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In 4 US state prisons, 3,300 inmates test positive, 96% without symptoms (reuters.com)
785 points by throwaway888abc 35 days ago | hide | past | web | favorite | 603 comments



One of the super-interesting things here, is that apparently everyone was in the specific window where they test positive for the virus. This implies the population was recently infected, had not been previously infected, and it spread almost completely within a tight window.

This implies a shocking high R(effective) for that population. In 2 weeks we'll have super interesting data one way of the other on the CFR.


A prison is very much like a cruise ship from a viral perspective. We know from the cruise ship data like the Diamond Princess that most people had no symptoms initially, but overtime most people became symptomatic [1].

Of course if we are really lucky and the prison was infected with a naturally attenuated strain we should make use of it [2].

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078829/

2. https://www.tillett.info/2020/04/12/how-would-a-search-for-a...


> We know from the cruise ship data like the Diamond Princess that most people had no symptoms initially, but overtime most people became symptomatic [1].

Your wording is too strong. Data is 50% asymptomatic at time of testing, paper models that that number dropped to 20%.

> from paper on 19 February and 50.5% (320/634) on 20 February (Table). Soon after identification of the first infections, both symptomatic and asymptomatic cases were transported to designated medical facilities specialised in infectious diseases in Japan. However, these patients were treated as external (imported) cases, and a detailed description of their clinical progression is not publicly available.

We conducted statistical modelling to derive the delay-adjusted asymptomatic proportion of infections, along with the infections’ timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5–20.2%).


If you read in the discussion they reference other data sets to show that more asymptomatic become symptomatic. It is also what the Icelanders found with their data too where most people started out asymptomatic, but many later developed symptoms.


In a region in Italy they tested [1] people twice, 2 weeks apart, and found that 43% were completely asymptomatic over the course of the infection.

[1] https://www.medrxiv.org/content/10.1101/2020.04.17.20053157v...


About 50% of the population in that study is over 50. I think it's a bad idea to draw generic conclusions about the disease from a diverse age range. You end up with conclusions that aren't relevant to either. Glossing over the tables I don't see any data about symptomatic over the two weeks by age. I'd be shocked if the rates of becoming symptomatic are not much higher for the elderly and much lower for the young. Didn't read it in depth though.


See: Simpson’s paradox, where trends in subgroups, taken together, can indicate a different trend.

https://en.wikipedia.org/wiki/Simpson%27s_paradox


Trends in subgroups can indicate a different trend. The above comment is not about a linear trend. It's about an average statistic for a population based on a sample that's not representative of the population age distribution for a process that probably isn't linear across ages. If the older half becomes symptomatic 100% of the time, and the younger half becomes symptomatic 0% of the time, it's very misleading, though not technically incorrect to say people become symptomatic 50% of the time. There's a huge impact on how to handle the problem.


Doesn’t that mean that 57% were symptomatic?


I didn't intend to contradict you. Granted it's totally valid to assume that many people who are tested positive and show no symptoms might develop some later, but I just thought it might be of interest to some people to have numbers where they tested patients twice over a long enough interval to conclude that they really were asymptomatic.


I think that. depending on the air conditioning system, it might be quite a bit worse. The density of people is huge, air flows freely between cells, prisoners eat packed together at communal tables. I bet R is like 10-20.


On the Diamond Princess it was about around 14. I would expect prisons to be much the same.


Do you know how that broke down by room? Is there data on that? I wanted to make a map by room of infections, because that data point should be useful, e.g. was the rate higher or lower in rooms with outside access? Crew versus passengers? I just don't know of it already exists, or how to source the data.


We seemed to have early fluffed the data out of the Diamond Princess. We aren’t even tracking how many people died - it seems once everyone got off the ship all interest in it was lost.


I'd have thought that more airflow would help to reduce transmission, given that like other coronaviruses it's transmitted by droplets and not aerosol. Droplets would have trouble staying suspended with more air movement, and would be unlikely to make it through centralised air conditioning systems without becoming deposited on some surface within them.

The eating together at communal tables, and any other close-quarters communal activities, seem far more relevant to increased R than the air conditioning system and the free airflow.


Not really, not in a prison: Droplets exist in a continuum, and while most are large enough to drop out of the air, as they get smaller they stay in the air longer, and require less air current to stay aloft. [0]

As such, while the small particles may not exists in sufficient quantities when it is just one infected, or a few infected inmates, but with each infected inmate the concentrations of those small particles will increase. Let's say they're normally 0.5% of the total particle exhalations: 10 inmates get sick through direct contact, and their combined exhalations bring the quantity of smaller, longer lived particles up to an equivalent of 5%. More get sick through contact, bringing it to 20%. At some point, you hit a critical mass where there is a sufficient concentration to infect people, and creates a downward spiral from there.

[0] https://www.nytimes.com/2020/04/14/health/coronavirus-six-fe...


> A prison is very much like a cruise ship from a viral perspective.

Prisons are much worse. Chow line with men almost heel to toe. Commissary line. Med line. Recycled air. Big crowds in small spaces.


    > Prisons are much worse. Chow line with men almost
    > heel to toe. Commissary line. Med line. Recycled
    > air. Big crowds in small spaces.
How could the prison be much worse than the cruise ship you just described?


OP is describing a prison.


That's the joke.


Stress.


But in prisons you spend way more time isolated. There is less freedom of travel, and I'd assume they'd be less physical contact (although maybe more if you considered shared showers).


They share cells. They move around at set times. They are not isolated.

The soap is expensive, they don't have it and masks are not allowed.


I am communicating daily with a prison inmate and they are required to wear masks all the time. But yes, that won't help due to all the other aspects of prison.


That’s a cruise


I don't see them advertising it like that.


The table settings might be nicer, but it's still a chowline of people asses-to-elbows.

https://c8.alamy.com/comp/B5D6CE/breakfast-buffet-served-onb...


I guess you guys will have to wait for your first time in jail to spot the differences, if you aren't going to learn from strangers on the Internet


We could make the same dumb jokes about:

Popular restaurants, clubs, sporting events, concerts, movie theaters, stores on Black Friday, etc etc etc.

(Personally, I’ve been on 5 different cruises and they’ve each been among the best vacations of my life. But lol lots of people so they must be just like prisons amirite?)


I don’t live at/in a restaurant, club, sporting event, concert, movie, or store for a week or more.

Perhaps you do. But if you don’t, none of those things are similar to a week-long cruise at all, since you are there for hours and not days.


> I don’t live at/in a restaurant, club, sporting event, concert, movie, or store for a week or more.

I don't live asses-to-elbows in line with people for a full week on a cruise ship either. I have a room to myself. What point are you trying to make, exactly?


The room is only for sleeping. Everything else is massive crowded common spaces


Apart from the demographics, they seem to me pretty similar ;)


Prison demographics are very different than cruise ships, so might be interesting if different


To give evidence to this comment, the gp's source notes that ~75% of those that tested positive for covid were >60 years old. Whereas the BOP [0] reports a distinctly different age demographic. We know that covid dramatically affects people differently based on age, so I wouldn't suspect IFR to be similar to that of a cruise ship (where it is typical for the demographic to be older and retired)

[0] https://www.bop.gov/about/statistics/statistics_inmate_age.j...


Since the hospitalization and mortality rates vary so much with age, it would be surprising if the rate of asymptomatic cases didn't. Since prisons skew young we would expect a much higher proportion of asymptomatics to be found there than in the general population.


If you are going to say they skew young (or make any claim), please provide some figures. I did a search, from https://www.bop.gov/about/statistics/statistics_inmate_age.j...

  <18    Under 18     6         0.0%
  18    Ages 18-21    1,866     1.1%
  22    Ages 22-25    8,366     4.9%
  26    Ages 26-30    21,931    12.8%
  31    Ages 31-35    28,182    16.4%
  36    Ages 36-40    31,206    18.2%
  41    Ages 41-45    26,545    15.5%
  46    Ages 46-50    19,979    11.6%
  51    Ages 51-55    13,749    8.0%
  56    Ages 56-60    9,475     5.5%
  61    Ages 61-65    5,467     3.2%
  >65   Over 65       4,746     2.8%
Whether this backs you up or not depends on what 'young' is.


Skew isn't the right word. But relative to average this is definitely younger.

https://www.indexmundi.com/united_states/age_structure.html

Local demographics will vary wildly but it looks like the baseline would be 16% 65 and older, 13% in the range of 55-64. Since we're talking about prisons we should probably ignore the population under 18, so the normalized expected population over 65 is closer to 20%.


That's federal prison data. People who go to federal prison aren't necessarily the same cross-section of the population who go to state prison.

Anyway, those numbers are certainly much younger than the median age in the USA nationally, which is 39 years with over 65s being 15% of the population.


SARS, MERS, and now SARS-2 are all known for super-spreader events where occasionally one person just infects a ton of other people. And we know from testing that the amount of live virus in a person's phlegm can vary by many orders of magnitude so I guess that isn't surprising. There are a lot of people who infect just one other person but the overall R(effective) is substantially driven by the long tail.


Exactly! What are the chances of that, compared to a gross error in the testing, like someone processing the samples was infected and not very careful?


The chances of an antibody test kit not being sufficiently specific to SARS-CoV2 as opposed to endemic coronaviruses is quite high.

Now, what are the chances that some government institution acquired tons of crappy test kits, didn't validate them before use, and then proceeded to publish the results which just so happened to be exactly what they wanted them to be?


>> .. that apparently everyone was in the specific window where they test positive for the virus.

The data comes from 4 prisons. It is theoretically possible that all 4 of them happened to be in the same window after an initial infection, but it doesn't seem very likely. I guess the only way to be sure is to do follow up tests every week or so, hopefully that will happen.


I repeatedly point this out. There was the church choir practice where 45 out of 60 people were infected within what two hours? There is also a Korean call center where about 2/3 of the people in an open office were infected.

I've repeatedly linked to CDC study that estimates r0 in Wuhan before lockdown at between 3.8 and 8.7. Median 5.8.

Take away: Completely avoid being indoors with large groups of people.


Especially if they're singing or talking non-stop.


That's also shown in the recent outbreak in Singapore - almost all of the cases are migrant workers housed in tightly packed dormitories.


Covid-19 fatalities takes up to 2 months after infection, symptoms have a median of just over 5 days from first exposure.


Keyword "up to".

The most common fatal case seems to be roughly: 1 week without symptoms, 1 week of mild symptoms, 1 week of severe symptoms, death. That's 3 weeks.

In rare (1/10000) cases, incubation time can reach 14 days, that's where the 14 days quarantine recommendation comes from. But the median is closer to 5 days.

Time to death can be much longer, the virus can cause all sorts of damage to the body, including secondary infections. And if the patient is strong enough to resist the primary infection but not enough to recover, it can take a long time.


> In rare (1/10000) cases, incubation time can reach 14 days

What's the cite for that number? I'd be very suspicious of anything claiming a 5-significant-figure result in a disease that has only (heh) 3M known cases.

In fact there's significant supposition that the incubation time and asymptomatic contagious state can last much longer than originally guessed. This would go a long way to explaining the difficulty of detecting early outbreaks (basically nowhere in the world was able to contain before community spread was happening) and the anomalously low rate of new case decline post-peak. But there's no good science on this, and probably won't be in time for it to be useful.


> What's the cite for that number? I'd be very suspicious of anything claiming a 5-significant-figure result in a disease that has only (heh) 3M known cases.

You are totally right about that. I misinterpreted the following paper https://www.ncbi.nlm.nih.gov/pubmed/32150748

The actual number is more like 1/100. The 1/10000 number is the probability for someone who has a 1/100 chance of being infected to develop symptoms after 14 days.

Uhm... too late to fix my original post, sorry.


South Korea had a very rapid decline post peak. 1062 cases on 2/29 to 114 cases on 3/11. With an average of just under 10 cases per day for the last week.

Also, the number I have seen was ~1% of cases where asymptotic for 14 days. https://hub.jhu.edu/2020/03/09/coronavirus-incubation-period...


Do note that rapid decline in the day-to-day increase of case numbers is not necessarily indicative of rapid decline in transmission rate. Transmission rate could have declined gradually over the course of a week or more, and the rapid decline in day-to-day increase of case numbers could have just been detection catching up with existing infections.


Their daily testing was reasonably consistent over that time period. So, their is some wiggle room, but it’s at minimum a 80% drop over 2 weeks. Compared to the US where daily numbers are steady for weeks with the last two days having the greatest number of detected cases.


I've given up on the USA numbers. There is no way an underlying exponential process is going to deliver you a near constant 30K new infections for nearly 4 straight weeks.

Something else is driving those numbers, and it ain't the actual number of new infections. For me the obvious candidate is they hits some testing limit.


You shouldn't think of it as one underlying exponential process though. It could be many overlapping and time-delayed S-curve type graphs which are additively causing a constant rate of infections.

Although it's also most certainly true that we're vastly undercounting the cases in most (if not all) areas.


Only if you develop a severe case in the first place, which you would know about within 2 weeks average. Incubation on average is 3-5 days, severe symptoms then start in the second week.

After you're hospitalized, yes you might or might not then have a month long battle for survival.


Median of 10 days though.


Numbers I have seen where median 5.1 days from exposure to first symptoms and a median of 8 days from first symptoms to death in Italy. So, a median of ~2 weeks from exposure to death. https://hub.jhu.edu/2020/03/09/coronavirus-incubation-period... https://www.lastampa.it/esteri/la-stampa-in-english/2020/03/...

However, early estimates are going to be biased without the slowest fatalities. Further, that’s also population specific.


The South Korean data suggests there is a long tail on the death rate. It seems the very old and ill die quickly, but those that are healthier it can take a month or more to die.


[flagged]


[flagged]


There is definitely a lot of evidence that people who test positive will eventually die. It might take 50-100 years, and they might die of something else, but yes they will definitely die. Plenty of evidence of that.

But seriously, what is this evidence you speak of?


[flagged]


I find your joke to be in poor taste.


Wow. Just wow.


The CFR will be an interesting (though tragic) data point, but I wouldn't generalize too much from it. The demographics of the inmates are probably not representative, and the health care that prisoners receive is absolutely not of the same quality that civilians may receive.

The Princess cruise was a good indicator of CFR for a specific demographic range when they tested early, often, and had access to quality care. If the CFR of this prison will tell us anything, it will be a counterpoint to such information showing what happens when quality of care is greatly diminished.


Are these tests covid-19 specific or are they based on IgM/IgG?

> The results of the tests for IgG, IgA, and IgM levels are usually evaluated together. Abnormal test results typically indicate that there is something affecting the immune system and suggest the need for further testing. Immunoglobulins testing is not diagnostic but can be a strong indicator of a disease or condition. There are a number of conditions that are associated with increased and decreased immunoglobulins.


I'm looking for this in the article and not seeing mention of a specific window. Can you help me find what you're referring to?


The sensitivity of PCR tests to SAR-COV-2 varies over time from time to infection. I think the the OP is overestimating how narrow this window is though. It's not like you need to test within a few days or a week to be positive - it's just that viral load is maximum for only a few days (roughly the +/-3 days around onset of symptoms).

Even after the viral load in the upper respiratory tract drops off, there's still lots of virus around. You'll likely test positive -1/+2 weeks around onset of symptoms.


Depending on the nature of the virus and how the PCR test works with it, I couldn't rule out that it could be much longer than that. Virii are not my area, but there are bacterial diseases that result in positive PCR years after infection. Disclaimer: microbiology is also not my area.


Yes, it all depends on how the agent infects the body and how the immune system is able to clear it. Our belief (based on how other coronaviruses behave) is that the body is pretty good at clearing it out, and that the virus doesn't really have a mechanism for forming a reservoir within the body.

The expectation is that PCR negative is roughly correlated with fighting off the disease in the short term, and basically perfectly correlated in the long term.


Is this test sensitive to just SARS-CoV-2? What is the possibility that other corona viruses (like the ones that cause many of our colds) could create a false positive? I haven't heard anything from a reliable source on that.


PCR is pretty selective for SARS-CoV-2


I'm trying to figure out what tests they used. PCR is very specific but only works reliably in the early stages and generally has a high false negative rate.

Antibody tests only work after several days of infection, but some of them are not very specific to SARS-CoV2 and therefore have a high false positive rate.

So you really need use both tests, but you can't just OR the results together, because then a faulty antibody test will massively skew the results upward.


> but only works reliably in the early stages

No, it only works if there is enough virus to be detected. If you are infected, you will have a high enough count to be detected. Each type of PCR test is highly selective. The point of them is that they can differentiate between many types of virus, depending on how its setup.

> Antibody tests only work after several days of infection

Yes. Depending on the type of test, and how its done. it can be up to a month before these tests are accurate.


In principle yes, but The PCR test is usually a throat swab, where detectable amounts often disappear after a few days, even in severely symptomatic cases.

This is the reason why China started to accept clinical diagnosis like chest scans, causing a sudden big spike in cases.


Is there a source that says they were using PCR testing? The article seems pretty light on technical details.


Do not know if it is in the article or not but I guess the reasoning from GP goes like this: huge percentage of people are pre-symptomatic. The median time for symptoms to appear is 4-5 days from infection time, so they were probably infected in a very short period of time recently.


You're probably right, but I think that's flawed. We still don't know how many people are completely asymptomatic. It's a very weak assumption to base this argument on.


We don't know if infection provides immunity, so they could be repeat infections, or even viruses that never cleared.


Why is this being downvoted?

Yesterday the World Health Organization said: "There is currently no evidence that people who have recovered from COVID-19 and have antibodies are protected from a second infection."

https://www.npr.org/sections/coronavirus-live-updates/2020/0...


We will also see if they are asymptomatic or presymptomatic.


To add another data point, Singapore has been testing foreign workers living in dormitories extensively, uncovering about 10,000 cases (about 80% of the country's total cases). These are relatively fit individuals between the ages of 20 and 40. However, the number of people in the ICU has remained remarkably consistent at about ~20 people in the past 10 days (Fingers crossed it remains that way).

https://www.moh.gov.sg/news-highlights/details/46-more-cases...


Many migrant workers in Singapore are the only breadwinners for their families back home in Bangladesh/India/Myanmar. One of the reasons the Singapore government has done extensive testing way beyond global standards is that many of these foreign workers are financially disincentivized to seek medical help the moment the first symptoms appear. The fact that Singapore's leader has publicly promised stable incomes for this affected group speaks volumes of the government.

As a fellow south east asian, I am deathly afraid of the under-reporting and testing in other ASEAN countries. Singapore. A 2017 statista study of migrant worker populations in South East Asia (https://www.statista.com/statistics/711513/asean-number-of-m...) shows that Malaysia and Thailand have much larger migrant worker populations than Singapore. Their living conditions are either on par or worse than the ones living in Singapore, yet there are apparently no Covid-19 clusters among migrant populations in other ASEAN countries. This is extremely alarming.


It would be extremely alarming if there were large numbers of deaths or hospital admissions occurring among the Singapore workers who are testing positive. Given that there are virtually none, the lack of testing in other ASEAN countries is hardly extremely alarming. A lack of data is less concerning if you wouldn't do anything differently if you had the data.


If it turns out that the infection rate is higher and the death rate is lower for healthy individuals than originally estimated it would be extremely alarming that were are not taking a totally different approach to handling this pandemic


agreed.


Thailand just yesterday reported some.


I've read that asymptomatic carriers are though to be less infectious than those with symptoms because of the lower concentration of virus in the saliva. Also, many virologists mentioned in recent texts that the initial concentration of the virus you receive can affect how sick you'll get - the more viruses you're exposed to, the faster they can invade the body and the more severe it will get.

Can those two facts be combined into a theory that asymptomatic carriers are more likely to produce more mild and asymptomatic cases?

Don't know if it makes any sense (probably not), but it would certainly explain how in some closed environments there's a prevalence for mild cases, while in others there's a plenty of very sick people, regardless of the age.


I have been wondering about the effect of the degree of initial infection. I kind of assume the virus grows exponentially inside the body and would dwarf the initial constant. If a low initial dose affects the severity of the disease, I think that would be incredibly useful to know. I also wish there was info on percentage of infection via fomites vs inhalation.


I think it’s the opposite. The initial conditions have a profound effect on the exponential nature of the growth. Half the dose could mean that your immune system can suppress it. There is probably critical rate at which both growth rates match.


I don't think my response is 100% correct. The rate of change (derivative) of an exponetial function is another exponential function. d/dx(2^x) = (2^x)*ln(2). And if the base is e, then d/dx(e^x) = e^x.


Yo people, I don't understand why my statements are correct - please someone with a math background or an epidemiologist comment here, I suck at math and I don't understand how these growth rates compare. My comment is being upvoted but it may not be true.


Under the exponential growth model, the increased incubation time (before you hit the same total population size) is inversely proportional to the log of the initial dose, so (e.g.) 1/100th of the initial dose gives you only ~7 (log2(100) = 6.64) additional doubling times before arriving at the same population size. While this is not insignificant, it does mean that initial dose is potentially much less important than sources of variability that affect the in-host doubling time.

Edited to add: Here's a preprint that is relevant to the "initial dose vs. immune system response" thought: <https://www.medrxiv.org/content/10.1101/2020.03.26.20044487v...


The math is a simplification of a biological process that is highly variable and poorly documented. Your original idea is a fine hypothesis, though the suitability of the params behind exponential growth is mostly irrelevant.


We’re learning a lot about virology during this time. Infection and immunity are not binary, and now we have enough data to recognize that. We will also learn a lot about mutation, things like carrier recombination. I think this will change everything about how we attempt to control things through vaccination.


> I kind of assume the virus grows exponentially inside the body and would dwarf the initial constant.

That assumes your immune system wouldn't kick in during the asymptomatic phase or a time close to exiting the latter. But your immune system would actually kick in as soon as it detects the infection, which would plausibly be much earlier. That would effectively buying you time to figure out which antibodies to produce before things become out of control.


Variolation

"The procedure was most commonly carried out by inserting/rubbing powdered smallpox scabs or fluid from pustules into superficial scratches made in the skin. The patient would develop pustules identical to those caused by naturally occurring smallpox, usually producing a less severe disease than naturally acquired smallpox. "

https://en.wikipedia.org/wiki/Variolation


> I kind of assume the virus grows exponentially inside the body and would dwarf the initial constant.

Perhaps a more mild initial growth stage gives the immune system more time to respond.


If the immune response occurs in constant time, and is able to neutralize up to a constant number of viri at that engagement time, then you'd expect to see bimodal outcomes.


Most infectious diseases are dose dependent in their effects actually, which can be a bit counterintuitive. I dont know of any data for covid yet, but heres a paper on influenza, another viral disease:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342672/


In addition to the other comments, there's also thought to be differing impacts by infection location.

Covid in your alveoli is very bad. Covid in your throat not so much.


Not expert but I think if the initial constant can be several orders of magnitude different (I guess a few when airborne vs billions in a droplet) then it can impact the delay of the infection and give time for the immune response.


Yeah, as I understand it, typically the "dose" of the initial infection is important for how bad the symptoms will be.

The virus will grow exponentially, but so will the immune response, so starting conditions are important.

This is a basis for the old pre vaccine "variolation" strategy of getting immunity. Some radical thinkers argue for it as a Covid remedy.


Early on, there was some discussion of the possibility that folks whose initial exposure is via the eyes would have a more mild illness than if the exposure was via nostrils or inhalation, because the immune system would have time to begin work while the virus was multiplying more slowly in tissue less suited to it.

I haven't heard anything in weeks studying anything like this, though, so I don't know where we ended up, if, indeed anybody knows anything at all.


I remember one of the health experts in a Sam Harris podcast saying in an interview the dosage affects the severity and that this is seen in some other viruses (though I don't recall which ones). That's one reason doctors and nurses might be getting hit so hard, due to the continual exposure.


These were exactly the assumptions of the RIVM in the Netherlands end Februari and March causing it to completely spiral out of control.


> causing it to completely spiral out of control.

I live in the Netherlands and I think you could say that we actually have it quite under control (relatively speaking of course). We never reached peak ICU capacity and the ICU occupancy has been steadily declining for more than two weeks now[1]. Our schools for children between the ages of 4 and 12 are scheduled to partially open again on the 11th of may[2].

[1] https://nos.nl/artikel/2331720-coronacijfers-van-25-april-ri...

[2] https://nos.nl/artikel/2331460-kabinet-wil-basisscholen-voor...


We, the Netherlands, got it under control because we went into stricter lockdown against the advice of the RIVM. Above assumptions led to advice against a lockdown.

Only under pressure of the IC doctors on Sunday the 15th of march did the prime minister close all schools and restaurants. (A high school is like a festival every day)

The policy and advice of RIVM has been wrong and misleading from the end of February. For many of us it was already clear they lost control beginning of March.

There was one hero in the North of the Netherlands who went against RIVM and the Minister of health: https://eenvandaag.avrotros.nl/item/het-gelijk-van-microbiol....

This article in the Volkskrant describes how the RIVM was panicking beginning of March, but the prime minister wanted to present "good weather", and they also send their stock of masks to China beginning februari: https://www.volkskrant.nl/nieuws-achtergrond/nederland-stuur...

Even now they are not testing healthcare people with symptoms in elderly homes and also not providing any sort of masks. Claiming the available masks are not of sufficient quality, they prefer to send healthcare people to work without masks then to get high quality masks even if it is without the preferred certificate.

Last point. Under control is relative. The IC capacity reached a higher number then we ever anticipated, still more IC beds are occupied by corona patients then we had in total beginning of 2020. https://stichting-nice.nl/

For most of the hospitals in the Netherlands most operations and treatments are still postponed. For instance for many cancer patients the date of their treatment is still not sure: https://www.volkskrant.nl/nieuws-achtergrond/chemo-uitgestel...


The data doesn't support any of this. Overlaying the R0 estimation and the date that interventions were taken shows that the original "stay home when sick, wash hands and don't shake" advise had the biggest effect. This also matches most research that has been done on influenza. The lockdown on schools and restaurants had a much lower effect.https://www.volkskrant.nl/nieuws-achtergrond/corona-onder-co...


The RIVM lost the battle in Februari not in March, this is was what my initial comment referred to.

Throughout Februari and March they had the following policy:

- Clearly stating and assuming that asypmtomatic people cannot or are extremely unlikely to transmit (had letters sent to schools and events I particpated in, was also on their website). True or not, there was no data to backup this up and an assumption like this has big consequences when false.

- When from risk area AND with symptoms as a policy they didn't test. In general the whole focus was to test as minimal as possible. Causing us to be completely caught off guard of the true scope untill March.

- Clearly trying to sell the idea that masks don't work for normal people, while at the same time trying to claim them for themselves

- As a policy not testing healthcare people, not even with symptoms. First random test of healthcare people with symptoms in the south was only done on the 8th of march, they were shocked by result (and for a long time untill march allowing people to work with symptoms ). https://www.rivm.nl/nieuws/steekproef

- As a policy "non-essentials" healthcare people get no protection unless evidence of covid and hardly get tested. 900 of 2100 healthcare/elderly houses now have the virus with 20 to 30 percent death rate.

- Failed attempt of centralised buying of masks and other protection wear

https://www.volkskrant.nl/nieuws-achtergrond/jos-de-blok-buu...

- Till the end of Februari claiming they had it under control

- etc...

There is more things to point out but Ill leave it at this. The above assumptions and actions are a big part of what made the Dutch ministery fail to deal with the crisis properly and on time. Like many of the Western democracies. If they would have acted in Februari a full lockdown probably could have been prevented or shortened, lives would have been saved and many other treatments wouldn't have been cancelled or postponed.

The article i've linked to above talks about the doctor who did the opposite in the North, and was succesful with it, they even tried to force him to follow their policy.

To come back to your article. The short answer we don't have enough data (yet) to make such conclusions.

It seems reasonable that from the 9th of March the infection rate went down. This was the week all of Europe freaked out and many people started working from home, even if it wasnt official policy except for in the South of Holland (this idea was good, but too late). Certains schools already (partially) closed, partly because not enough teachers were showing up.

Whether or not school en restaurant closure lead to a lower infection rate is not clear from the data. The RIVM's analysis indeed suggests that it only had a small impact. The English analysis of the Dutch data in the same article does suggest a bigger impact. It's telling that the RIVM doesn't trust their own analysis enough to turn in into policy, you can watch the briefing of Dissel of last week, they only very slowly open the elementary schools in a few weeks from and don't open restaurants and high schools till the end of may.


> Rijksinstituut voor Volksgezondheid en Milieu

> Netherlands National Institute for Public Health and the Environment

> The Netherlands National Institute for Public Health and the Environment, is a Dutch research institute that is an independent agency of the Dutch Ministry of Health, Welfare and Sport. RIVM performs tasks to promote public health and a safe living environment by conducting research and collecting knowledge worldwide.


Where did you read that? Superficially that might make sense - less virus particles == less obvious symptoms - but there are a wide variety of virus responses that show virulence and infectiousness aren't necessarily correlated.

If anyone wants to read more about this I can't recommend highly enough the book Spillover by David Quammen, which was published in 2012, and covers zoonotic (animal-to-human transmission) viruses, including SARS. Reading the section on SARS made the hairs on the back of my neck stand up. It's uncannily similar to what's happening with Covid-19, and explains a lot of the background involved in these kinds of viruses.


A crude mathematical model from Swedish authorities on the Stockholm outbreak used two parameters: 1: the fraction of undetected cases (assumed to be mild or asymptomatic) and 2: the relative infectiousness of that group compared to the “detected” group.

The larger the undetected group is, the lower their relative infectiousness has to be in order to fit the observations. The best fit I believe was 1/25 detected and 11% infectiousness of the undetected group.


For the particular conditions in Stockholm, such as testing prevalence etc


Yes absolutely. The actual parameters would be different everywhere but they seem to indicate that the symptomatic group is more infectious (which I guess is the base hypothesis for a droplet transmitted disease).



Thanks - that's the kind of thing I was looking for, but this line stuck out to me: "However, the evidence of the relationship is limited by the poor quality of many of the studies, the retrospective nature of the studies, small sample sizes and the potential problem with selection bias." The book I mention gives me enough reason to doubt that what we know about Covid-19 at this point is anything like the whole story.


I actually do remember reading that this was true specifically in the case of COVID — that more exposure so far seems to correlate with a worse infection.

Sadly, I have no idea where I read this. But... I know I did! Recently! Maybe NYT?



Yeah, I saw it too. The hypothesis, as I recall, is that the more virions inhaled, the more likely some of them will get deep in the lungs where they can do the most damage.


I read something like that too. I came away thinking that ingestion might be a better way to get it than inhalation. I think you really need to keep it out of the lungs and nervous system. But that's all my impression from who knows where.



Bucheight


>> Also, many virologists mentioned in recent texts that the initial concentration of the virus you receive can affect how sick you'll get - the more viruses you're exposed to, the faster they can invade the body and the more severe it will get.

That is basic infections 101. When you are exposed to any dangerous virus a race starts between the virus and your immune system. If the virus starts out only infecting a handful of cells, your immune system has a head start in developing antibodies before symptomatic infection sets in. (This is also a basic principle behind many vaccines.) But if you are hit will a massive viral load that instantly infects every cell in your lungs, the immune system is fighting uphill from day one. A massive initial viral exposure can also trigger an excessive immune response, for instance dangerously high fever. Such an immune response can be as deadly as the virus. Much covid research is going into not defeating the virus directly but regulating/slowing the immune response to the patient survives their own immune response.

This principal explains why healthcare workers are suffering so. They are exposed to constant massive doses of virus, possibly from multiple patients carrying slight different versions of the virus. So they get sicker than people who are exposed in the general community.


Sleep deprivation and extreme stress could be factors that affect healthcare workers (1)

https://theconversation.com/coronavirus-does-the-amount-of-v...


There's already a comment above pointing out that it might be the initial infection location plays big role. Seems very likely that poor ventilation (in e.g. medical facilities) is main cause of severe cases.

Also, the virus is replicating exponentially only if it can reach many uninfected cells. It takes ~10 hours for an infected cell to start producing virus. Not sure whether non-specific immune system can somehow "contain" virus, would be great to learn about that.


I would think they'd be less infectious simply because they don't have symptoms that make them more so, namely coughing and sneezing and the like.


It depends. If you're not going to develop symptoms for another 14 days then yeah, you're not infectious. But for a few days before symptom onset you become just as infectious as you will be after symptom onset - which is one of the things that makes this virus hard to stop.

A low initial dose doesn't seem to affect the course of the infection much, though. For a sufficiently low dose either none of the viruses find an ACE2 receptor or your innate immune system wipes up the virus without you noticing (as it does for you with other viruses every day).

There's some evidence that particularly high doses can cause particularly bad prognoses. We have pretty good evidence that this is the case with measles. There's very anecdotal evidence suggesting that maybe this is the case with SARS-CoV-2. But it looks like low doses lead to a chance of no infection, not a chance of an asymptomatic one.


I'm very confused by this as I understand that asymptomatic patients are infectious. How does this square with your second sentence?


So, it looks like most asymptomatic infections later become infectious. Basically every person who gets symptoms had a multi-day asymptomatic infectious period they went through. SARS-CoV-2 does a better job than most of avoiding the automatic immune system because its large genome lets it encode a bunch of proteins that aren't for making new viruses but rather screwing with the immune system in ways that let it reach high numbers before the immune system catches on. Every virus that can actually make you sick does this but SARS-CoV-2 seems better than most. But frequently you're able to fight off the infection at a later point before the part of the automatic immune system reaction that makes you feel sick kicks into play but after you're got a chance to infect other people.

The normal course of the automatic immune system wiping out an invading pathogen without you noticing is that it happens immediately without you noticing and you never get a chance to infect anyone. But if that doesn't happen for COVID-19 or influenza or most things there'll be a time period after infection but before you notice anything where you're infectious. For COVID-19 this period is particularly infectious compared to the flu or SARS-1 or most things. It might be that flu and other coronaviruses tend to people who are infected enough to transmit the virus but never go on to develop symptoms. I don't know in that case.


WHO have maintained for months that asymptomatic patients are not infectious.

Until very recently they still said

"The risk of catching COVID-19 from someone with no symptoms at all is very low."

They now say "Some reports have indicated that people with no symptoms can transmit the virus. It is not yet known how often it happens. WHO is assessing ongoing research on the topic and will continue to share updated findings."


Yes, the WHO was incorrectly relying upon information provided by China. Since then it has become an accepted fact that asymptomatic transmission of SARS-CoV-2 is not only occurring, but is one of the primary transmission vectors.

https://www.nejm.org/doi/full/10.1056/NEJMoa2008457


Prof. Dr. Drosten (inventor of Covid-19 test) said in the interview below that there is a study which shows asymptomatic adult shed as much virus as symptomatic ones. He also said there is no good study on how many viruses are shed by kids.

https://tvthek.orf.at/profile/Additional-Content/1670/Langfa...


I remember Robin Hanson [1] used the second fact to recommend "variolation", injecting everyone with a tiny amount of the virus so that they'll get the immunity without the sickness (making it a "natural vaccine").

[1] http://www.overcomingbias.com/2020/03/variolation-may-cut-co...


how do you ever make antibodies if you are perpetually asymptomatic though?

do you carry it forever? does it attack eventually?

what happens if you are an asymptomatic carrier and get a vax?


"Typhoid Mary" was an asymptomatic carrier of typhoid fever for at least 38 years.

https://en.wikipedia.org/wiki/Mary_Mallon


FWIW I think that article shows her infecting people for only 15 years (1900-1915), then being quarantined for a further 23.


The Wikipedia article doesn't cover everything. Reference for the sibling comment: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3959940/


That article has 1906 (her engagement as cook in the Warren household) to 1932 (her paralysis) as the period of her infection of others, assuming after her paralysis she didn't infect anyone else.

That's still not 38 years; it's not especially important a point - she infected others over at least 2 extended periods amounting to mor than a couple of decades in total.

It's interesting to me, I thought nih.gov was a scientific publication but at least one part of that document appears to be opinion asserted as fact (~"she never intended to abide by the conditions of her release").


> ... 1906 ... to 1932 ... as the period of her infection of others ... That's still not 38 years ...

I never stated that she infected others for 38 years. Being an asymptomatic carrier does not require continually infecting others, only that the carrier maintains the infection without showing symptoms. [1] Additionally, the NIH article isn't complete in listing likely infections, as evidenced by comparing it to the Wikipedia article. Nor does is state that she continued to infect others until her paralysis in 1932.

As for the 38 years, the Wikipedia article notes 1900 as the first known, likely infection of a family she worked for. Then, from the NIH article:

> A post mortem revealed that she shed Salmonella typhi bacteria from her gallstones ...

Her death (and, presumably, post mortem) was in 1938. "Bacterial shedding" [2] implies infection and, thus, being a carrier in 1938, though asymptomatic. I arrived at 38 years by considering her likely a carrier from 1900 to 1938.

[1] https://en.wikipedia.org/wiki/Asymptomatic_carrier

[2] https://www.google.com/search?q=bacterial+shedding


A post-mortem confirmed that her gallbladder was loaded with the bacteria that cause typhoid.


Wikipedia mentions that autopsy might not have been performed.


AFAIK asymptomatic means you show no symptoms but your body fights and creates antibodies just the same. Disclaimer: I'm not a doctor.


I've been tracking the antibody study results in a spreadsheet, and they are suggesting a 10-20x undercount of cases in the official "confirmed" numbers. You can see the data I've collected here: https://docs.google.com/spreadsheets/d/16onEUBWIV5IqN1RCvTla...


I did similar calculations, and found the institutions in charge give us very unreliable data. The term "corona case" is very, very, ambiguous and cannot the understood as such without a detailed explanation on how the counting was done.

Thanks for sharing.

I found the peek in all-case mortality also very interesting, because that way counting is much more unambiguous: dead is dead.

They showed a clear diversion from the "average" in recent weeks, but... they did not show the stdev for the averages. Finally I found a chart that shows that "outliers" are not uncommon.

https://imgur.com/IPNiXRe


Here is for Europe if you are interested. There are charts for excess morality by country, by age, and by year.

https://www.euromomo.eu/

There is a very noticeable spike in the worst hit countries: Italy, Spain, France, Belgium, Netherlands and the UK. The cumulative excess deaths for all Europe in the last two months matches the COVID-19 reported deaths (around 100000).


> The cumulative excess deaths for all Europe in the last two months matches the COVID-19 reported deaths

This article claims that most Covid-19 hotspots have significantly more excess deaths than reported covid deaths. Suggesting that there is a lot of underreporting. NYC being a notable exception. I think several countries count only corona deaths in hospitals, but systematically miss all deaths in care facilities. https://www.spiegel.de/wissenschaft/corona-todesfaelle-wie-v... (charts should be readable despite any language barrier)

(I don't defend either view. As the old joke goes, don't trust any statistics you haven't manipulated yourself :) Statistics and causality have always been difficult, even more so in exceptional time with imperfect short-term data only.)


There is under-reporting that's for sure, but there is also a delay in all-cause mortality. In some cases, the same country both over and under reports. To be honest, I am surprised to see such a close match. I expected similar numbers, but this is within ~10%, which I suspect is a coincidence. But still, that's interesting.

Another coincidence is that the country that is most suspected of under-reporting, Germany, is also the least represented in EuroMOMO. There is data for only 2 regions.

Italy, Spain and France, the most significant contributors both in COVID-19 related deaths and excess mortality in general, all count deaths in care facilities now. I don't know about the UK though. Deaths at home are probably not counted, but according to the authorities, they are a minority: COVID-19 does not happen suddenly and people normally have time to go to the hospital. Still significant though.

My gut feeling is that there are actually ~50% more deaths than reported. But we'll have to wait for at least a few months to get proper statistics.


It's nice to have another source on this. I saw a similar comparison from an Italian site:

https://www.corriere.it/politica/20_marzo_26/the-real-death-...

New York has been criticized for retroactively adding older cases. The Johns Hopkins data just tacks it on to the end of their time series, creating a weird spike and screwing up what the data represents:

https://battlepenguin.com/tech/fighting-with-the-data/


The spike has all but receded. Maybe be we're just having 3 flues in one year. Reason enough to stop the world?


The spike receding is due to incomplete data rather than a reduction in deaths. Deaths (especially in the current circumstances) can take a couple of weeks to get registered properly.


also need to take into account the decline in deaths from other factors due to quarantines, social distancing, and improved hygiene. less driving, less spread of other infectious diseases, etc.


> The cumulative excess deaths for all Europe in the last two months matches the COVID-19 reported deaths (around 100000).

Where's the noticeable spike for Ireland? Where's the spike for Portugal? Where's the spike for Luxembourg? Where's the data for the rest of Germany outside of Hesse and Berlin? Where's the spike for Austria? (And this is not a criticism of them, but they only track Western Europe by the looks of things.)

Don't know where Euromomo is getting its data from but I suggest to you that it's incomplete.

This NYT article[0] (other publications like The Economist[1] have arrived at similar numbers) show that the cumulative excess deaths for France, Netherlands, Switzerland, Spain, and England & Wales sometimes far exceeds reported Covid-19 deaths.

Of the countries you mention only Belgium is actually reporting accurately. (As is Sweden btw, a country you do not mention.) Note that both are smallish countries.

As of 14 hours ago Chris Giles (FT economics editor) tweeted[2] "A cautious estimate of the total number of UK excess deaths linked to coronavirus stands today at 42,700" (He updates this most days.) Worldometer[3] currently has the UK on 20,319 deaths. Quite a difference.

In fact, the numbers show that in Europe actual deaths are between 1.4 [Swiss] and 2.1 [British] times higher than the reported numbers for countries that are under-reporting.

Btw, you say that there are 100,000 deaths? EU 27 has 96,533 deaths as of this moment. EU+UK has 116,852 deaths as of this moment. And Europe in its entirety[4][5] has 122,568. (Am tracking these figures using a spreadsheet.)

Let's say that the adjustment we have to make is between 1.4 and 2.1 and let's ignore population size and pick 1.75 and then lower that to take into account that some countries are accurately reporting and let's err on the conservative side so let's choose 1.66… repeating as our adjustment rate, agreed? This gives us an estimated excess # of deaths for Europe of 204,280. Twice the figure you've given.

[0] https://www.nytimes.com/interactive/2020/04/21/world/coronav...

[1] https://www.economist.com/graphic-detail/2020/04/16/tracking...

[2] https://twitter.com/ChrisGiles_/status/1254105061745098752?s...

[3] https://www.worldometers.info/coronavirus/country/uk/

[4] https://en.wikipedia.org/wiki/Europe [5] https://www.reddit.com/r/europe/new/

(Europe is generally taken to extend from the Atlantic states of Ireland, Portugal, and Iceland in the West to the Ural and Caucasus Mountains in the east, from Scandinavia in the north to Italy and Greece in the south.)


There's a simple reason for that: the lockdowns are killing lots of people and will kill even more. That's not under-reporting but rather an obvious outcome of clearing the health system in expectation of a surge that never came. Probably the UK will see an extreme form of this effect as people are encouraged to see the NHS as a sort of public park that everyone has to take care of rather than a large mechanical system, as in countries with private/insurance based healthcare.

Admissions at hospitals have collapsed: in the UK they halved. Admissions due to respiratory illnesses however didn't really go up, not surprising when you consider the small absolute numbers. There is now a massive backlog of operations and diagnostics for cancer that health systems will struggle to clear in time.

There's a story with some analysis of that problem here:

https://www.telegraph.co.uk/news/2020/04/26/what-second-coro...

In the past the recommendations of epidemiologists have ended up killing a lot more than they saved, with the 2001 foot and mouth epidemic in the UK being a classic example. It's likely it will be true again this time.


Malta is also considered part of Europe, south of Italy.


Of course yes, sorry. I should have said the Mediterranean Islands Malta/Cyprus in the the south. Thanks!


Yea, I agree that the case counts are very different between regions. I think the more interesting column is the IFR estimate based on the antibody study results, since dead is dead as you say.

It is interesting though that the median undercount is converging to ~10-20x. Perhaps the protocols across regions are similar enough that the confirmed case counts are somewhat comparable.


It seems possible to me that there are variations, mutations, in different regions that might account for some of the variance in apparent R value and such?


That chart is funny. The title is “unprecedented spike”, but it just shows that such outliers have occurred regularly.


Not at this time of the year if I am reading the chart right.


Time of year doesn't matter for his point though. What we care about is mortality, not whether it happens at the start or end of a winter.


Not all months are the same. It's clearly unprecedented for this time of the year.


> because that way counting is much more unambiguous: dead is dead

Unrealistically low death stats coming from Turkey compared to cases easily refute that argument.

Dead is dead, unless the state finds a way to claim that it was not a COVID19 dead.


No. Not unless. You misunderstand what all cause mortality means. The state can count COVID19 deaths as all bungee-jumping related the number still shows up in the total death rate. If you know what is the usual statistic you can show if there is an effect.

This is what “dead is dead” mean. One can argue what should count as a COVID19 case, and how exactly we are counting. There is a lot less argument over who is dead and who is not.


> You misunderstand what all cause mortality means.

It seems I actually missed the mention of "all-cause" while reading the comment.


a family member who is a doctor at a hospital told me that any patient that dies of a respiratory illness is being marked as covid19 even if they were never tested for it. dead truly means dead. dead by covid19 does not.


> Dead is dead, unless the state finds a way to claim that it was not a COVID19 dead.

It's just a matter of demanding tests to declare as a COVID death and do not providing enough tests.

Brazil, for example, has an artificially low count of cases due to the lack of tests and a similarly low number of deaths. However, cases of death by "pneumonia", generic types of SARS and "unexplained respiratory diseases" skyrocketed: https://oglobo.globo.com/sociedade/coronavirus/alem-da-covid...


This has occurred in many countries and will presumably be part of way the final impact is finally measured. https://www.nytimes.com/interactive/2020/04/21/world/coronav...


I would add that it doesn't have to be a malicious government purposefully undercounting. It's very easy to undercount or misdiagnose even with competent and well meaning people. Specifically every pneumonia death, heart attack, stroke is a potential undercount, and people aren't "bad people" or evil for making the wrong conclusion about the ultimate cause, especially when testing is less available and frequent than it should be. Also I have heard that there are a significant number of people dying of COVID19 in their homes and that those are more likely to be undercounted vs. a death in a hospital.


I said "dead is dead" in relation to all cause (not "only covid") mortality figures.


I saw it just now. My apologies.


Or the other way around, someone who dies in a car crash but tests positive could count as a COVID death.


Outliers such as this one are not uncommon in the winter. They are in April. According to that chart there isn't a single point outside of winter months as high as this year.


Maybe because its new and hit late winter?


Is this for the US? Do you have a link for the source?


the chart is from ft.com, the news paper


Red line, nearly transparent old data, no link to source data... What's not to like? :) /s


Your sheet is interesting. Looking at it, the IFR varies from 1.66% down to .11% and the 0.11% is for the Santa Clara, which many considered rather suspect.

The 1.66%, otoh, seems reasonably in line or at least compatible with what's been observed in Korea and elsewhere.

Given age is going to skew things a good deal, it seems like a picture is emerging but not that new a picture. An IFR of even 1% is pretty bad, especially given these statistics show how infectious this virus is.


The numbers that are quoted for Austria (which has the listed IFR of 1.66% in the document) weren't obtained via antibody tests, but via PCR tests. Here's a better source than the one linked in the document: https://www.sora.at/uploads/media/Austria_COVID-19_Prevalenc...

Most of those tests were done on April 4th and 5th which was 3 weeks after Austria started relatively strict lockdown measures, which also impacts that number, as this will result in the test to find an even lower number of positive people.


> An IFR of even 1% is pretty bad

To be clear: it would be the most dangerous general epidemic disease since the advent of vaccination, and by a significant amount. You need to go back to measles and polio to find general population outbreaks that were more lethal.


A very important thing is that % is concentrated among old and people with preexisting health conditions.

Not every death is the same - a 80 year old with weak immune system could have lived 5 years longer without corona, but a healthy 20 year-old dying from cytokine storm caused by influenza has lost potentially 60 years of healthy life - the loss is much worse.


I agree we should consider years of life lost but the figures are a bit higher than they ones you're using. 14 years on average for men and 12 for women. Not 60 years but more than 5. https://wellcomeopenresearch.org/articles/5-75


Thank you for this link - this is the reason I come here :)

Seeing those numbers makes me more supportive of the isolation measures.


Not every death is the same - a 80 year old with weak immune system could have lived 5 years longer without corona, but a healthy 20 year-old dying from cytokine storm caused by influenza has lost potentially 60 years of healthy life - the loss is much worse.

In a triage situation, where you have to decide between different people dying, such choices are unavoidable or necessary. But I want highlight that you are talking about this stuff to say that it's OK to plan for the death of "a 80 year old with weak immune system could have lived 5 years longer" versus no death at all. And that's not OK.


But those discussions happen all the time. If there is a drug that can extend the life of this person for 5 years but it costs X - should it be covered by society ? At what X does it become unacceptable ? The highest numbers I've seen argued are in the 200k/year but realistically it's much lower depending on the country.


Well, there are two kinds of choices that are generally considered radically different.

One choice is providing some opportunity for further life beyond what's expected. That's generally considered something society likes but isn't obligated to provide. Society doesn't obligate it's member to spend money developing some miracle-extend that gives someone five more years.

The other choice is taking life that would normally be expected. That is something that society very much frowns on. If you could protect someone and you don't do it 'cause it would cost you money, you may wind-up in jail for murder.

Very quantitatively oriented people seem to have a hard time grasping why there's a difference here. But I think it's very rational in an evolutionary game-theoretic compact kind of way. Everyone is a member of society and values everyone else's life highly, more highly than immediate material things though maybe not more highly than other people's lives. This gives member of society basic security - you are thinking my insulin might worth just stealing and selling on the open market, me murdering you first might be my best strategy. You can see where things break down? The "social contract" is kind of the way around this.


You can self isolate without quarantine measures in effect so your point isn't that strong to me and you are ignoring that one of the biggest destabilising forces in history is economic downturn. US-China relations have been bad for a while now and both sides are throwing blame at each other as a populist policy (China has a US origin story allegedly). If this pushes the economy in to a global depression who knows what will happen s few years down the road. Taiwan, Korea, plenty of places that could erupt if things become politically unstable - both in the US and China.


Self-isolating vulnerable populations is almost impossible. You're talking over 100M in the US.

And not doing anything (pretend it's just the flu) will result in 50M dead world wide. Everyone worried about a new depression should realize one is going to happen no matter what we do now. The only thing we can do is act in a humane fashion.


Your number is highly suspect. About 16% of the US is 65+, or roughly 50M.

Where do you get your 50M worldwide figure? When a new flu appears, Neil Ferguson claims his 3K lies of undocumented C code forecast 200M will die. These numbers are all speculation and worse predictors than throwing darts at a board behind your back.


One third of the US population is considered at risk due to comorbidities. With over 328M people in the US, that's roughly 100M.

If you look worldwide, there are 7.8B people. If herd immunity takes 60% of the population becoming infected, that's 4.6B infections. With an IFR of 1%, that's 46.8M deaths. 460M hospitalizations (where possible).

Even say the IFR is overstated as some like. Say it's a magnitude less, comparable to the flu at .1% Now you are down to 4.7M deaths, but still the 460M hospitalizations. Still one of the most serious crises in the last 100 years.


> Not every death is the same

OK, so how many "influenza death equivalents" are we looking at? What's your metric for how bad this is? I mean, I think that's a little ghoulish, obviously, but if people really want to make this argument I'd really like to see the kinds of well-founded numbers that the experts are producing. Medical ethics is hardly a new field, after all. You'd think someone would have pulled some analysis off the shelf.

Instead, the people pushing "these people would have died anyway" seem to be almost exclusively political actors (or their proxies on social media sites like this one) with a goal of either defending the inaction of the current administration or pushing a policy goal that necessarily sets the virus loose on the public.

But if you really want to make a numerate case for not trying to save the old and sick, I'd genuinely and carefully read it.


I think there are already such calculations for approving drugs on public health programs - cost of treatment vs years of quality life provided.

So you would need to estimate the number of years lost vs the economic damage. This is impossible to get right on both sides but at least it gives you a framework.


Roughly 25% of males in the US 35-44 had hypertension in 2010, and this grows to over 50% by 55-64, an age I don't think anyone would consider especially old: https://www.heart.org/idc/groups/heart-public/@wcm/@sop/@smd...

Basically everyone who dies from this has a preexisting condition, but basically everyone in the US will develop at least one of the big three (hypertension, diabetes, or obesity) at some point.


Fortunately those conditions are preventable in the majority of cases.


You are correct, it is possible to prevent some of those conditions in some (but not all) future cases.

Kinda sucks if you have any of those conditions right now though.


We don’t cry about the millions of heart attacks that are easily prevented. We definitely don’t Give a damn about some person dying of cancer when they are smokers. Why are we drawing a distinction here?

You chose to eat X and not exercise for years/decades. A middle class American has enough education and purchasing power to know and behave accordingly.

Does it suck? Yes, but i find it incredibly unfair and hypocritical towards the rest of the world by ruining their lives based on the extremely old and or fat/unhealthy population.


You do realize that at least in the US, 30% of the population has comorbidities?

And your comment comes across as extremely crass and insensitive.


How does that compare, with other high IFR diseases, didn't they also have deaths skewed towards older people?


The 1918 flu was notable for having a high fatality rate for young children. That does not seem to be the case for flu in general.


To be perfectly fair (and for the record I'm very much not among the "sacrifice the old and weak" set!): not as much as covid.

The elderly and immunocompromised obviously die more to almost every illness. But the effect is really pronounced with covid. And most other viral infections tend to kill children at higher rates too, and covid very notably does not. It's definitely an interesting aspect of the disease, though it's produce a kind of horrifying calculus among a lot of the right wing in the US.


> since the advent of vaccination

What do you mean? There were vaccines in 1918.


or the 1957 Flu pandemic but I suppose that was not long after the polio vaccine.


It's interesting that this jibes with conventional wisdom. Since nearly the very beginning of when people started looking at graphs of case counts, my colleagues and acquaintances (mostly scientists) assumed a ratio of roughly 10x. Folks would look at any graph and automatically bump it up by an order of magnitude.


I think antibody tests will soon become more useful for tracking disease progression in a population than the viral tests. The collection methods may skew things but they still are much more close to a random sample than the viral tests which have lots of issues with test shortages and people unable to get tested (or not wanting to go to the hospital with mild symptoms).


They have huge false positive rates though.


Not sure why you are being downvoted:

https://www.oxfordbiosystems.com/COVID-19-Rapid-test

"In order to test the detection sensitivity and specificity of the COVID-19 IgG-IgM combined antibody test, blood samples were collected from COVID-19 patients from multiple hospitals and Chinese CDC laboratories. The tests were done separately at each site. A total of 525 cases were tested: 397 (positive) clinically confirmed (including PCR test) SARS-CoV-2-infected patients and 128 non- SARS-CoV-2-infected patients (128 negative). The testing results of vein blood without viral inactivation were summarized in the Table 1. Of the 397 blood samples from SARS-CoV-2-infected patients, 352 tested positive, resulting in a sensitivity of 88.66%. Twelve of the blood samples from the 128 non-SARS-CoV-2 infection patients tested positive, generating a specificity of 90.63%."

That gives us 62% false positive ratio according to (where a study finds the prevalence to be 6% of subjects using the test):

http://vassarstats.net/clin2.html

In some cases we have research being carried out with such low positive results that they can entirely be accounted for by the low specificity. So for example if you took samples from 100 people, based on 90% specificity, even if everyone had never had corona, 10 could be found positive.

Credit to this post:

https://old.reddit.com/r/COVID19/comments/g7f373/second_roun...

However it should be noted the article in question for this submission does not mention the type of test used.


I wonder what's the process through which false positives happen in this case. Previous infection by milder Coronaviruses?

Edit: I'm looking at the reddit post but I have a lot of reservations with the "prevalence 0.06", unless we'll use the test to test absolutely everybody and not only people who are suspect. Has that calculator been validated as well?

If the test was 12 false positives in 128 negatives, how come they can claim the false positive rate is 60%?


Apologies for the way this was linked to. The 6% is from this study:

https://www.miamidade.gov/releases/2020-04-24-sample-testing...

"Our data from this week and last tell a very similar story. In both weeks, 6% of participants tested positive for COVID-19 antibodies, which equates to 165,000 Miami-Dade County residents"

That is what the commentator is referring to in the linked post.

So if you plug their own figures into the calculator:

Sensitivity .8866 Specificity .9063

and a Prevalence of .06 based on the study, you get the 62% false positive rate.

As the prevalence increases, as with the NYC study which found the positive rate to be 21% (prevalence), the false positive rate decreases, down to 28% of the NYC study.


The password you need to Google for why it happens is "antibody cross reactivity." Not necessarily other coronaviruses but I imagine they're disproportionately more likely to cause it.


This is from ARCPoint Labs, where I took my antibody test:

The Antibody test is a serology test which measures the amount of antibodies or proteins present in the blood when the body is responding to a specific infection. This test hasn’t been reviewed by the FDA. Negative results don’t rule out SARS-CoV-2 infection, particularly in those who have been in contact with the virus. Follow-up testing with a molecular diagnostic lab should be considered to rule out infection in these individuals. Results from antibody testing shouldn’t be used as the sole basis to diagnose or exclude SARS-CoV-2 infection. Positive results may be due to past or present infection with non-SARS-CoV-2 coronavirus strains, such as coronavirus HKU1, NL63, OC43, or 229E.


Yes. That’s one possible explanation. Interestingly quite a lot of people might be somewhat immune to the new Corona virus due to anti bodies from previous Corona cold infections. More than 30% showed such antibodies in a recent study. https://www.finanzen.net/nachricht/aktien/drosten-hinweis-au... (Sorry that the only source I have ready right now)


60% is the probability that a particular positive test result is actually a false positive. It's not the overall the false positive rate.


Only until the point that they don't. Unfortunately that requires a lot more people to have had covid...


There are many different tests, from different manufacturers. Some of the tests have higher false-positive rates than others. Some have higher false-negative rates. Even a survey with an imperfect test can be designed to yield reliable data.


And false negatives.


If the rate of false positives and false negatives is the same but the true prevalence is << 0.5, then you will overestimate the number of positives.


Do we know the rates already?


There’s preliminary data: https://covidtestingproject.org/


I wonder which tests they used? A recent study is finding that some antibody tests are much better than others when it comes to false positives:

https://tildes.net/~health.coronavirus/o6a/coronavirus_antib...


I know that the California studies used the same test kit. It had 2 false positives out of 371 samples of pre-covid19 cases, and has a 10-20% false negative rate. Because the case numbers are so small, the false positives can skew things quite a bit. I took the midpoints of their 95% confidence intervals in the spreadsheet.

I don't know what tests the other studies used.


Here's a similar spreadsheet, with less detail but more studies:

https://www.reddit.com/r/LockdownSkepticism/comments/g6eqtt/...

It may be helpful to you.

Thank you for tracking these metrics.


What numbers are you using for sensitivity and specificity?


It depends on the study, I've been using their reported confidence intervals. The two California studies (Santa Clara and Los Angeles) used the same kit, which has 2 false positives of 371 tests, and 10-20% false negative rate.


It's worth noting that is the manufacturer claim but has not held up to independent validation.

Specifically, the Premier Biotech/Hangzhou Biotest Biotech test was validated by a Chinese provincial CDC and found 4 false positives out of 150. [1]

It was also validated by the COVID-19 Testing project and found 3 false positives out of 108. [2]

The Biomedomics test used in the Miami Dade study was also validated by the COVID-19 Testing Project and found 14 false positives out of 107. [2]

Hence I would recommend taking the results of the California and Florida studies with a huge grain of salt as the prevalence rates they found were within the false positive rates of the tests used.

[1] https://imgcdn.mckesson.com/CumulusWeb/Click_and_learn/COVID...

[2] https://covidtestingproject.org/


you have the prevalence for NY state but the row is called NY city. The NYC prevalence was 21.2


i don't understand the value of this multiplier. Case statistics are not an official census, it's incidental , depending on the criteria with which each region makes tests. Case numbers are unimportant, it's the total infections and consequent deaths that matter


You're probably right that they shouldn't be, but case statistics are regularly treated as an official census. I've seen many news articles in the vein that suchandsuch country is handling it better or worse because of their case numbers, or statistics like CFR that are computed from official case numbers.


while the absolute value doesn't matter, its time course is mostly representative , because countries rarely change strategies wrt testing.


That just doesn't seem true. In the US testing strategies are rapidly changing, since health officials indicate this will be essential to safely removing restrictions.


One reason is that early on when the curves looked pretty much exponential, folks were trying to pin down how bad it could get, and when. This was when the worst case scenario was for nearly the entire population to get it. I'm not quite sure we're completely out of that woods yet.


News articles from around April 10 indicate that mass testing hadn't begun, or was just beginning (1 example here [0])

Reporting on 96% without symptoms is misleading without mentioning this: It gives the impression that the # of coronavirus infections could be up to 24x higher than the known positives cases. But symptoms can take 2-14 days to develop, meaning it is entirely too soon to tell if these are all asymptomatic cases, or merely pre-symptomatic.

[0] https://www.dispatch.com/news/20200410/coronavirus-in-marion...


96% are without symptoms yet.

There was a nursing home in Massachusetts which had 51 out of 98 residents testing positive but asymptomatic in early April. While this sounded encouraging in the sense no one was critically ill because of coronavirus, a few weeks later 19 had died and about 30 more had tested positive.

Let’s wait a month until there is a clearer picture about the impact of the virus on a particular population of people.

https://www.boston.com/news/local-news/2020/04/04/coronaviru...

https://www.wcvb.com/article/85-of-patients-at-wilmington-ma...


The virus affects young and healthy folks without co-morbidities dramatically less than it does old folks at a nursing home. Based on New York City data, people without co-morbidities account for something like 6% of hospitalizations [1]. Old folks on a cruise ship full of old people, about 20% showed no symptoms. Is it a big stretch to think that scales to prisoners as shown? They're pretty young (only 2.8% of prisoners are over 65 [2]), and therefore pretty unlikely to show symptoms let alone require medical care. Age is unquestionably the biggest factor in outcomes for COVID [3].

"Asymptomatic" is kind of a sliding scale. Are you sick? No. Do you have a stuffy nose? Kinda.

[1] https://www.the-scientist.com/news-opinion/nearly-all-nyc-ar...

[2] https://www.bop.gov/about/statistics/statistics_inmate_age.j...

[3] https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e3.htm


Over 40% of Americans have comorbidities (hypertension and obesity being most common) so that stat is not particularly useful.


42.4% of Americans are obese: https://www.usnews.com/news/healthiest-communities/articles/...

When you add in all the other things that count as comorbidities here, you're probably looking at like 75%.


To add to your point, here is a paper with data on this:

https://www.kff.org/global-health-policy/issue-brief/how-man...


If a virus is spreading in a community exponentially and the latency from exposure/infection to symptoms is greater in length than the doubling time then half of everyone who tests positive is going to be pre-symptomatic.


Wouldn’t prisons disproportionately have poorer health than average for several reasons?


Is there data on that? Or is it an assumption?


There's plenty of data indicating that's the case, for instance [1]. That said, the population is also much younger and age has a much bigger impact than comorbidities.

[1] https://issues.org/correctional-health-is-community-health/


surely there are data. "assumptions" underlie all "data"[1], but i would call "prisoners not having good health care" a pretty decent assumption. if you're so curious, maybe go google it yourself, and then post a study if you find one.

i'm so tired of literally every other HN comment being like this. there is truly nothing more low effort / "i am very smart" than the HN-classic "do you have a source for that? where's the peer-reviewed study?". it adds absolutely nothing to the discussion, and yet i see all sorts of materially less obnoxious things be downvoted to oblivion.

[1] "citation needed"



Basic healthcare in prison is free (I believe?), and I reckon the food is comparatively less likely to give you diabetes?


Healthcare may be "free" but that doesn't mean it's any good.


One would suspect, but one would be wrong. The data bears this out but also food that's bad for you tends to be cheaper than food that's good for you and Bureau of Prisons isn't known for doting upon its charges.


Even then I imagine the financially and socially disadvantaged would be more likely to end up in jail, who are unlikely to be in good health to begin with.


Perfect, sounds like a good experiment then, so let's wait and see.


Did you see the NYC antibody sample that showed that approximately 21 percent of citizens had antibodies? It seems like a nursing home is a pretty bad representation of a population.


Am I the only one who is still confused by what they're finding in these antibody tests? Are they looking for antibodies that attach to specific features unique to SARS-CoV-2? Because I'm pretty sure even HCoV-NL63 enter lung cells through ACE2 as well. How can they tell antibodies for these viruses apart? Also aren't antibodies effectively developed in a sort of random process?


> Are they looking for antibodies that attach to specific features unique to SARS-CoV-2?

They're all slightly different, but yes they're looking for antibodies against specific parts of SARS-CoV-2, like the N protein [0][1]. I think the N protein ones are most common. I just did a BLASTp against SARS-CoV's N protein and there's maybe ~90% homology? So I would hope they're using a site that's different between the two. Or, there's an assumption that most people have not been previously exposed to SARS-CoV or others with similar N proteins.

> Also aren't antibodies effectively developed in a sort of random process?

Yeah, but there's only so many prominent features to a virus that you can make antibodies against.

[0] https://www.abcam.com/novel-coronavirus-igg-antibody-detecti... [1] https://www.ncbi.nlm.nih.gov/protein/QHW06046.1?report=fasta


I am also curious about this. As I understand it, an immune individual could have any mathematical subset of antibodies from the base set, which is the collection of all proteins that can bind to something on the surface of a COVID-19 virion. Furthermore, I would think these base sets can change slightly for different mutations of the virus.

Perhaps humans tend to have enough random antibody generation that they are likely to start mass producing most of the protein shapes that are able to bind to the virus? And as another commenter pointed out, there are not that many options to bind to.


Look up VDJ recombination[0] for a sense of how antibodies are generated. Ling story short, yeah its pretty random in a really clever process that generates enormous variability. There are also only so many features to bind on the covid virus protein, which are what we test, but there are a lot of antibodies that our body can make against them


I am confused as can be about all of it now. First it was stay locked in, then heard immunity, now they say heard immunity may not happen.


you're not wrong to be. I think the problem is that there's this perception that you must be authoritative to get people to do things, and also this perception that science is authoritative. As a former scientist I think both are wrong, and especially science under duress is likely to be even wronger, for many reasons. We don't live in star trek where you can boop boop a console and magically get answers.

I wish we had leaders that had the chutzpah to say things like, "look the science is inconclusive, so we won't arrest you, but please do the right thing and wear masks". But we don't. And also we have people spouting completely non-evidence based assertions like "if you don't force people to wear masks, then they won't". Which of course fuels assholes to flaunt not wearing masks, because now it's not about doing the right thing, it's about freedom.


If you're confused by this, you may need to check your news sources. Experts have been explaining all of this for months. First, people need to stay locked in and keep distance in order to slow down the spreading so health care systems don't get overwhelmed. Second, the disease itself can only be stopped once herd immunity is reached. Ideally, herd immunity is achieved by vaccination, once there is one. Until then, social distancing is needed to limit the number of deaths and keep the health system working. Third, it is not yet clear whether long-lasting immunity can be achieved at all. It's very likely, but there is not yet enough data. Immunity may last from 2 months to 2 years or longer. We don't know yet for sure.


Not sure why you're being downvoted, this is well established. Even with everyone indoors, the US new infection rate remains around 40,000 new cases per day recorded -- and holding steady. Now with states re-opening that can only go one direction, until herd immunity is established.


Those are positive tests. They are remaining high because the number of tests has been increasing. The important metric to track is percent positive tests, which has been consistently dropping for weeks.

https://coronavirus.1point3acres.com/en/test


I'm curious if that's true -- based on that excellent data, it appears that the number of people who test positive has been pretty much steady. Chances are those were always, and remain, positive tests at the point of care/admission to a hospital. The new tests are likely randos. So long as we continue to see the same raw absolute number of positive tests, I'd say it's not a win -- yet. There's been in fact a steady increase since 4/21 in positive tests in real number terms.


And that's assuming that herd immunity will be established, which we have no way of knowing until we know how long - and if - a person is immune after recovery.


The 40,000 new cases per day is predominantly a function of the number of tests being run. The number of actual infections has far outpaced the number of tests. Look at test positivity rate across NYC for example.

https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...

If instead we had done random sampling we could have been very accurately projecting the number of active cases pretty easily, but apparently we’ve mostly decided not to do that until now with the antibody studies.


It's a sign of the times that this, one of the most level-headed and factually-accurate comments on this post, is being downvoted so heavily.


I agree, but so is a group of male inmates (many of them older) compared to the general population. Until more testing on this group is done in a few weeks and symptoms emerge, we won’t have a clear picture on how many are truly asymptomatic.


I am not sure that these antibody tests mean what people generally think they mean. For example, there seem to be multiple events where people get sick even after it has been established that they have antibodies.

Furthermore, people in environments with a lot of virus (i.e., cruise ships, hospitals, or just northern italian towns where the virus has run amok, tend to get sick and die at much higher rate than those antibody tests would suggest.

There may be a mechanism for multiple infection which makes multiple exposure more dangerous even if you have antibodies.


>For example, there seem to be multiple events where people get sick even after it has been established that they have antibodies.

Do you have a source for that?


It didn't show that - there's a good analysis at https://towardsdatascience.com/were-21-of-new-york-city-resi...


I don’t buy any inference drawn from that study that is in the realm of “20% of the population of NYC was exposed to SARS-CoV-2 and developed immunity”

I think that will be the primary message that people will get from that study.


Is there any evidence that would convince you 20% of NYC was exposed and developed immunity?


How about an actually randomly sampled test, for starters?


It's the "for starters" that concerns me. A lot of us were always saying that official case counts are much too low, and antibody surveys were supposed to be the definitive proof that we were responsibly waiting for. Now they're starting to come out, and still nobody believes it. I worry it's a moving goalpost, and no evidence will ever be enough to make people start reconsidering their beliefs.


> antibody surveys were supposed to be the definitive proof that we were responsibly waiting for.

Simple question: Why?

For most coronaviruses, antibodies reflect only a temporary immunity, that is usual gone in 6-24 months, due to the nature of these viruses.

All an antibody survey shows is that antibodies can be created, not that they are effective long term. Showing a longer term immunity takes statistical analysis, usually after that temporary window has ended.

In fact, antibody surveys may not even show an effective temporary immunity, if the wrong kinds of antibodies are being screened for.

Knowing this, why was the antibody surveys supposed to be some golden bullet? The advice from the medical community was "this is being actively studied, wait and see."

The surveys provide the medical community with important data, but they don't really provide us with policy making data, and they certainly don't predict the future for the general population when exposed to the virus.


I think the argument being made is less about lasting immunity but reevaluating the actual risks for the general population. 8 million people live in NYC and there has been 0.155 million confirmed cases so far. If the actual infection rate is 20% then that represents a 10 fold overestimation of morbidity and mortality.

A good place to keep an eye on for the short term would be Sweden. Despite the lack of lockdown their disease penetrance is still on par with the UK.


> If the actual infection rate is 20% then that represents a 10 fold overestimation of morbidity and mortality.

10 fold over what? A problem since the beginning is that many people are confusing CFR and IFR. Worse is when people compare the IFR of COVID-19 to the CFR of the flu. Regardless, the IFR for COVID-19 has been thought to be .5-1% since the beginning. If we assume the NY antibody study is mostly correct (even with the sampling errors), I believe it puts the IFR in the .5-1% range [1]. If that IFR holds it still means 1.6-3.3M deaths in the US assuming the healthcare does not get overwhelmed.

[1]

deaths/(cases x 10 fold) x 100 == IFR

21908/(288313 x 10) x 100 == ~.75%

Data pulled from https://www.worldometers.info/coronavirus/country/us/ on 4/26/2020 @ 8am EST.


> If that IFR holds it still means 1.6-3.3M deaths in the US assuming the healthcare does not get overwhelmed.

You cannot assume that 100% of people will be infected. Looking at case studies like USS Roosevelt (840 of 5000) and Diamond Princess (712 of 3,711) as the worst case prevalence because they are much higher-R environments.

So basically your IFR based fatality numbers could be divided by roughly 5.


In both case quarantines were put in place and/or people were eventually evacuated. Yes, there is a limit where not 100% of the population will be infected. Given the R0 of COVID-19, currently herd immunity is thought to be reached between 60%-80% of the population getting infected. So even if we are generous and take the low end of the IFR we get 960k - 1.28M deaths to reach herd immunity.

There is some news out that is putting the IFR closer to .3% on the low end. That is great news if it holds up. The problem is that the numbers out of NY, if flawed would bring the IFR lower than reality, and they are ~.75% IFR.


However, biased antibody studies (no self-selection criteria) that may have high rates of both false negatives and false positives do not represent anything about the current level of estimation whatsoever.

Which is why when these studies happen, the public is told to wait for it to be assessed, rather than pretending all of us are remotely qualified to judge the content and draw conclusions from it about what actual risks the general population might be facing.


I'm not sure I follow what you're responding to. Antibodies are definitive proof of a previous infection, which is what I was talking about.


> Antibodies are definitive proof of a previous infection, which is what I was talking about.

When the studies in question have a high rate of false positives, that is absolutely not the case. It may simply be a statistical anomaly, from taking the incorrect confidence interval.

Currently, from the studies taken, it looks like we have high rates of both false negatives, and false positives. Which means that the testing does not give you an accurate picture of whether a population group has previous infections or not.


I don't think anyone is moving goalposts. Most of the antibody studies that have come out have had serious flaws either with the tests themselves or the sampling. The recent NY one was ok, but still had sampling issues because it only sampled people who were out and about during a lockdown. I would expect those people to have a higher prevalence of exposure.

With that said, the extrapolated numbers for NY do fall in line with the original IFR of .5-1% The downside is that if that is the IFR then the US is looking at 1.6-3.3M deaths assuming hospital systems can keep up as the infection spreads.

Edit. It's also important to talk about infection counts (what antibody tests are looking for) and case counts (people who show symptoms and end up seeking medical care). In the past when people were saying it's just the flu they were comparing COVID-19 IFR to the flus CFR.


I totally agree that the confirmed case counts are way too low, because even most people who were symptomatic weren't able to get tested (e.g. me), let alone random people who were asymptomatic.

But the 21% study is seriously flawed because it didn't do a random sampling of the population. We need that at a minimum to know with any certainty what the actual exposure rate is. The figures that are coming back from studies using random samples in other places have been much lower.


It did a random sampling. We should do followups to screen off possible biases people have proposed, but stopping random people in the grocery store is by any reasonable standard randomization.


No. It’s a random sampling of people who are out during lockdown. It can’t being extrapolated to the whole population when large parts are not leaving their homes.


And if you go around to people's homes, you'll oversample people who aren't out during the lockdown. There's no silver bullet here.


You do both. This is why study design matters. And it's one of the reasons all of the early antibody studies have issues (the other being test accuracy).


It's almost like you need to do your random sample based on a list of all residents, and not just go out and try to find people at various locations.


There is no such list. No US state has a master list of all residents. The DMV has a fairly high percentage but even that tends to miss children, older people, undocumented immigrants, etc.


I would be willing to bet that if you combine all the different lists that New York State and its various agencies have (DMV, DOE, Department of Taxation and Finance, NYC ID, jury duty, voter registration, social services, etc.), that you would easily get >99% coverage of all people who've resided here for at least one year.

This would be a much better list to sample randomly from than "go to a grocery store and test everyone who walks in".

I should point out on /r/nyc, some local redditors saw the testing going on all week in the same location and posted about it, informing others. I suspect this led people who wanted a free test to actively seek them out, especially because it's so hard to get tested otherwise. I'm pretty sure I had it over a month ago and I still haven't gotten tested, so if I'd seen those posts in time I'd have headed over there to get tested myself. Point is, the sample is even further biased because word spread around and some number of people getting tested there were actively seeking it out for reasons.


Now you're talking about a huge legal issue just to get access to the data, followed by a huge record linkage issue to remove duplicates. So with the time pressure involved, your proposal is so completely impractical as to be ridiculous.


These are all state agencies. They're already sharing data with each other anyway (e.g. the jury selection tool is getting feeds from many of these other sources).

What huge legal issues? This is all the government. Of course it has lists of all of its citizens, and can and does use said lists.


You say "almost like", but scientific studies rarely sample the population this way because researchers generally don't have access to a list of all residents.


The state is running these studies. I guarantee you New York State has many good lists of people living in the state. Start with the jury duty list, for example. It pulls data from the DMV, voter registration files, state tax filers, non-driver's IDs such as NYC ID, and more. That covers all the adults. You can get a good list of adults residing in the state to pull your random sample from, and to include the children go get data from the school system and/or just test whatever children live with any given adult that you pick randomly.


Well, the type of information you're trying to gather is rather unique. Usually we just wait for a virus to run course then test lots of people to see the resulting case counts. But we can't do that here. Normally you just use a control and test group, but that doesn't work for figuring out underlying infection rates.

There are some tests trying to sample everyone in a geographic area (SF Mission census block) but the data isn't out yet because they're conducting tests as we speak.


I guarantee you that when the data comes out:

* It will also show an undercount of at least an order of magnitude.

* Commenters will still pop up to explain why the results can't be trusted and which further studies are absolutely required before we believe them.


I guess we'll see. I don't share your certainty. Although I'd love to be able to go outside sooner.

Also elsewhere in this thread it's mentioned that the Florida and Santa Clara results could be entirely explained by high type II error in the test. The Florida test appeared to have a false positive rate of ~15% when independently validated, which is basically the infection rate they found. In other words, this is a specific form of base rate fallacy where the test accuracy is really low.


> It will also show an undercount of at least an order of magnitude.

It seems pretty likely that the data will come out showing at least 10%, so it's literally impossible for it to undercount by an order of magnitude.

How do you think a random sample of inhabitants would be off by a whole order of magnitude, anyway? Can you explain the mechanism whereby that might happen? The only thing that comes to mind would be using a worthless test with a 90+% false negative rate.


Does anyone not think case count is an order of magnitude less than infected count?


That's definitely relevant to the question of why it's difficult to do such a study. However, it's not relevant to the question of whether such a study is necessary to make strong inferences about the population as a whole. The difficulty of making the right study does not change our ability to draw inferences from the wrong study. (We can't.)


I am not sure how reliable these studies are, given this reporting:

https://www.nytimes.com/2020/04/24/health/coronavirus-antibo...


8,399,000 people in NYC, 21 percent of that is 1,763,790. 1% of that is 17,000.

So far as of Saturday at 6:49pm EST 16,919 have officially died in NYC.

I think the anti-body test in this case is fairly close and the death rate is probably 1% more or less depending on the demographic distribution. Obviously there are a number of people in NYC who will die over the next month even if all new infections where halted right now.

The serology tests are just wrong when only a small number of people in the sample were infected, which is what we’ve seen from the stuff in CA so far.


You're using the death count for the entire state, not just NYC. The blood antibody test positive % for the overall state is 13, not 21. And the population is around 19.5 million.


The NYC DPH reports both a confirmed and probable death count. The sum of those, as of Saturday, is 16270


People are going to use whatever version of the facts supports their bias.


? You're comparing a prison to a nursing home

Another data point, ~12k healthy foreign workers in Singapore have tested positive. The city-state has only 12 deaths total (all elderly, not the foreign workers). In Singapore the foreign workers may have already been asymptomatic for weeks.

Based on NYC data, LA city data, Stanford's Santa Clara data, Singapore, Danish blood donor data, a picture is emerging that the virus is not particularly dangerous to healthy people.


Prisoners in America are not in great health. Many untreated conditions, often inadequate health care since birth.


I'm curious if these specific diseases actually make them more susceptible to COVID or not but this is fact.

- Over half of state prisoners and up to 90% of jail detainees suffer from drug dependence.

- Hepatitis C is nine to 10 times more prevalent in correctional facilities than in communities.

- Chronic health conditions, such as asthma and hypertension, and mental health disorders also affect prisoner populations at rates that far exceed their prevalence in the general population.

- About 40% of all inmates are estimated to have at least one chronic health condition. With a few exceptions, nearly all chronic health conditions are more prevalent among inmates than in the general population. [1]

With that said, the average age is much younger than the general population [2]. Age is by far the biggest factor in outcomes, followed by co-morbidities.

[1] https://issues.org/correctional-health-is-community-health/

[2] https://www.bop.gov/about/statistics/statistics_inmate_age.j...


Also they're malnourished to the point that you can reduce prison violence by giving vitamin supplements

https://www.chicagotribune.com/opinion/commentary/ct-perspec...


In addition, concentrated and extended exposure to the virus, like what you see in prisons where it is impossible to maintain distance between yourself and other inmates during meals and in bed, is known to affect people of all ages. Look at the effects the virus has had on medical professionals who do not have access to PPE—eg Usama Riaz.


1. Most of these cases were diagnosed in the last two weeks

2. Over 5000 (roughly 40%) have been hospitalized https://www.scmp.com/week-asia/health-environment/article/30...

Data from antibody tests can so far be summarized as garbage:

- https://www.buzzfeednews.com/article/stephaniemlee/coronavir...

- https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

I guess the TL;DR of all of this is, this picture you're talking about is a fallacy premised on bad data


Is it indicated how long ago these inmates were tested? Seems like that's a key component in this mystery and I can't find it.


In Marion Correctional Institution, Ohio it could look like the tests were conducted in mid-april according to https://drc.ohio.gov/Portals/0/DRC%20COVID-19%20Information%....

But as of 25-04-2020 at least 4 inmates have died to Corona virus according to https://eu.marionstar.com/story/news/local/2020/04/24/corona.... There are 2,564 inmates (https://drc.ohio.gov/mci).


As a note on sample size, given those numbers the general fatality rate would imply eventually 25 deaths.


It is currently at 40 deaths according to https://drc.ohio.gov/Portals/0/DRC%20COVID-19%20Information%.... No specifics on age or comorbidities yet.


Also there have been reports of Ground Glass Opacities[1] in the lungs of people with infections previously thought of as asymptomatic.

[1]: https://en.m.wikipedia.org/wiki/Ground-glass_opacity


Sure, but that doesn't mean they... won't go away. In fact, I'm pretty confident all signs point to the fact they will go away. In fact you get ground glass opacities with H1N1 inflenza [1]. I guess the question is "functional asymptomaticity" vs "actual asymptomaticity". Like, if it's not bad enough for people to even notice does it really matter?

[1] https://pubs.rsna.org/doi/full/10.1148/radiol.10092240


Do those count as a comorbidity though?

If coronavirus goes around again, that could raise the death rate


There's no evidence to date that people are being re-infected. They may in the future, but to date, no such evidence exists. There are some people who tested negative before who are testing positive now, but that is much more likely to be false negatives and/or false positives.

It would be pretty novel for the human immune system to clear out the disease on it's own, then a few days later forget how to do that, and become re-infected. SARS-COV-1 saw immunity conferred for 2-3 years. [1] I suspect something similar is likely here, probably for a shorter duration due to the more limited severity, but long enough to get us to a vaccine.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/


And there's no evidence to date that people who test for antibodies are immune to future infections.


Ah my old friend greedo. That's how it normally works, this time could be different, but we have no reason to believe that.

Generally for as long as you show antibody response you won't be re-infected because that's what antibodies do. The link I provided to the study I referenced was specifically for the purpose of, and I quote: "to assess SARS patients’ risk for future reinfection."

"To be clear, most experts do think an initial infection from the coronavirus, called SARS-CoV-2, will grant people immunity to the virus for some amount of time. That is generally the case with acute infections from other viruses, including other coronaviruses." [1]

If you think this time is different the burden of proof is on you to provide studies and not provide unsupported, unsubstantiated conjecture.

[1] https://www.statnews.com/2020/04/20/everything-we-know-about...


We have no idea how long lived the antibodies we develop in response to SARS-CoV-2 last. And obviously, an initial infection to COVID-19 will generate antibodies that will immunize the patient, as long as the antibodies persist. Don't you think that if this was a foregone conclusion, we'd be able to demonstrate that? Isn't it odd, that with people having been infected and recovered months ago, that no one is saying how long the antibodies persist?

In science, it's incumbent on those making the claim to provide studies and proof. That means you...

And to say that this is unsupported, unsubstantiated is ridiculous, and you know it. It's straight from the WHO's mouth.


> It's straight from the WHO's mouth.

Nothing I said contradicts the WHO.

> Don't you think that if this was a foregone conclusion, we'd be able to demonstrate that?

I'm sorry, do we need to re-prove how the immune system works? Why re-demonstrate the utterly obvious?

> Isn't it odd, that with people having been infected and recovered months ago, that no one is saying how long the antibodies persist?

No, because it hasn't been long enough. I'm confident that research is under way.


but it would go against everything we know about viruses and our adaptive immune systems. I know there are some vaccines with lower take rates. Hep B requires 3 injections and only has a 60% change of generating antibodies.

But an immune response from an actual virus should last for at least a few years. There are situations where you can get reinfected later in life if you're not exposed or given booster shots (likes Shingles).

Is there evidences that our adaptive immune system only generates short lived antibodies, and for what families of viruses?



This should be something that can/will be resolved by testing. I find it unusual that no medical authority is going on record as saying there's any long term immunity granted by infection, and that the WHO is being extremely clear in the lack of evidence to support such a conclusion.


Greedo, no. haha. They're not on record yet because the tests are under way. Had they found an early failure that shakes the foundations of medical science, they'd have shared it. Especially in this news cycle which overwhelmingly favors negative information.

It's like saying "I find it very strange no scientists came out on record this week with a study showing water remains wet -- does it?! How can we tell if we don't check again."

Lack of proof of an affirmative is not proof of a negative, and especially not when plenty of other evidence points in the direction of the affirmative (again, not conclusively).



Nothing there is at all incompatible with what I had to say. In context, the WHO is saying that getting the disease once may not be a lifetime immunity to COVID guarantee and shouldn't be used as the basis for issuance of something along the lines of yellow fever prophylaxis certifications like these [1].

I agree. In fact, its highly unlikely, as with coronaviridae we've seen that the milder the disease the less likely you are to obtain long-term immunity. Even SARS, a much, much more serious disease, gives you 2-3 years as per my reference.

However, that's not what GP was arguing. GP argued broadly that "people who test for antibodies [may not be] immune to future infections." That's extremely unlikely. The question is how many people, and for how long, and then how do we utilize that information. Broadly speaking a positive test for antibodies means you're pretty likely immune at the time the test is taken. Of course the question is how that antibody response changes over time.

I was pretty clear about that: "Generally for as long as you show antibody response you won't be re-infected because that's what antibodies do."

The WHO is saying don't issue one-off certificates of immunity for life on the basis of testing positive for antibodies at one point in time before we know more. I agree.

I suspect a round of infection is likely to tide us over to a broad vaccination program, but we need a study.

[1] https://thegate.boardingarea.com/wp-content/uploads/2016/04/...


This is crystal clear from the WHO:

""There is currently no evidence that people who have recovered from COVID-19 and have antibodies are protected from a second infection.""

They were prompted to issue this because some people were touting this idea of immunity being granted perpetually and allowing people to safely return to work.

"Broadly speaking, a positive test for antibodies means you're pretty likely immune at the time the test is taken."

That's in complete contradiction to what the WHO is saying. Read carefully: There is no evidence.

You're using circular arguments to provide bad information. Something you've consistently been doing.

"...but we need a study."

Why? You've said it's unlikely to be different than other viruses. Of course we need a study, because we don't know.


No greedo, that's not the correct interpretation.

There is currently no evidence of X does not mean X is not true. It just means there's no evidence of X being directly true yet. Nothing I said contradicts the WHO.

What I said was that we can reasonably infer from similar coronaviruses (including both more and less severe ones that are up to 90% genetically identical) that immunity is conferred. Also from other viruses. We shouldn't base our global health policy decisions on that until we have conclusive evidence but there's no reason for you to continue with the messaging when all evidence points to immunity being conferred for some duration of time.

Specifically what I said was that we do not have enough evidence to issue prophylaxis certificates, but that chances are good immunity is conferred based on studies of very similar diseases. I also stand by the fact it would be hugely surprising (totally novel) that any of those testing positive right now are actually re-infections due to the limited timescale involved.

Seeing smoke doesn't mean there's fire, but it means there's a pretty good chance of fire. Yeesh.


[flagged]


All evidence points to (i.e. implies) but does not prove conclusively yet because studies are under way. Is there some disconnect in your reading of this? This is absolutely how science works. You identify something likely to happen due to a preponderance of evidence then you attempt to prove or disprove it by study. This is called inductive reasoning, and it's the basis for what's known as a hypothesis. An experiment or study is then conducted to prove or disprove your hypothesis.

You have not brought any evidence to the table. If there is a study that says SARS-COV-2, unlike the majority of (all?) viruses and all coronaviruses that results in immune response sufficient to clear the disease that then immediately dissipates, I'll certainly accept the premise.

Until then, a preponderance of evidence points (or suggests without proving conclusively) otherwise.

> That's crystal clear, but it doesn't align with your opinion that this is just like the flu in seriousness.

That is not my opinion. My opinion is that it's milder than the flu for young people (it is) [1], and much worse than the flu for older folks (it is) -- no citation needed, I assume. To suggest otherwise would be to ignore the evidence you claim to hold sacrosanct.

[1] https://time.com/5816239/children-coronavirus/


Hi Artic, here's a new study you might find interesting. It seems to disagree with your contention that immunity is normal for coronaviruses.

https://www.technologyreview.com/2020/04/27/1000569/how-long...

wavegeek 35 days ago [flagged]

> I'm pretty confident

powerful argument there, not.

With SARS1 there was continuing damage post 6 months.


SARS-COV-1 has a two orders of magnitude higher fatality rate, so one would imagine the damage would be substantially worse. Is it really a stretch to believe that level and quantity of damage correlate both to recovery time and to mortality rates? Further, were there asymptomatic SARS-COV-1 cases?

SARS-COV-1 had an IFR (not CFR) of 14-15%. Broken out, it's less than 1% for people younger than 25, 6% for those aged 25 to 44, 15% for those aged 45 to 64, and more than 50% for people 65 or older, officials said. [1]

On the other hand SARS-COV-2 has an IFR of somewhere in the lower quartile of the range 0.1% to 1%, trending to around 0.3%.

Not to mention, I argued that lung function would recover, to which you said "strong argument, not [the much worse disease saw lung function recover in 6 months]" which implies you were actually supporting my argument not refuting it.

The coronaviridae family is huge, and fatality varies from ~0% in the 15% of common colds they cause to 0.1-1% for COVID to 15% for SARS-COV-1 to 50% for MERS. I can't stress this enough. SARS-COV-1 and MERS are not SARS-COV-2, they are much worse diseases.

[1] https://www.cidrap.umn.edu/news-perspective/2003/05/estimate...


...Let's also ask if the tests have a high false positive rate?


Dr. David Katz was just in Bill Maher’s show indicating the test has a high false negative rate


That doesn't rule out a high false positive rate. The false positive rate can be even higher than the false negative rate.


Totally agree. The difference being a high false negative rate may be more dangerous because it may mean asymptomatic false negative carriers are still spreading the virus. The downside of a false positive is that people are self quarantining needlessly.

For a patient, a high false negative rate is usually worse. For an insurer, a high false positive rate is usually worse. The perspective matters.


> The difference being a high false negative rate may be more dangerous

At an individual level, false negative seems more dangerous. But at a macro level, a high false positive rate could lead to taking dramatically policy decisions


Sorry, I was editing my comment to add that very perspective just as you replied it seems.

I agree with the caveat that false negative rate is much more relevant for society when a disease is very contagious. Take measles, with an R0 in the teens. A high false negative rate can cause explosive growth in the numbers of people catching the disease, which I think is what makes it relevant to the COVID-19 scenario


What appears to be the potential danger of a high false positive is the narrative that it's safe to end stay-at-home because everyone already has/had it. Which seems to be the story being pushed with any talk of a lot of positives.


Nope, the narrative is "the mortality rate is similar to flu, therefore draconian measures aren't necessary".

Implementation examples: South Dakota and Sweden.

It seems clear that the infection rate has been severely under counted, meaning mortality rates are artificially high. Even better (from the standpoint of restarting) that "flu-like" mortality rate is concentrated in people over 60 years old.

What that all means together is the economy can easily restart, with the most vulnerable (old and sick) taking extra precautions.

This entire thing has been a fascinating exercise in how poorly central planning can work given bad information. The cure has been vastly more damaging than the disease.


Good point


It may have both for all we know. I really do think relatively little is known about this virus at the moment. We learn a lot everyday I'm sure, but it's still somewhat of a mystery. We don't like mysteries, which I'm pretty sure is what generates all the fear around the virus.


The evidence shown is a false negative rate of 30% based on a study of 1,000 patients with the virus. So there’s at least some preliminary evidence.

https://www.medpagetoday.com/infectiousdisease/covid19/85717...


The false negative rate isn't that relevant on this context, and has no obvious relation to the false positive rate.

Besides, the article you pointed down in a reply is about a different kind of test.


I was under the impression the PCR test was a better test. Is that incorrect?

I do disagree that a false negative rate is not important in the context of a disease where asymptotic cases may still be contagious, however.


Even more relevant to this observation is that false positives aren't properly random: if the test happens to be falsely positive for some other rare virus it's very much possible that the entire prison was hit by that virus, creating a cluster of connected false positives.


Want to make a bet they won't develop symptoms? Or at least please help me understand how can you compare these two populations with wildly different average ages, preexisting conditions, etc...


Is this not just another Diamond Princess Cruise?


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