramatically over-sampling exactly the people who have the most potential exposure to COVID-19, and dramatically over-sampling the people who don't. This could easily be way off. It's not testing the people who aren't home at all, and it has a low chance of only testing the people who leave home rarely, only when strictly necessary. It's mainly finding the people who leave home a lot.
    - TheBlight 6y ago
      How do you know how often these people are leaving their homes? Everyone has to leave their home to go to the grocery store eventually or someone in their family does. Yes there are delivery services but the vast majority of people aren't using those.
      - kgwgk 6y ago
        The more often they go, the more likely you encounter them.
monadic2 6y ago
> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction. How on earth could you differentiate this from the sample bias?
- TheBlight 6y ago
  All three studies created their sample groups differently. Are all three methods flawed in some way? Sure. Is it more reasonable to completely ignore the picture all three are painting or to consider there might be something to the trend?
  - zamfi 6y ago
    > Are all three methods flawed in some way? Sure. They're not just all flawed in some way -- along at least one dimension they are flawed in the same way, that is, they all have a biased sample of the population that seems more likely to be infected than a true random sample. So, sure, they're "created differently", i.e., they're not all facebook ads, or grocery stores, but all those methods bias towards higher infection rates, don't they? Given that, you'd expect high precision, low accuracy -- these tests all paint a trend for sure, but we don't know how close that trend is to reality.
    - timr 6y ago
      These studies have completely different methodologies, and the only way you can plausibly call them similar is by arguing that anyone willing to get serological testing is substantially more likely to be infected than those who are not. That's little more than a fancy way of disregarding any testing result you don't happen to like. We've been systematically biasing toward testing the old and infected for months now. Why should rates derived from PCR testing with known severity bias considered a strong prior, yet rates derived from serological surveys with hypothetical severity bias be disregarded for the same reasons? At this point, we have many different studies from different parts of the world -- each with its own methodological flaws and biases -- and yet all are pointing in the same direction: a systematic under-count of cases, and an IFR somewhere between 0.5% and 1%, with a bias toward the lower value.
wtvanhest 6y ago
"Preliminary test results suggest 21% of NYC residents have Covid antibodies" Should be re-written as: "Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies" All of these tests suffer the same sample bias, and that sample bias is massive. People who are already in a grocery store (risky behavior), who are willing to give a blood sample (believes they may have been previously exposed) are not the same as people who are leaving their house only for very limited purposes and ordering food online. If we assume that the % of people shopping in grocery stores and willing to take the test are in risk group A, and the people not shopping are in risk group B. We can run the following quick analysis. First, assume risk group A is 5 times as likely to have had COVID than Risk group B (but plug any assumption in there necessary) Then assume that risk group A is only 10% of the population, then rerun the numbers as follows: For every 100 people in risk group A who tested, 21 were positive. Risk group A is 100 people, and risk group B is 900. Risk group A's 100 were 21 prior positive. Risk group B's 900 could be estimated that 4.2 people per 100 were positive, so in total of the 900 people 37.8 were positive. 21+37.8 = 59 people of 1000, or 5.8%. Plug any numbers you want in the analysis, but the assumptions drive huge variability in the % of New Yorkers infected. Without a less biased sample, we really don't know much other than that way less than 21% of New Yorkers have anti-bodies.
- walterbell 6y ago
  > Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies Would be useful to know how many deaths from the same zip codes are people who visited crowded grocery stores.
- mannykannot 6y ago
  NYC is not suburbia. I believe a higher percentage are still working outside of their homes (and, in many cases, that also means travelling on public transport), and a smaller percentage have the financial and transport resources to significantly reduce the frequency of their shopping trips. Apartment living means a greater exposure to your neighbors whenever you do enter or exit. The sampling in NYC, while far from ideal, may therefore be better than the other regions, in that shopping behaviour may not be as dominant a risk factor as you suppose. For the same reasons, plus the likelehood that the virus arrived in NYC early and frequently, NYC's figures cannot be extrapolated to the rest of the state or country. I would like to see how these figures match up to the sewage-sampling method of estimation.
ummonk 6y ago
Since false positives can be low single digit percentages, anything outside the NYC data is pretty meaningless, and a bunch of meaningless data is still meaningless.
jonahbenton 6y ago
Death counts in NYC, combined with a 1% CFR, indicate that the number of infected was ~1.5M 3 weeks ago; probably 2.2M now. That's about 25%.
kurthr 6y ago
Nope, because they're all designed to skew higher. People who are out shopping, people who will go out of their way on a weekend to be tested, healthcare workers... all of them are likely skewed high. One might as well conclude (with as much evidence) that the purpose of the tests is to show higher infection rates. Still, nobody doubts that we're under testing by a factor of 3-5x to catch a majority of cases. What's disturbing is that if you take the estimated statewide data of 2.7M infected (out of ~20M) at face value and only the current 21k dead you still get 0.7-0.8% fatality rate.
- kurthr 6y ago
  In support of my statement, it is now noted that the lead professor's wife sent out misleading emails to find people who wanted to return to work, if they tested positive. The professor says he knew nothing of it and because they are wealthy they would skew low rather than high. BuzzFeed News: A Stanford Professor’s Wife Recruited People For His Coronavirus Study By Claiming It Would Reveal If They Could “Return To Work Without Fear”. https://www.buzzfeednews.com/article/stephaniemlee/stanford-coronavirus-study-bhattacharya-email https://www.buzzfeednews.com/article/stephaniemlee/stanford-...

slg 6y ago

>There are many more infections than we know about and the CFR is not anywhere close to the actual IFR. That is the optimistic takeaway. The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work.

arcticbull 6y ago
> The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work. The third option is, when you take into account that it's approximately as bad as the flu for folks under 40, we let out the young and keep the older folks and the vulnerable inside. This will boost our progress towards herd immunity without materially increasing the death counts.
- slg 6y ago
  I have never heard of any healthy person under 40 dying from purely the flu. COVID-19 is certainly less dangerous to the young than the old, but there are plenty examples of it killing young and otherwise healthy people.
  - TheBlight 6y ago
    See: https://www.cdc.gov/flu/about/burden/2018-2019.html https://www.cdc.gov/flu/about/burden/2018-2019.html Search for "Table 1: Estimated influenza disease burden, by age group — United States, 2018-2019 influenza season"
    - slg 6y ago
      I skimmed through that so please point it out if I missed it, but the numbers don't seem to account for comorbidities. The question was whether COVID-19 is more dangerous than the flu to healthy young people not all young people.
      - mlyle 6y ago
        Just because you've not heard about it, doesn't mean it doesn't happen. Seemingly healthy young people die from the flu each year. Since Feb 1, 204 people from 15-34 have been confirmed to have died from COVID-19 in the US. 162 people from age 15-34 have been confirmed to have died from influenza in the same period. I don't think you'll find either statistic broken down by comorbidities.
        slg 6y ago
        You mind sharing the source on those numbers because the total deaths is somewhat meaningless without the number of infections which I imagine is much higher for the flu? Even those absolute numbers show total deaths being just over 25% higher for COVID-19 than the flu.
        mlyle 6y ago
        Table 2: https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm The statement you differed with is "The third option is, when you take into account that it's approximately as bad as the flu for folks under 40," You're ignoring data in order to make a wishy-washy statement that it must be much worse for people under 40 based on anecdotes you hear. I don't agree flu infections were higher for those 2 months, either. Distancing has been spectacularly effective against influenza, since it has a lower Rt in the absence of controls. Under 1% of influenza surveillance tests are positive right now, which is a level usually only seen in the middle of summer. Our best guess for overall infection fatality rate is about 0.3%, double or 3x influenza (because of the very high death rate in the elderly), but COVID-19 deaths overall have been 4x influenza in that time period, indicating that COVID-19 prevalence is higher.
- qqqwerty 6y ago
  You can't have it both ways. If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work. The fact is, the number of deaths is too high in NYC to be able to cherry-pick your way to an argument that supports your view. Either R0 is really high, and we need to shutdown to prevent it from infecting the entire population in a very short time span, or the IFR is way higher than the flu and we need to shutdown to prevent it from killing a lot of people. The only sane way to open things back up at this point is to implement widespread testing and contract tracing.
  - deleted 6y ago
    [deleted]
  - mlyle 6y ago
    > If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work. No. If you miss a constant percentage of cases, you get the same shape of exponential curve. That is, a virus with an R0 of 2 and 1 case doubling to 2 and 2 cases doubling to 4.... looks the same when you miss 99% of infections and see 1 of 100 infections doubling to 2 of 200 doubling to 4 of 400. This fallacy has been common in the response to this data, but it makes no sense. Large numbers of missed cases shift the curve forward and backward in time, and don't change the shape of it. All of the current findings are still consistent with R0 in the range of 2.0 to 2.5.
    - qqqwerty 6y ago
      > If you miss a constant percentage of cases We are talking about 1 serological study at a single point in time. We have no idea what percentage of cases we were missing before or after that point.
      - mlyle 6y ago
        There's no reason to interpret the serological study to think a higher R0 is implied, which is what you said. The only way that missing cases affects estimates of R0 from time series is if we're missing a much bigger proportion of cases now than we were in the past. All the evidence I've seen leans the other way-- more testing, better testing, more cases diagnosed by "presumed" criteria. So, it actually implies the opposite of what you're saying, and argues for lower R0.
CubsFan1060 6y ago
I don't think this a binary outcome. Getting closer to herd immunity also makes things better. Take NYC. Now 1 out of 5 people you see can't infect you (assuming having antibodies means immunity). The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%.
- badfrog 6y ago
  > The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%. It depends on how long the immunity lasts. If it's permanent, this is indeed great. If it lasts only a few months, this 21% won't make any difference for another wave of infections this fall.
  - __blockcipher__ 6y ago
    We use the term "immunity" but we should remember that there is a difference between the presence of active antibodies and the presence of immune "memory cells". The latter hang around long-term, even if the former disappear. So at a minimum if one does develop infection they will recover far sooner and with better outcomes. And likely will reach a far lower peak viral load which might bring down transmission.
    - maxerickson 6y ago
      That is not well established. It's likely enough, but we don't gain long term immunity to every virus that we successfully clear.
      - __blockcipher__ 6y ago
        It is well established. https://en.wikipedia.org/wiki/Memory_T_cell https://en.wikipedia.org/wiki/Memory_T_cell You are conflating active circulating antibodies with the memory T cells. Which is literally what my entire comment was about.
        6y ago
__blockcipher__ 6y ago
At this point people advocating the position you’re advocating for are in a state of denial (this is my opinion, not a matter of fact, obviously). Your assumption is that we can effectively prevent the majority of the nation from exposure via lockdown. Not only does evidence seem to point against that, but when you do the math on mortality due to suicide and overdose it’s not clear that containment would even save more lives in the long run. Here’s how you can tell people’s philosophical positions: if they talk about fear of a “second wave” they are Containers, since that implies the initial “wave” will not infect the majority; ie the virus is successfully contained (EDIT: See https://news.ycombinator.com/item?id=22961927 https://news.ycombinator.com/item?id=22961927 for the caveat here). Ironically, leaders like Fauci are verbally saying that containment is not the strategy, yet every word that he says and the IMHE model everyone is relying on are all the result of a Containment ideology. The alternative is what I would call Pareto mitigation. The vulnerable portion of the population self isolates, while the rest of us are _allowed_ to resume working and living more or less normally (still no large gatherings presumably). I'd like to take this moment to put out a brief PSA that the serological data coming out, while not 100% reliable, is all telling more or less the same story. Let's look at these IFRs (the second link is CFRs but for Italian healthcare workers who presumably are all getting tested so I'm treating it as a de-facto IFR): https://old.reddit.com/r/COVID19/comments/g4tqvk/dutch_antibody_study_of_blood_donors_reveals_3/fnzkn4k/ https://old.reddit.com/r/COVID19/comments/g4tqvk/dutch_antib... https://old.reddit.com/r/COVID19/comments/g6nmtf/update_on_italian_progression_of_covid19/foatvgv/ https://old.reddit.com/r/COVID19/comments/g6nmtf/update_on_i... (I'm linking to the reddit comments instead of the actual study because they're really nice tables and the links are still there for anyone who wants to double check) As others have said, for those around age 45 or less, Covid is equally or less dangerous than Influenza. And particular for those under 30 the flu is an order of magnitude more deadly at least. In the general population overall, Covid is undeniably more deadly than the flu, but only about 3-5x (and I think 3x personally right now). Recall that the flu is characterized by deaths in the very young and very old, while being less harmful to those "in between", purportedly due to the "cytokine storm" which is a scorched earth reaction of the immune system. Covid is very different, it is extraordinarily deadly to the very old, extraordinarily non-deadly to the very young, and about the same as the flu to those in between. A disease with such a "spiky" (highly variable) mortality rate based on your risk factors is precisely the kind of disease that is most effectively treated with risk-informed self quarantine rather than a national lockdown. Unemployment is correlated with a 2-3x higher chance of suicide, of which perhaps half can be explained away by mental health confounds [1]. There's unique factors in play here - rampant social isolation and widespead fearmongering, propagated even by health experts and "trusted" news sources at times - that lead me to believe that the spike in suicide and overdoses will actually be much higher than predicted by just unemployment alone. We're currently at 50,000 suicides per year in the US as a base rate, it is not unimaginable that we would see at least 50,000 _extra_ suicide deaths attributable to a mixture of lockdown and the general socioemotional environment. -- I haven't even gotten to the philosophical battle of "freedom versus security". I am, ideologically, someone who drank the koolaid and really believes in freedom and civil liberties over "security" (which I view as illusory anyway), but _even just viewed through the lens of reducing mortality_, the evidence is stacking up that lockdown is going to do more harm than good. Is the evidence fully settled? Of course not. But it's shocking to me how many people seem to be operating off of the projected CFR's we had in early February, shouting from the rooftops about "1 in 20" people dying (random recent case in point: https://news.ycombinator.com/reply?id=22952764&goto=threads%3Fid%3D__blockcipher__%2322952764 https://news.ycombinator.com/reply?id=22952764&goto=threads%...). I don't know whether it's just that a large swath of the population already had clinical anxiety which is further magnified by social isolation and social media and news headlines, or whether something else is at play, but I'm very concerned about the state of discourse in the United States right now, and more broadly, the entire world. In fact, ironically I feel a bit luckier to be in the US than some of these other countries because in the US _every_ issue is partisan, which while entirely irrational means that roughly half the country will be in favor of ending the lockdown at any given time (the position I am advocating for, within reason, insofar as hospitals are not overwhelmed), as opposed to other places where you can get given a $1600 ticket for driving a car by yourself, based off of a superstition that _being outside_ causes Covid as opposed to exposure to infected respiratory droplets... -- EDIT: Lastly I should mention that in a perfect world we could have voluntary variolation; I would love to be able to expose myself to a controlled dose of SARS-CoV-2 and self isolate for several weeks to ensure that I can never pass on Covid to someone else. Unfortunately that would be very hard to make a reality due to the political environment, even though I am advocating for it to be totally voluntary. I was heartened to see this recent paper toying with a variant of that approach: https://www.medrxiv.org/content/10.1101/2020.04.12.20062687v1 https://www.medrxiv.org/content/10.1101/2020.04.12.20062687v... (I don't agree with an "Immunity Card" for ideological reasons but I'm glad we have a paper attempting to model it out which does show benefit of voluntary self exposure) [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/pdf/v057p00594.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/pdf/v05...

jnbiche 6y ago

The CFR will be much higher than the IFR. For example, approximate flu IFR is 0.05%.

Scipio_Afri 6y ago
Exactly, CFR is always expected to be higher than the IFR. Most people getting sick don't go to the doctor. The CFR is an indication of how many people feel they need to go to the doctor and get treatment due to how severe the illness is. And of those that seek treatment how many die.
scythe 6y ago
In comparing the COVID-19 IFR to the flu IFR, it is important to remember that flu vaccines are widely available and limit the spread of influenza. For example, CDC retrospectives for 2018-9 estimate that 35M Americans[1] got influenza over the flu season, or less than 12%. By contrast, the current COVID-19 infection rate in New York (from these data) is already higher than 12%. So COVID-19 has the potential to infect a larger proportion of the population than the flu usually does. (If the CDC data is correct, the flu shot may save around a hundred thousand lives per year. Don't skip it!) 1: https://www.cdc.gov/flu/about/burden/2018-2019.html https://www.cdc.gov/flu/about/burden/2018-2019.html
- __blockcipher__ 6y ago
  That point is entirely true but doesn't really change the policy implications unless you believe that we can actually contain this thing indefinitely (i.e. that a given individual can avoid being exposed to it indefinitely)
- themgt 6y ago
  It's actually important to point out, the CDC is very explicit that they are estimating flu illnesses, in their own words "an estimated 34 million people had symptomatic influenza illness" Many people are attempting to extrapolate asymptomatic COVID to estimate an infection fatality rate and then making comparisons to CDC flu data for symptomatic illness, which is completely wrong. Actual flu sero studies suggest a large fraction of the population gets flu every year[1] and IFR is more like 0.02->0.05%, so even taking NYC numbers at face value COVID looks 10-50x more fatal than flu. [1] https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002082 https://journals.plos.org/plosbiology/article?id=10.1371/jou...

Karrot_Kream 6y ago

> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction That's unfortunately not the way statistics works. Combining multiple bad tests just makes the results incorrect or highly uncertain; the devil often is in the details. The Stanford/Santa Clara County study is a good example of how the details can really undermine a study. Things do indeed seem to be converging to the idea that the CFR is not near the IFR, but none of this is new news, and the IFR remains very close to what most epidemiological predictions indicated early last month. If anything, it confirms that COVID is a difficult beast to tame.

TheBlight 6y ago
We don't have perfect studies and perfect data but calling these tests "bad" seems slightly unfair. They give us an imperfect but useful snapshot of what's going on. But thanks for the condescension. I'd expect nothing less on HN.
- CydeWeys 6y ago
  The study is dramatically over-sampling exactly the people who have the most potential exposure to COVID-19, and dramatically over-sampling the people who don't. This could easily be way off. It's not testing the people who aren't home at all, and it has a low chance of only testing the people who leave home rarely, only when strictly necessary. It's mainly finding the people who leave home a lot.
  - TheBlight 6y ago
    How do you know how often these people are leaving their homes? Everyone has to leave their home to go to the grocery store eventually or someone in their family does. Yes there are delivery services but the vast majority of people aren't using those.
    - kgwgk 6y ago
      The more often they go, the more likely you encounter them.

monadic2 6y ago

> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction. How on earth could you differentiate this from the sample bias?

TheBlight 6y ago
All three studies created their sample groups differently. Are all three methods flawed in some way? Sure. Is it more reasonable to completely ignore the picture all three are painting or to consider there might be something to the trend?
- zamfi 6y ago
  > Are all three methods flawed in some way? Sure. They're not just all flawed in some way -- along at least one dimension they are flawed in the same way, that is, they all have a biased sample of the population that seems more likely to be infected than a true random sample. So, sure, they're "created differently", i.e., they're not all facebook ads, or grocery stores, but all those methods bias towards higher infection rates, don't they? Given that, you'd expect high precision, low accuracy -- these tests all paint a trend for sure, but we don't know how close that trend is to reality.
  - timr 6y ago
    These studies have completely different methodologies, and the only way you can plausibly call them similar is by arguing that anyone willing to get serological testing is substantially more likely to be infected than those who are not. That's little more than a fancy way of disregarding any testing result you don't happen to like. We've been systematically biasing toward testing the old and infected for months now. Why should rates derived from PCR testing with known severity bias considered a strong prior, yet rates derived from serological surveys with hypothetical severity bias be disregarded for the same reasons? At this point, we have many different studies from different parts of the world -- each with its own methodological flaws and biases -- and yet all are pointing in the same direction: a systematic under-count of cases, and an IFR somewhere between 0.5% and 1%, with a bias toward the lower value.

wtvanhest 6y ago

"Preliminary test results suggest 21% of NYC residents have Covid antibodies" Should be re-written as: "Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies" All of these tests suffer the same sample bias, and that sample bias is massive. People who are already in a grocery store (risky behavior), who are willing to give a blood sample (believes they may have been previously exposed) are not the same as people who are leaving their house only for very limited purposes and ordering food online. If we assume that the % of people shopping in grocery stores and willing to take the test are in risk group A, and the people not shopping are in risk group B. We can run the following quick analysis. First, assume risk group A is 5 times as likely to have had COVID than Risk group B (but plug any assumption in there necessary) Then assume that risk group A is only 10% of the population, then rerun the numbers as follows: For every 100 people in risk group A who tested, 21 were positive. Risk group A is 100 people, and risk group B is 900. Risk group A's 100 were 21 prior positive. Risk group B's 900 could be estimated that 4.2 people per 100 were positive, so in total of the 900 people 37.8 were positive. 21+37.8 = 59 people of 1000, or 5.8%. Plug any numbers you want in the analysis, but the assumptions drive huge variability in the % of New Yorkers infected. Without a less biased sample, we really don't know much other than that way less than 21% of New Yorkers have anti-bodies.

walterbell 6y ago
> Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies Would be useful to know how many deaths from the same zip codes are people who visited crowded grocery stores.
mannykannot 6y ago
NYC is not suburbia. I believe a higher percentage are still working outside of their homes (and, in many cases, that also means travelling on public transport), and a smaller percentage have the financial and transport resources to significantly reduce the frequency of their shopping trips. Apartment living means a greater exposure to your neighbors whenever you do enter or exit. The sampling in NYC, while far from ideal, may therefore be better than the other regions, in that shopping behaviour may not be as dominant a risk factor as you suppose. For the same reasons, plus the likelehood that the virus arrived in NYC early and frequently, NYC's figures cannot be extrapolated to the rest of the state or country. I would like to see how these figures match up to the sewage-sampling method of estimation.

ummonk 6y ago

Since false positives can be low single digit percentages, anything outside the NYC data is pretty meaningless, and a bunch of meaningless data is still meaningless.

jonahbenton 6y ago

Death counts in NYC, combined with a 1% CFR, indicate that the number of infected was ~1.5M 3 weeks ago; probably 2.2M now. That's about 25%.

kurthr 6y ago

Nope, because they're all designed to skew higher. People who are out shopping, people who will go out of their way on a weekend to be tested, healthcare workers... all of them are likely skewed high. One might as well conclude (with as much evidence) that the purpose of the tests is to show higher infection rates. Still, nobody doubts that we're under testing by a factor of 3-5x to catch a majority of cases. What's disturbing is that if you take the estimated statewide data of 2.7M infected (out of ~20M) at face value and only the current 21k dead you still get 0.7-0.8% fatality rate.

kurthr 6y ago
In support of my statement, it is now noted that the lead professor's wife sent out misleading emails to find people who wanted to return to work, if they tested positive. The professor says he knew nothing of it and because they are wealthy they would skew low rather than high. BuzzFeed News: A Stanford Professor’s Wife Recruited People For His Coronavirus Study By Claiming It Would Reveal If They Could “Return To Work Without Fear”. https://www.buzzfeednews.com/article/stephaniemlee/stanford-coronavirus-study-bhattacharya-email https://www.buzzfeednews.com/article/stephaniemlee/stanford-...