Covid-19 twice as contagious as previously thought – CDC study | Hacker News Reader

Covid-19 twice as contagious as previously thought – CDC study

thinkpol.ca

264 pointsdeegles295 comments

I will repeat a question I would love an epidemiologist to reply to: Where is the random background testing? Not testing "I feel unwell" presentations at random, truly going into the field and testing the wider population at random, healthy (believed) or not?

You cannot model the IFR accurately from skewed samples and it feels like we aren't even doing skewed sampling.

It's in South Korea where there is an abundance of tests. Where tests or people to administrate them are scarce, we have to prioritize those with symptoms.

There is an argument to me made for the exact opposite. Those who are already sick will be treated according to their symptoms, a diagnosis has little bearing on the treatment they will receive and therefore low value. Meanwhile, proper random sampling of the population will give more accurate prevalence numbers and trends, allowing for much better epidemic response.

South Korea (and Singapore and HK etc.) had a specific policy of contact tracing instead of random sampling, which works if you start it early enough and can actually keep ahead of suspected contact spread.

Sick people should be tested when they require medical intervention as it guides their care. It would be a shame to assume COVID when it might be something treatable.

I'm sure the decision tree is complex, and there are branches where what you say is absolutely true. However, given the testing kit bottleneck, if identification of "something treatable" is crucial, it might be better to test for those treatable diseases that are likely given presenting symptoms.

In the extreme case, if you require a ventilator right now or you'll die, nobody needs to wait on verification of the specific viral cause to begin treatment.

"Those who are already sick will be treated according to their symptoms, a diagnosis has little bearing on the treatment they will receive and therefore low value."

It is not low value for the healthcare providers, though, who can spare measures against getting infected to those who actually have COVID and thus both save time and scarce resources.

The cost of false negatives would be too high. Hospitals have to treat all respiratory cases as potential COVID right now, even if initial tests are negative.

Both Munich and Prague have started such studies with several thousand random people in each. That should give us a much better estimate for the number of "silent" cases.

There are a bunch of serological surveys going on right now in the US: https://www.sciencemag.org/news/2020/04/unprecedented-nation...

Santa Clara county did surveillance testing which likely saved California https://www.cdc.gov/mmwr/volumes/69/wr/mm6914e3.htm despite the shortage of test kits. Just imagine if the nation had sufficient tests available.

"During March 5–14, among patients with respiratory symptoms evaluated at one of four Santa Clara County urgent care centers serving as sentinel surveillance sites, 23% had positive test results for influenza. Among a subset of patients with negative test results for influenza, 11% had positive test results for COVID-19."

Seems to me like Santa Clara was just testing sink people to me

It continues:

At the time the investigation began, testing guidance recommended focusing on persons with clinical findings of lower respiratory illness and travel to an affected area or an epidemiologic link to a laboratory-confirmed COVID-19 case, or on persons hospitalized for severe respiratory disease and no alternative diagnosis (1).

At the time, the standard was to test people with travel history to affected areas or when they were hospitalized for unexplained respiratory illness.

Compare that to testing people that went to urgent care because they felt sick. There's a difference.

Still, there's no reason to say that any of these selection criteria result in sample sizes big enough to be statistically relevant against larger populations. Also, testing "people with travel history to affected areas" does not amount at all to being a representative segment.

Santa Clara's results are basically only useful for Santa Clara. _Maybe_ the Bay Area in addition, but not really any farther.

So, we DO need surveillance testing, on a much wider scale, across the US and globally to be frank.

The poster up thread is claiming that the surveillance testing in Santa Clara lead California to act as if there was community transmission.

That's super useful information when most testing is using restrictive criteria like travel to affected areas.

They aren't arguing that it was enough, they are pointing at it as an example of what broader testing can help do, if only it existed.

So I don't really understand your refutational tone ("Still", "relevant", "only useful", "DO", etc). Is one of us misunderstanding the flow of the thread?

You are getting downvoted because you're arguing for global surveillance, which is not a popular topic on this board. And that doesn't include some real fears of actual dictator-ish moves, with COVID as a justification.

i'm not scared that hungary might use covid-19 as a justification to abandon democracy in exchange for dictatorship. i see they did. an observation is a terrible thing.

Test a group known to be 10x more likely get infected and you can get meaningful results with a much smaller sample size. 'sentinel surveillance sites' to try and catch the wave before it sweeps through the general population.

Testing people without symptoms would likely result in getting nothing given the limited sample size. The point is that the sampling was done with less bias, without the preconceived notion of who may actually have it (at least among fever patients).

Dr. Fauci addressed that the other day when asked about when schools might reopen. He hedged a lot, but basically said he thought it could happen for the Fall. At the same time he was talking about that as the time frame for wide-spread anti-body testing that would show just how far the virus had penetrated. (And that such testing and continued contact tracing etc. would be what facilitated cautious steps towards reopening society)

I think Iceland is aiming for full population test

This is probably what you're looking for: https://www.eurosurveillance.org/content/10.2807/1560-7917.E...

This, unless we do that there is no way to establish true IFR so we wont know until its in hindsight how deadly the covid is? I dont know why this is not the priority 1 for the CDC.

on the similar lines, Germany is doing something clever, they are mixing samples to do a kind of binary search with covid tests so they can clear a large number of people with few tests. we should do that without waiting for antibody tests etc. it should be noted that this approach would only work if the infected sample set is very small so the window of doing this is going to close soon.

> I dont know why this is not the priority 1 for the CDC.

What actions would we do differently based on the result? Not knowing is frustrating for everyone, but we should save our resources for stuff that's actually going to improve outcomes.

IFR is a really important peice of information as it effects the overall management of outbreak. consider these scenarios: A) IFR is really low and most people dont seen anything past slight discomfort, so given the number of 'cases' being treated now it would mean a much larger portion of populus is already infected. translation we can reach herd immunity faster & more safely. B) IFR is high (closer to CFR), which means the current case count is more or less in line with actual infected people. which means we really need to focus on lockdown and slow the growth so we can manage the hospital capacity.

as you can see both of these scenarios have different optimal responses but we cannot decide unless we get this info. so far we only have a few biased sample sets (from us pov) diamond cruise & skorea which dont shed much light on our situation.

Testing should be saved for two purposes:

1) to guide care in a hospital setting. 2) to determine the current status of the population.

Randomized testing would give us a view into where the virus is spreading and allow us to allocate resources effectively. It would also give us a view into what percentage of a given population is a carrier, or has already recovered. All of these data would be greatly helpful. What isn’t helpful is using tests on people who aren’t either part of a study or under active care.

Literally every epidemic model being used to justify lockdowns depends on IFR as a critical parameter, and the best we can do right now is guess at the value.

If the IFR is 1/10th (quite possible!) or 1/50th (less likely, but still possible) of the current estimate, it completely changes the calculus for locking things down. A fifty fold under-estimate of infections would mean that approximately half of NYC has already been infected, for example, and that we're not far from herd immunity.

We just don't know.

> Germany is doing something clever, they are mixing samples to do a kind of binary search...

Do you have a link for that? I can find some papers suggesting you can detect one positive sample mixed with 31 negative, but nothing about that being applied.

Just googled this and I found a discussion here: https://twitter.com/koeppelmann/status/1245037948099330048

"Testing capacity in Germany will be increased by up to factor 10 to up to 400,000 a day (!) by doing pooled testing. E.g mix 16 samples and if negative - all are negative, otherwise binary search for the positive(s). Could of course be used worldwide."

He quotes a tweet with a link to this study: https://idw-online.de/de/news743899

Not an expert, but this is something I've been wondering about too. But then I had another thought, What happens if people refuse the random test. Even if those who refuse are only a small amount of people who get drawn in the covid testing lotto, that likely introduces a serious compounding variable.

So then the question is, do you go out into the general population with law enforcement, and force people who were drawn in the testing lotto to be tested at gunpoint if they don't comply? I think the answer is that we don't have the will to do that. And perhaps even if this would be incredibly valuable data, it would be morally wrong to gather it in the way we'd have to gather it to have a truly random sample without such a confounding variable.

I am not a doctor, not an epidemiologist. Just some random thoughts.

Just pay them, e.g. 500USD each, or however much is enough to reduce the number of refusals to a negligible proportion. It's cheap compared to the cost of making incorrect decisions because you don't know how many people have the virus.

That's a perverse incentive. You'd have to pay everybody $500 each, not just the refusals, since everyone is going to refuse in order to gain $500.

You can even refuse 500USD in hope to get 1000USD...

60-80% of people routinely hang up when being called for political surveys, yet surveys still manage to get within 2 or 3% of election results. This is a non-issue.

Nope. They’re often off by 7-11%. As evidenced in many elections.

Refusals should only matter if they correlated with infection (or lack thereof). I don't see why that would be the case here though.

Really? You don’t see any way that someone who wouldn’t be willing to let the government conduct a test on them wouldn’t also be less likely to follow CDC guidelines on social distancing, etc?

90% of people will comply. Let's say it's only 50% though. Those people are no more or less likely to be infected. So it's 100% perfectly fine not to test everyone if the goal is to determine the number infected overall.

If looking to go beyond, hey let's get testing capacity to the point where those who are sick and desperately want to be tested can be tested, then we can attack the 1984 fantasy.

"But but what if I can't force those to be tested who don't want it" is an absurd and insane concern given that only a tiny minority of people can be tested who desperately want to.

Once we have testing capacity then those who want to force testing should go out and try to force people to be tested and happily accept whatever happens to them.

You are reading a lot of stuff into my comment that is not there.

1. I’m all for more testing, and would gladly be tested. 2. You really don’t think the cohort of people who would refuse testing wouldn’t also be less likely to follow CDC guidelines? This increasing the chance they are infected. 3. All I was trying to say, was that random testing will be confounded by things like people refusing. Perhaps it gets us close enough. Perhaps people who refuse are more likely to be infected. Or maybe less likely, because they are loners who don’t go out thanks to their doomsday prepping or something. I think it is worth being aware of.

You seem to be incredibly confident that the cohort who would refuse testing will have the exact same infection rate as everyone else. Care to share why you are so certain of that?

So far only PCR tests are available at scale. Those PCR tests are very sensitive (e.g when detecting traces of long broken virus on surfaces) but even they are only reliably positive for a few days into the infecting. Apparently, the immune system wins the battle for the upper throat quite easily, even in patients who later die from pneumonia. If you did a random sample study with a test that only identifies a positive for a short time you'd still have a huge uncertainty from not knowing reliable averages about how long someone infected "lights up" in the test. There just isn't much information upside from background sampling PCR, particularly when serological tests (those than detect who already had the virus) will soon be available. Those are not useful at all to combat spread anyways, so they can be used for background testing without competing for more immediate use cases.

At the moment, that data would not make any significant difference on the actual decisions that are made today. Or can you think of anything you would change with the knowledge that 20% of the population have been infected, versus 2%? As long as thousands die each day, there really is no alternative to the current strategy.

It would possibly help planning for the next phase. But mostly it would feed our curiosity.

Contrast to using limited tests to find the greatest number of infected people: every additional person knowing without a doubt that they are infected is one more person doing their best not to spread it further.

It’s wasteful to test randomly until we are able to test high risk groups every day or so. Cashiers, for example are at high risk of infection, and ha e possibly hundreds of contacts per day.

For population-randomized testing, antibody tests should be far superior anyway. They allow detection of. It just active cases but also past infections.

Sure, let's just keep the economy closed, who cares if the death rate is 10%, 1% or 0.1% (seasonal flu), what difference does it make right?

Great job not getting it. Hope your unique skills are transferable to viruses.

Epidemiologist here: you're right.

Testing resources are in short supply, so testing is being performed to guide clinical decisions (ie sick people) rather than public health/science (ie random sample).

The handful of serosurveys that have been performed have been quite valuable, but there aren't nearly enough.

Hopefully this changes as testing capacity ramps up.

When the clinical demand wanes, I hope there will be extensive antibody testing in various regions to do a comparison between how many people actually got it vs. how much damage it did. If, as I suspect, there are significant differences (ex: almost everyone in this town seems to have the antibodies, but nobody died, vs. a third of these people have the antibodies, but a lot of people died), we might be able to learn things such as which public policy decisions mattered most and maybe even which ethnicities are more vulnerable or resistant genetically. If a genetic variation is found, it might even lead to a new generation of preventative therapies that go beyond vaccines to shield people against viruses that don't even exist yet.

Not really.

There are a million reason for differences like that, without studying the problem extremely carefully, you'll never know which of those reasons was consequential. For instance, more people may die in a certain city because there is more pollution in the air, or the concentration of fluoride in the city's water supply is higher, or maybe that city houses a large meat packing plant and an abnormally large number of residents had to roll the dice and work throughout the lockdown. The number of possibilities are nearly endless, and you'd need to eliminate them prior to drawing any conclusions.

Even ethnically, you can't really draw any conclusions. Were people of this ethnicity or that ethnicity more likely to be essential Walmart stockboys than others? That one little detail can have an enormous impact on medical outcomes depending on the contagiousness of a pathogen like covid-19.

To draw any reasonable conclusions, you'd really need far more than just a comparison between regions, because the different regions have such different public health needs and environmental realities. You'd even need more than a simple comparison between ethnicities. You would need to go wayyy more deep. But I can guarantee that researchers will dig deep to find anything that can be used attack similar pathogens in the future.

> without studying the problem extremely carefully, you'll never know[...]

It seems likely to me that this problem will get studied extremely carefully.

As I said, test for antibodies. Put the "who's more likely to work at Walmart" speculations aside and start with, "Of the people who were provably infected, how likely were they to die?" question. If there are large differences, then look at age (for sure), prior health factors, gender, ethnicity, etc., to see what (if anything) made the virus not matter, and consider whether there might be other ways (besides vaccination) to make future viruses (prob some category) not matter to anyone.

I'm convinced this is my aliens theory, but I do think pollution plays a big role. My hypothesis being that long term exposure to high pollution is akin to chronic asthma.

Fits in Italy, as their elderly are retired locals exposed to their terrible air for life. In China, the hard hit had a transient population that moves away on retirement. In New York, it is hitting more men, who are proportionally more likely to with outside jobs such as construction. That is to say, all of the hard hit groups had long term exposure to pollution.

And again I stress. I view this as my conspiracy theory. Really want to not believe it.

Firefighters have an inordinately high rate of lung cancer, even though comparatively few smoke (as their job comes with physical fitness requirements). This rate of cancer is believed to be tied to regular smoke exposure.

On a lower, slower timeframe I wouldn't be surprised if pollution had the same effect re: weakening the lungs.

Follow up question: are you guys seeing a drop in norovirus, rotovirus and D&V? I would expect social distancing has reduced opportunistic infection rates: no swimming pools, less interactions at large.

I have anecdotal evidence from a hospital saying that wards usually reserved for norovirus and C. difficile at this time of year have not been required this year due to the lack of those infections.

the Swedish state epidemiologist used that measure as a way of measuring the effectiveness of the social distancing and quarantine measures.

apparently, the number of people receiving hospital treatment or dying from yearly virus infections like the flu has dropped significantly.

Do you have any insight into the validity of the Nasopharyngeal covid19 PCR test? I suspect I got a false negative. Get to have a "conversation" with HR tomorrow since I am essential.

For samples with coronavirus particles, sensitivity and specificity are around 100%. (1)

The issue is that many samples do not contain virus. As far as I know, that has not been quantified outside of China. (2)

1. https://www.fda.gov/media/136151/download

2. https://www.mdmag.com/medical-news/comparing-rt-pcr-and-ches...

Single swab tests may be inaccurate, nasal, throat and anal swabbing is usually recommended for good coverage.

The PCR test itself has near 100% sensitivity and specificity when done correctly.

Poor swabbing or contamination can result in false negatives or positives.

Manual PCR testing is known to be quite prone to human errors hence why the all in one cassette kits are quite popular.

I really appreciate this answer, and as a lay person, it helps me put some pieces together in my mind. I suspect there are at least four reasons for testing, and they all benefit from better results, but two of these need good quality unbiased general statistics, and the other two only need high confidence negative/positive results for specific cases:

- Policy makers weighing the strategic pros/cons to orders for the public

- Individuals (either frightened or skeptical) weighing their decisions

- Physicians treating patients

- Quarantining people based on contact tracing

Can surveillance testing not be done using standard laboratory reagents (ie not FDA approved)? (I suppose the rather invasive sample collection protocol might pose a bit of an issue though.)

Some studies are looking into performing tests on sewage to estimate the number of infected people in an area.

There are ways of doing population wide testing that are not very invasive.

Foolish. Short-sighted. Informed policy was, and is, more important than a couple thousand more clinical tests.

If policy had been informed, the number of people saved would have out-weighed the handful who died due to missing out on a test.

Hard to make this decisions in smaller contacts when you are potentially endanger real patients needing care right now Vs future "less dead" estimations.

At this point do we even need to test most of the serious cases. if it walks like a duck, quacks like a duck, coughs like a duck, it's a duck. Didn't China start counting cases based on CT scans at one point too?

China is not forthcoming with the truth, at all.

Health systems deal with scarcity on a daily basis. There's no room for the emotional hand-wringing you're describing. Moving scarce resources from diagnosis patients to studying the population during a pandemic will be one event in a causal chain that results in deaths. It also prevents the outbreak from spreading beyond control.

Several orders of magnitude more people have been killed by uninformed policy than would have been killed by redirecting a portion of tests. What kind of MONSTER chooses for so many more people to die?!?

If you don't test a patient that's positive and put them with other patients and unprotected health personnel you'll infect others and significantly weaken the hospital's ability to treat patients. If you don't test a patient that's negative and put them with covid patients they risk getting infected, taking up an extra ICU spot, and might die. Would you want one of your loved ones in that position?

What would change in public policy with more randomized tests?

A full nationwide shut down without waiting for individual states to shockingly have the same issues as every other state was one possibility.

By comparison many ill people are currently being told to stay home until they have difficulty breathing, making tests have minimal clinical value. Especially as the risk of false negatives are significant.

AFAIK, they are different tests:

* PCR tests to check if you are carrier of the virus right now. That's what you do with patients and personnel.

* Anti-body tests to gauge the number of people having been exposed to the virus in the past.

It takes a couple of days to develop anti-bodies so you don't want to use that one for the first use-case. And you can have anti-bodies without carrying the virus. (Well, it depends on the type of antibodies)

If you know the exposure in the general public, we would better know the real mortality, how far we are with herd-immunity, and if general quarantines do make sense, or it might be sensible to be more selective.

Do you know what's even worse for the hospital's ability to treat patients? If the city continues to operate normally until the number of cases is so large that it's obvious the outbreak is unmanageable even without testing.

Randomized tests would have told policy makes exactly how fast this spreads, even in Western cities. Lockdowns would have happened earlier. The total number of infected would be 1-2 orders of magnitude lower. Hospitals would have been better off.

>Would you want one of your loved ones in that position?

I wouldn't want one of my loved ones to die because my civilization was so short-sighted that it let a disease run rampant. Your appeal to emotion is garbage, and it doesn't even make sense, since all of our chances would be better if we'd known what was going on.

Soooo....

- SARS started out with a similar <4% estimated fatality rate and was then revised upwards to anywhere from 9-15% later.

- COVID-19 is caused by a different strain of the same virus as SARS.

- The CFR of SARS and COVID-19 appear to be very similar, and more notably, appeared by be very similar when we had around 8,000 infections which is where SARS ended. (Similar meaning the CFR hovers between 4% and 20% of closed cases.)

How are we so sure that this is any less deadly than SARS?

The above suggests to me one of the two is likely to be true:

1. There could have been many more undetected cases of SARS than we knew about, indicting an IFR much lower than it's recorded CFR.

2. COVID-19 could actually end up having an IFR that is similar to SARS (~10%).

But of course, I am no epidemiologist, so I assume there's a flaw in my logic.

Did I miss something, or is this pretty much the same disease as SARS but with a higher R0?

There's little to no data that backs up your guess.

Studies on COVID-19 estimate that the true IFR is somewhere between 0.1% to 0.39%...why? Because the more we test, the more we find asymptomatic and mild cases. And we're yet to even do the type of serological testing that would give us such a decisive sample. Yet we're ALREADY seeing data that suggests that the IFR is lower than SARS and asymptomatic cases/transmission are common. [0]

I never read anything about SARS being mild or asymptomatic in the majority of patients...in fact it was the opposite. It was so severely symptomatic that the virus killed itself with natural selection. If you had SARS-COV-11 and exhibited high viral load, you were likely too sick to go spread it. The only serological testing they did on SARS-COV-1 deemed asymptomatic cases to be uncommon [1]. Quite the opposite of COVID-19.

0: https://www.cebm.net/covid-19/global-covid-19-case-fatality-...

1: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035549/

This is good news. Thank you.

I am not convinced that this data is conclusive yet.

In two closed environments where everyone was tested, the Diamond Princess and the Washington Choir, the CFR is currently 1.55% and 4.44% respectively, with still cases in critical condition.

Of courses both populations are older, but it seems safe to say that the IFR is much higher than the numbers you quoted for older populations.

What your safe to say claim misses however is that the CFR drops off dramatically below a certain age, and very dramatically, especially if you compare between people in their mid 40s and blow to people in their mid-60s and above. Thus, your own mention of the populations of both cruise ships heavily tending towards elderly passengers (and infected) would almost certainly skew the CFR very strongly upward, and even with this upward skew, it's still so far resting at a fairly moderate 1.44% in the case of the Diamond Princess. As for the Washington Choir, not sure of why its CFR is almost three times higher, perhaps more of the passengers were older than in the DP? Either way a look at all current aggregate data on fatality counts by age from nearly any region of the world you'd like to look at clearly shows that same near drop off a cliff as soon as you reach age groups below 60.

Agree for the most part, especially with the claims with respect to SARS having a higher IFR

However, given the high R0 and still somewhat high IFR for older populations and even a relatively high IFR compared with the flu IFR for younger populations this virus still has the potential to wreak havoc.

What we are seeing is that a disease with a high R0 can be much more deadly than a disease with a lower R0 and higher IFR.

Did we ever do randomized tests due SARS? You linked one, but I would presume more would be needed for a decision?

The main study which backs up his guess is the report from the WHO's trip to Wuhan, where they actually did what was suggested above and mass-test random samples from the population, finding a very low incidence of positives. Personally I would find the WHO on a trip to China as not the most reliable of sources, so take it with an enormous grain of salt. But it's one data point.

Meanwhile, your link in [0] flat-out says we don't know the percent of asymptomatic cases. It is well-documented they exist. I have also seen some papers estimating the percent of cases that are asymptomatic as well under 20%, and as high as the 80-90% range, the difference between them making an enormous difference to the implied mortality rate.

A month ago, people pointed to South Korea's 0.5% fatality rate, a country which tests very aggressively, as an indication that the true fatality rate is much lower. Well, they still test just as aggressively, and now their mortality rate has risen to 1.96%. The disease started spreading in a population that skewed young there, and but now the disease has started to hit older demographics who have a much higher mortality rate. And of course, "asymptomatic" cases can also mean pre-symptomatic cases, as happened with the Diamond Princess, which had an observed 46% of positive cases be asymptomatic...only to have this rate fall to ~17% later.

Yeah basically the extreme correlation of mortality with age makes it hard to state a mortality rate. Small changes in the population age distribution create big changes in mortality.

I've posted this in an earlier thread but I don't think that low estimates make sense. The virus killed 0.44% of total population of Bergamo

4500/1112187

https://translate.google.com/translate?hl=en&sl=it&u=https:/....

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

EDIT: and there are harder hit communities: 400/40000 https://www.ecodibergamo.it/stories/valle-brembana/il-grido-...

take the age bias into account. 45 for Bergamo and 29.6 for the world.

This first link is utter nonsense. It bases on things like 6 people having died on Diamond Princess. That number is now 12, and growing.

I’ve seen these reports but I don’t understand the math. The true simple CFR meeting actual deaths as reported over actual cases as reported should be adjusted for the course of disease which would make it in places like Italy well over 10% and in other places perhaps 4-6. % or more and we know China’s data, the only place with a major outbreak where it’s really run its course, is bullshit. How do you get to 0.1%? Maybe I’m missing something here but you’d have to believe there were probably 50-100 times the Number of reported cases that were actual cases which would mean the US would have to have had 50-100 million people already have contracted the disease but 98-99 pct of them had no idea. I’m not seeing that as a passes the smell test possibility. But again maybe I missing something here.

> Did I miss something, or is this pretty much the same disease as SARS but with a higher R0?

Yes, SARS-1 and SARS-2 (Covid-19) are more similar than not. But debating IFR, CFR or R0 with the testing data we have is pointless.

You can learn a lot about the nature of SARS-1 by reading about the 2002 warzone in Toronto hospitals, which wiped out whole ICU teams. Sounds just like corona today in Italy or NY.

I think there are also several issues that are causing the reported CFR to be understated, that counteract to some degree the “denominator problem” from undiagnosed mild cases. The one that I think is the most important is the simple understanding of cohort analysis which I just don’t ever see explained or included any calculations. The number of cases is rising exponentially, and there’s a six week course roughly to the disease, so the number of deaths at any point in time is six weeks behind the number of diagnosed cases at any point in time and the number of cases is increasing exponentially, so this seems like a big error. I have to say I’m truly surprised by how we are this far into this thing and it doesn’t seem like anybody has even done some of the basic mathematical maneuvering you would need to do to understand this or at least they haven’t done that and explained it very publicly.

Did we miss the chance of going deeper on the Diamond Princess? Maybe a full, in depth check on everyone in the ship would be the ideal approach.

Everyone on the Diamond Princess was checked. That's why it keeps being cited as a closed population with statistically valid sampling (100%). It's the re-projections from that ship onto the age range of the general population that yields low fatality rates comparable to flu.

If I was on the diamond princess during this outbreak and in a balcony room, that balcony door would have been wide open the entire time.

> . It's the re-projections from that ship onto the age range of the general population that yields low fatality rates comparable to flu.

Using old data that misses fully half of the deaths so far... (and there are still people in serious condition).

For SARS resolved CFR was a more accurate estimator at all points in time: https://academic.oup.com/aje/article/162/5/479/82647

We clearly saw this coming. In fact, in 2006 President Bush signed “National Strategy For Pandemic Influenza, Implementation Plan”, Homeland Security Council, May 2006. As an epidemiologist, what do you think went wrong? How come we are caught off guard? Here is a comparison of our policies and fight against the Spanish flu and Covid-19. Remarkable how stay-at-home and social distancing policies worked then too. https://thepeel.news/stay-at-home-orders-worked-a-century-ag...

My grandparents were dating at the time of the Spanish flu. They lived in a little town in the middle of Iowa and practiced social distancing by having their dates at my Grandmother's home, doing quilting, instead of socializing with others.

It strikes me that R0 could be a very rubbery figure because surely it's dependent on the social setting. An R0 of kids crawling over each other in a kindergarten must be different to an retired estate where most people spend their time in their own house.

The final R0 is I guess is how all those values average out in a society. Is it normal for all the different societies around the planet to have the same values?

R0 is the expected number of cases generated by one infected case in a susceptible population. Usually derived through mathematical models, it is not a measure / rate of transmittablity or infectivity of the pathogen, but an indicator used to predict severity: R0 <= 1 implies the disease is endemic, otherwise; an epidemic.

R0 is dependent on parameters taken into consideration and the model itself. R0 makes sense only in the context laid out by the model.

> An R0 of kids crawling over each other in a kindergarten must be different to an retired estate where most people spend their time in their own house.

Ref: https://news.ycombinator.com/item?id=22818413

> The final R0 is I guess is how all those values average out in a society.

See: https://en.m.wikipedia.org/wiki/Basic_reproduction_number#No...

Study from the first community transmission center in Germany. Brand new. https://www.land.nrw/sites/default/files/asset/document/zwis...

Preliminary results and conclusions of the COVID-19 Case Cluster Study (Gangelt municipality)

Prof. Dr. Hendrik Streeck (Institute for Virology)

Prof. Dr. Gunther Hartmann (Institute for Clinical Chemistry and Clinical Pharmacology, Speaker of the Cluster of Excellence ImmunoSensation2)

Prof. Dr. Martin Exner (Institute for Hygiene and Public Health)

Prof. Dr. Matthias Schmid (Institute for Medical Biometry, Informatics and Epidemiology)

University Hospital Bonn, Bonn, 9 April 2020

Background: The municipality of Gangelt is one of the places in Germany most affected by COVID19 . It is assumed that the infection is due to a carnival session on 15 February 2020, as several people tested positive for SARSCoV2 in the aftermath of this session. The carnival session and the outbreak of the session are currently being investigated in more detail. A representative sample was taken from the community

Gangelt (12,529 inhabitants) in the Heinsberg district. The World Health Organization (WHO) recommends a protocol in which, depending on the expected prevalence, 100 to 300 households are randomly examined. This random sample was coordinated with Prof. Manfred Güllner (Forsa) to ensure its representativeness.

Aim: The aim of the study is to determine the status of SARS-CoV2 infections (percentage of all infected persons) in the community of Gangelt, which have been and are still occurring. In addition, the status of the current SARS-CoV2 immunity shall be determined.

Procedure: A serial letter was sent to about 600 households. In total, about 1000 inhabitants from about 400 households took part in the study. Questionnaires were collected, throat swabs taken and blood tested for the presence of antibodies (IgG, IgA). The interim results and conclusions of approx. 500 persons are included in this first evaluation.

Preliminary result: An existing immunity of approx. 14% (antiSARS-CoV2 IgG positive, specificity of the method >.99 %) was determined. About 2% of the persons had a current SARS-CoV-2 infection detected by PCR method. The infection rate (current infection or already been through) was about 15 % in total. The case fatality rate in relation to the total number of infected persons in the community of Gangelt is approx. 0.37 % with the preliminary data from this study. The lethality rate currently calculated in Germany by Johns-Hopkins University is 1.98 %, which is 5 times higher. The mortality in relation to the total population in Gangelt is currently 0.15 %.

Preliminary conclusion: The lethality calculated by Johns-Hopkins University is 5 times higher than in this study in Gangelt, which is explained by the different reference size of the infected persons. In Gangelt, this study covers all infected persons in the sample, including those with asymptomatic and mild courses. In Gangelt, the proportion of the population that has already developed immunity to SARS-CoV-2 is about 15%. This means that 15% of the population in Gangelt can no longer become infected with SARS-CoV-2, and the process has already begun until herd immunity is achieved. This 15% of the population reduces the speed (net reproduction rate R in epidemiological models) of a further spread of SARS-CoV-2 accordingly.

By adhering to strict hygiene measures, it can be expected that the virus concentration in a person infected can be reduced to such an extent that the severity of the disease is reduced, while at the same time immunity is developed. These favourable conditions are not given in the case of an exceptional outbreak event (superspreading event, e.g. carnival session, après-ski bar Ischgl). With hygiene measures, favourable effects with regard to total mortality can be expected.

We therefore expressly recommend implementing the proposed four-phase strategy of the German Society for Hospital Hygiene (DGKH). This strategy provides for the following model:

Phase 1: Social quarantine with the aim of containing and slowing down the pandemic and avoiding overloading critical supply structures, especially the Health care system

Phase 2: Beginning of the withdrawal of quarantine while ensuring hygienic conditions and behaviour.

Phase 3: Lifting of the quarantine while maintaining the hygienic conditions

Phase 4: State of public life as before the COVID-19 pandemic (status quo ante).

(Statement of the DGKH can be found here:

https://www.krankenhaushygiene.de/ccUpload/upload/files/2020... adug_Lageeinschaetzung.pdf)

Note: These results are preliminary. The final results of the study will be published and presented to the public as soon as they are available.

"By adhering to strict hygiene measures, it can be expected that the virus concentration in a person infected can be reduced to such an extent that the severity of the disease is reduced, while at the same time immunity is developed. These favourable conditions are not given in the case of an exceptional outbreak event (superspreading event, e.g. carnival session, après-ski bar Ischgl). With hygiene measures, favourable effects with regard to total mortality can be expected."

This is the most interesting part of this and should be discussed more. There seems to be a lot of evidence that viral load is a factor. If social distancing and proper hygiene doesn't just lead to less infections but to more mild infections that's a big dea.

My understanding is that viral load is always a factor for every virus. Laboratories don't use soap and water, because they want to kill 100% of the viral particles, not 99.9%. They work with samples that have extremely high concentration of viral load.

(Source: lecture by prof. Krzysztof Pyrć)

That last link is broken. This should be the correct one: https://www.krankenhaushygiene.de/ccUpload/upload/files/2020...

In Alberta we are only testing pretty sick people until this week, but are high in the global rankings for tests per capita. About 2% of cases have been positive. Widespread, random testing would not make as good a use of resources, since the chemicals to make the tests are becoming a little scarce until Chemical manufacturers catch up

> the chemicals to make the tests are becoming a little scarce until Chemical manufacturers catch up

To the best of my knowledge this isn't true. Only specific product lines by specific manufacturers are approved for clinical use by most governments. As far as I know, the reagents used for RNA extraction and PCR are cheap and readily available.

I believe Wuhan government eventually went door-to-door to test the population exhaustively.

This doctor from Stanford agrees with you and is trying to run this type of test: https://www.youtube.com/watch?v=-UO3Wd5urg0

There was a town in Italy, roughly 10k population, where they tested every single person regardless of symptoms. The result highlighted how many asymptomatic people tested positive. AFAIK the test was not repeated so there was no data on evolution / spread. But it was still an eye opener.

(My apologies for not citing the article, I had read it in the paper edition of the NY Times, and failed to find it online.)

I remember reading this story in NYT also. Are you thinking about the town of Vò?

If so, here is a related article about how they tested everyone there: https://www.theguardian.com/world/2020/mar/18/scientists-say...

Recently in Argentina there were news of a case were 400 people in a boat were exposed, quarantined and then tested. From official government sources, of all that tested positive, 70% were asymptomatic.

Edit: I rechecked, the numbers are right but apparently this has been the first test. It hasn't passed enought time to see if those that tested positive, but were asymptomatic, later on develop symptoms.

Random testing here in Iceland over the last week showed 0.6% infected.

https://grapevine.is/news/2020/04/06/random-sampling-reveals...

This is a useful albeit very small data point, because Iceland received its cases from both Europe (alpine ski cluster) and the US. So it puts a very loose upper bound on the incidence in the US up to the point travel to Iceland was effectively halted.

You might check out https://seattleflu.org/

I don’t know if they’re the only ones doing random testing, but my understanding is that they were able to put pressure on the CDC when no one in the US was legally allowed to test other than the CDC. they were somehow able to circumvent testing restrictions by claiming research status since they were in the middle of a two year flu study.

In any event, I suspect the cost and difficulty of good randomized is why they needed such good funding for only a 2 year study.

As said by someone else, tests are in short supply. So other ways are being tested, like testing wastewater[0].

[0]: https://www.sciencedaily.com/releases/2020/03/200331092713.h...

I also don’t get it - shouldn’t work better testing a random population sample and interpolate the numbers?

Stanford just tested 3200 people in Silicon Valley for antibodies at a drive-through location.[1] There are similar group tests being run in LA and Colorado. In a few days there should be more solid info on how many people have had the disease without symptoms.

[1] https://www.stanforddaily.com/2020/04/04/stanford-researcher...

Seems promising! I really hope it's a couple days though and not weeks like the usual coronavirus testing I've been hearing about.

The tests here in Queensland give results in 24 hours, according to a friend who was just tested

That's not bad. In the US my friend just got tested and they said 5-10 business days.

IIRC, the researchers stated yesterday not to expect any surprises based on what they've found so far... :(

The test takes 10 minutes:

https://premierbiotech.com/innovation/covid-19/

They're talking about it being a few days before they release the statistics from the testing.

(As someone who knows next to nothing about medicine or microbiology) I’m curious how they already know which antibodies to look for? Are antibodies unique to each virus? Is it possible that some people have stumbled upon antibody X at some point in their lives that maybe isn’t perfect for fighting off this new coronavirus but still helps? Along the same lines, is it possible some people have antibodies IgM and IgG from some other illness they’ve encountered in their lives? Or is the presence of those antibodies conclusive evidence that they’ve been infected with COVID-19?

From https://www.sciencemag.org/news/2020/04/unprecedented-nation...

> We’re also concerned about the related coronaviruses that cause the common cold in humans. These viruses induce antibody responses that, at least temporarily “neutralize” the virus, but they don’t appear to last. Because the neutralizing antibodies wane over the course of 1 to 2 years, people can be reinfected with the exact same coronavirus 1 or 2 years later. The simplistic idea is that people are going to get infected with SARS-CoV-2 and then they’ll be resistant to infection for the rest of their lives, and that herd immunity will accumulate over time, etc. But if this is anything like the more traditional coronaviruses that cause common colds, you’ll get robust neutralizing activity at first, but it will wane over time. And so our studies are really focused on assessing that issue of persistence of immunity from reinfection.

[...]

> Q: What happens when SARS-CoV-2 infects a person who has antibodies to the other four coronaviruses that infect humans and cause the common cold?

> A: We were on a call today with the CDC about this. If we look at people who just went through a SARS-CoV-2 infection and have a burst of antibodies against the virus, they’ve also boosted their pre-existing antibodies against the classic cold coronavirus. And the earliest antibody responses that CDC researchers have seen in careful longitudinal studies to SARS-CoV-2 are actually those cross-reactive memory responses to the classic cold coronaviruses.

> Q: How might these cross-reactive antibody responses matter?

> A: The immune memory to previous infections may help control infection with those cold viruses and even ameliorate symptoms of SARS-CoV-2 infection. But it can cause problems with the accuracy of SARS-CoV-2 diagnostics, as people reinfected with common cold coronaviruses could score as false positive with some SARS-CoV-2 serological assays.

They are looking for antibodies to the spike protein on COVID-19. So if a person has those, then it's proof that they've been exposed to it.

Antibodies are unique for each virus, but there can be "cross-reactivity" where an antibody for a different virus also binds to COVID-19.

Yes, it is possible that there are people out there who have antibodies that bind to COVID-19, who have never been infected with it, but rather have an antibody that cross-reacts with COVID-19. But it would be a very rare occurrence.

So having COVID-19 antibodies isn't a perfect was to tell if a person has been exposed, but it's a very good way to do it since antibodies is highly correlated with exposure.

Would antibodies that can attach to COVID-19 but were produced from a different viral infection potentially make you immune to COVID-19?

I think that is the holy grail.

That was the case with Smallpox and Cowpox, and the original vaccine.

Interesting! From the smallpox wikipedia page:

> The term vaccine derives from the Latin word for cow, reflecting the origins of smallpox vaccination.

Yes.

It’s similar to the AAV (adeno-associated virus), a common virus.

There are multiple subtypes, but often an antibody to one will bind to other variants, making you immune.

IgM and IgG are types of antibodies:

https://kidshealth.org/en/parents/test-immunoglobulins.html

Specialized versions are produced in response to individual disease. They are similar to each other but not the same.

I've seem people theorize about this before saying maybe it's good news because that means it's less fatal than we thought and therefore maybe we're closer to herd immunity. But I saw an epidemiologist talk about how an R0 of 2 meant you needed 50% of people to be immune for herd immunity, and an R0 of 3 meant you needed 66%. This would mean it'd need to be even higher - the article says 82%. We're nowhere close to that now - even northern Italy, they're estimating that maybe 10% have caught it.

yes, this is why measles vaccination rates must be nearly total. measles has an R_0 somewhere beteween 14 and 18 (!!!)

A second is that the measles vaccine fails more often than many others. The combination drives the necessary vaccination rate quite high, and the risk isn't just the unvaccinated kids, it's a % of the ones who thought they were fully vaccinated.

Also, it seems that people with low severity symptoms don't build up a lot of antibodies, so they're not sufficiently immune. So out of that 10%, only some count towards the herd.

Citation?

From the last sentence in the conclusion: "Stronger antibody response was associated with delayed viral clearance and disease severity."

https://www.medrxiv.org/content/10.1101/2020.03.24.20042382v...

That's very interesting.

> First, we observed three types of antibody responses in COVID-19 patients, strong, weak and non-response. Second, we found that the earlier response, higher antibody titer and higher proportion of strong responders for IgM and IgG were significantly associated with disease severity. Third, the weak responders for IgG antibodies had a significantly higher viral clearance rate than that of strong responders. These data indicates strong antibody response is associated with disease severity, and weak antibody response is associated with viral clearance, which resembles SARS and MERS.

I have a wild-ass guess. I vaguely recall that antibody response and cellular immune response generally tend to be inversely proportional. And that IgM antibody response is an aspect of allergy. So maybe strong antibody responders tend to have weaker cellular responses, and more wet lung problems.

Preliminary results from a Dutch study I was reading about a few hours ago, in Dutch, that I came across through news.google.nl - although because I'm in a different timezone now, google news has been refreshed, and I can't find this specific one any more. So take that as you will...

That's not what i've seen. They're looking at it for blood transfers and finding asymptomatic people have sufficient loads.

edit: i think this is logical (for my rudimentary knowledge) given the cause of death is not directly related to the virus. The symptoms of dry cough and fever are all the bodies response. Even the shortness of breath and pneumonia might be attributed to inflammation. Perhaps the asymptotic people just don't have such a strong response for whatever reason.

Do you have a source for this? Haven’t read that yet.

I've seen it mostly in the Dutch news until now. The source is the Dutch CDC (RIVM) via Marion Koopmans (Professor of virology and head of the virology department at the Erasmus Medical Center).

Article in Dutch (translated title: Mild corona virus symptoms seem to lead to lower creation of antibodies)

https://nos.nl/artikel/2329846-milde-coronaklachten-lijken-m...

This article says that people with mild symptoms have fewer antibodies and says it’s unclear if they can be reinfected. Further it doesn’t mention asymptomatic cases.

You don't even need to be infected to develop the right antibodies:

https://www.randombio.com/coronavirus2.html

You may not have a lot of them, but you have already evolved targeted killers.

> because that means it's less fatal than we thought

Don't we already have solid numbers on the fatality rate because of the Diamond Princess? A change in estimated R0 wouldn't affect that.

Diamond princess is a non-stochastic sample, with all its attendant biases.

What does that mean?

People on cruise ships are usually not a good sample of the general population, for example, the mean age is quite high.

But it is also a sample with few in the sickest bracket, who are a disproportionate fraction of deaths.

And the number of deaths is sufficiently small that it is consistent with a wide range of death rates.

Another way to put it is that the study is enumerative and not analytic. Unfortunately, just about every study being done on COVID is enumerative.

https://en.m.wikipedia.org/wiki/Analytic_and_enumerative_sta...

"Diamond princess is a non-stochastic sample, with all its attendant biases."

But isn't it a non-stochastic sample in the right direction ? Which is to say, the demographics of budget cruise line passengers are almost a worst-case. They represent the most susceptible cohort.

What we can infer from Diamond Princess is that the general population will, all else being equal, fare better statistically.

Yes, but I was responding to a claim that we already had "solid numbers on the fatality rate". They're far from solid. We have a small sample that gives us an idea of the CFR, for that demographic, but this tells us very little about global IFR.

That's a crowd that skewed older in age, amongst other issues.

You can adjust for age just fine as every country has different demographics. The real issue is the crew and passengers where significantly healthier than the demographics suggest.

Aka people undergoing chemo are less likely to go an a cruse.

I've heard tale that people have opted to be permanent cruise ship residents over living in assisted living facilities.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC526150/ https://www.cnbc.com/2016/07/26/ahoy-matey-more-folks-retiri...

Permanent is rather misleading in that context. These these people don’t have major mental or physical impairments yet. Their old enough to retire, but heathy enough to enjoy it.

That's not obvious to me. You can imagine people might take a cruise over a walking tour of Tuscany because the cruise is much more accommodating of physical disability, you can get around with a wheelchair, there's lots of staff to help, etc.

You can, but the cruise will not let you on once you reach a certain level of disability. Wheelchair is no problem, but if you can't get yourself out of bed into the wheelchair they will kick you off. Similar for other disabilities, minor cases are fine, but if you can't mostly take care of yourself you are off.

A cruise is one of the cheaper assisted living arrangements if you qualify, (but governments will not subsidize your cruise while if you need assisted living they will). Part of it is discounts for inside cabins, part of it is they know you are there and will give various jobs when they need help which helps pay for your cruise.

The town of Vò, 3300 tested repeatedly, 89 tested positive, 1 death.

If fatality rate is 0.1%, there was only a 10% chance of 1 death i.e. it is very likely death rate is higher than 0.1%. Also the Diamond Princess numbers are relevant, after adjusting for age spread in a normal population, it is a lot more than 0.1%.

How do you get 10%? On avg you expect 3.3 people to die if IFR is 0.1%, so it shouldn’t be as low as 10% likely to get 1.

IFR relates death to total infected, not total population.

Iceland has the most comprehensive testing, and their fatality rate is around 0.3%.

There are a couple of reasons for that, most likely. First, only 4.5% of the known cases in Iceland are over 70 years old. In Italy, around 16% of the population is 70+, so given that this virus has much higher death rates for the elderly, we would expect a much lower death rate than Italy. Second, there are still 11 people in intensive care (compared to six deaths). I'm not sure what the mortality rates are for patients that require intensive care, but I assume they would be much worse than average. I think I remember seeing about 20-30% mortality for ICU patients from a study in Italy. Finally, the majority of cases in Iceland are still ongoing, whereas many villages in Northern Italy have gotten through the worst of it, with fairly few cases unresolved. I bet that the age adjusted fatality rate in Iceland will end up being fairly similar to Italy.

I agree. So, the scalar fatality rate should be adjusted to the normal population distribution.

Diamond princess was tested early and often and therefore able to get immediate quality care before medical systems were overwhelmed. This gives something like the ideal minimum fatality rate if, for the most part, everything goes right with detection & treatment.

Out in the wild, this is very much not the case. Testing it still thin on the ground in many locales and the pressure exerted on the medical infrastructure is not evenly distributed, making levels of care vastly different.

Think of it like a highly treatable form of cancer: If everything goes right and it's caught in early stage 1, fatalities rates are much lower than when it's only detected after reaching stage 4 and fatality rates are much higher.

Diamond Princess, for distinct demographic group only, represents detection at Stage 1.

> ...R0 of 3 meant you needed 66%. This would mean it'd need to be even higher - the article says 82%

For anyone curious,

R0 = 3 means iff 2 in 3 people (66.66%) [0] are immune would R0 drop to 1 [1].

So, when R0 = 5.7 (the value proposed by tfa for SARS-CoV-2), 4.7 in 5.7 (82.45%) would need to be immune for it to drop to 1 [1].

Why should R0 drop to 1 [1]?

It means each case leads to only 1 successful transmission of the infection. This implies constant incidence over time. If a greater proportion are immune, then incidence will decline.

Why "herd immunity" isn't enough?

...problems arise because herd immunity is not the same as biologic (immunologic) immunity; individuals protected only by indirect herd effects remain fully susceptible to infection, should they ever be exposed. This has advantages, in protecting individuals with contraindications to vaccination or those who for other reasons miss vaccination, but it also has its disadvantages. Measles and mumps outbreaks among university students, and pertussis in adults, are among examples of the consequences of accumulation of susceptible individuals who have not been protected by vaccination, and escaped infection because of a herd immunity effect earlier in their lives... This means that there is a need for immunization programs to maintain high vaccine coverage, together with surveillance and outbreak response capabilities, as numbers of susceptible individuals accumulate in older age groups.

From: https://academic.oup.com/cid/article/52/7/911/299077

[0] This assumes a 100% effective vaccine (E = 1) in ((R0 − 1)/R0)/E. If, for a given vaccine, E < (R0 - 1)/R0, it'd be impossible to eliminate the infection through that vaccine. (R0 − 1)/R0 is known as the "herd immunity threshold."

[1] What I really mean is R0 x S = 1. See: https://en.wikipedia.org/wiki/Endemic_(epidemiology)

Nitpick: changes in the number of immune people change R, not R0

Yes, thanks. R or R-eff is more like it, but for the sake of simplicity I chose to instead leave a footnote about inaccurate usage of R0.

Your comment belies the inaccuracy of your user name :)

Would a greater percent of the population being immune lead to a greater chance for mutation since we're creating a higher barrier for survival of the virus?

Nope. Viruses can only evolve within a host. I.e. while they are infected. If you've go the right vaccine, there won't be enough time for a virus to get a strong enough foothold to make that evolutionary leap, since mutations are always a shoot of the dice.

Remember, there is no intelligence to evolution. Only happy coincidence. An immune population has the same probability of having a virus mutate in a particular infection, but has many, many fewer cranks on the mutation slot machine lever before the virus is eradicated.

Where you run into problems is with things like dengue fever, where multiple strains can all provoke a single immune reaction, but the other strains aren't crippled as much by the antibodies as the original, and can actually use the immune response to increase the severity of the infection.

I've read no papers that suggest that is a factor here.

> even northern Italy, they're estimating that maybe 10% have caught it.

How do we know unless we do randomized antibody testing in the population?

It's just a lower accuracy level of estimate, not an invalid method. For example you can know that an infected person that had no known contact with another infected person means that there is in fact at least one other infected person out there. Yes, not as accurate as random population testing, but it's a difference of degree, not kind.

One way to extrapolate: excess deaths. Look at the overall death rate for an area and compare it to the normal value when there isn't an epidemic going on. If there is a difference that is larger than your number of known Covid deaths (which is true for Italy by a factor of somewhere between 2x and 5x), you can assume you're undercounting your Covid deaths by that factor, and your overall Covid infections by at least that factor.

This still involves guesswork wrt the ratio of deaths to infections (IFR). Proper statistical sampling is the only way to get an actual answer.

The usefulness of that approach decreases as people's behavior otherwise changes. For example, accidents account for ~10% of deaths. It's quite feasible (and I think we already have data to support this) that that has dropped a lot due to people's behavior changing in response to covid-19.

Forget accidents, there’s a dropoff in heart attack, stroke, and cancer deaths as every death now is attributed to Coronavirus.

It is unknown what percentage of people who are infected get symptoms and it is largely unknown what percentage of people who get symptoms ultimately die. So there is no way to calculate anything.

https://www.imperial.ac.uk/media/imperial-college/medicine/s...

Which also means that a larger percentage of the population will end up being infected.

With R_0 of 2, you need to have 50% immune to have herd immunity, but in practice, you’ll have more people than that that will end up infected before the epidemics dies out. Think about it: when over 50% of people get and survive the disease, rendering them immune, the epidemics doesn’t just immediately stop. What happens instead is that since average sick person transmits it to 2 people, but one of them is immune, less than one person gets the disease, and the epidemics starts the exponential die-out. However, as it dies, people still get infected, albeit slower and slower until it stops altogether.

The epidemics final size equation is F = 1 - exp(R_0 F). For R_0 = 2, F is around 80%, despite the herd immunity kicking in already at 50%.

This is one reason that "herd immunity through everyone getting exposed and lots dying" is not the same thing as "herd immunity through vaccination".

If you vaccinate a big chunk of the population when only 1% are currently infected, you hit R(effective) = 1 much sooner. While becoming immune through infection essentially guaranties there are a tremendous number of infectious persons at the point R(eff) drops to 1.

Is it not the case though that R0 is dependent on all sorts of factors, including social distancing?

So if we keep some social distancing, presumably we can reach the equivalent “herd immunity” with far far fewer people infected, such that society can operate in a reduced capacity until a vaccine arrives.

Wait for the serosurveys before assuming how far it has already spread - would it be surprising if yet another estimate about this thing turned out to be way off base?

The actual article that is both readable and informative https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article

Here's an early estimate graphed by the NY Times at the end of February (scroll down a bit):

https://www.nytimes.com/interactive/2020/world/asia/china-co...

R0 estimated to be 2 - 4. Fatality rate: 0.1 - 3%.

This was the first information I saw that helped me understand why this was more than just a bad flu. Still didn't truly understand it at that point, at least the way I do now that we're experiencing the consequences of failing to contain it.

In pre-print on Feb 7, this paper initially estimated R0 to be somewhere between 4.7 and 6.6. People dismissed it as being not possible. It was never picked up by the MSM.

Now we're seeing from the both the real world and other studies that it's pretty plausible.

Yep, I read the preprint as soon as it was published and it scared me into buying masks back in mid-February.

Basic math, knowing population density, and then that paper was my "oh shit" moment. Got some half respirators and 300 rolls of toilet paper over the next week.

The toilet paper wasn't to prepare for a post-apocalyptic world. It was because I knew that was the first thing other idiots would buy when news of this thing hit your average Joe :)

Perhaps I'm misunderstanding your point, but it sounds like your bragging about being the first person to start hoarding.

I ran some SEIRD epidemiological simulations (I'm not an expert nor in that field), but the death rate in the NY region was indicating a R0 between 4 and 6, not the 2.5 or so I've seen in a bunch of places. Obviously, population density is a factor in that number as well. I'm trying to aggregate forecasts for the US on this website if anyone is interested: https://www.unitarity.com/app/challenges/us-coronavirus-outb...

> This was the first information I saw that helped me understand why this was more than just a bad flu.

I could be wrong but, I feel like I've read if you are healthy + young, it's on average pretty much just like a bad flu (or no symptoms at all).

Maybe on average, but it sounds like the variance is more extreme. I've heard enough horror stories of 30-somethings with no health problems being on death's door from this thing that I don't want to risk it, when I might for flu season (plus, there's a flu shot). I don't want to stop with the physical distancing until there's an effective anti-viral treatment.

> I've heard enough horror stories of 30-somethings with no health problems being on death's door from this thing that I don't want to risk it

While I don't disagree with that, my biggest question is:

https://www.cdc.gov/nchs/fastats/deaths.htm

2.8m people die a year in the US on average. 7.7k/day

what if we heard their stories of going from healthy to death every day in the news?

Can anyone explain how the virus is twice as infectious as we thought, but we're consistently lowering our death projections everyday?

The R is a function of human behavior, so sheltering in place lowers it (ideally below 1) which also reduces deaths in the future.

E.g., from the article: "Dr Feigl-Ding explains that R0 is the “R reproductive number at time 0 before countermeasures”. He points out that this is not the R(effective) at current time under mitigation measures such as distancing and testing, tracing and quarantine, which are expected to slash chains of transmission."

R0 is also culture dependent but before countermeasures are applied.

Thanks for adding that. Interestingly, Eric Feigl-Ding is a bit like Nicholas Christakis in that he is not an expert in infectious diseases, however he has positioned himself as an expert on COVID19. This seems to have annoyed some of the real experts like Marc Lipsitch. However, Feigl-Ding probably does deserve some credit for sounding the alarm relatively early on COVID19.

The real experts were all saying ‘it’s too early to say for sure’ and he was saying, yes, but the worst case scenario is really bad and everyone should know that.

Higher R0 should reduce the effectiveness of measures like social distancing, shelter-in-place, etc too though - those work by reducing the number of people each infected person interacts with, but if those interactions are more likely to spread the disease than previously estimated the eventual R you end up with is going to be higher than expected too.

Exactly, like HIV started with a high R0 but its transmission mechanism is pretty easy to get a handle on--don't have unprotected sex or donate blood. Once this information is widely known, BAM, low R0. SARS-CoV-2 is quite a different beast, and reducing the R0 might be a hell of a lot more difficult.

There is no HIV risk in donating blood. The risk was in receiving blood transfusions, before blood banks started doing HIV testing.

Wouldn't it mean that physical distancing was even more effective than we thought?

Because the IHME model is based on taking China's data at 100% face value and applying it to the US. It over-predicts in the short term and under-predicts in the medium to long term. They keep revising the overall number down to line up with an epidemic peak in 3-4 days in the US, which is ridiculous.

The reason why the epidemic peak is estimated so soon is that New York has the bulk of the cases and they began bringing new cases under control before most of the rest of the nation.

Dig into state by state numbers and most states are after that with lower peaks. See https://covid19.healthdata.org/united-states-of-america and then https://covid19.healthdata.org/united-states-of-america/new-... to verify.

Does anyone else think NY called their peak prematurely? It seems like there's some periodicity in the data and there's always a slight two day drop every 7 days. This makes sense since many people are still working mon-fri in the city.

This is for NYC specifically (last few days of data are incomplete): https://www1.nyc.gov/site/doh/covid/covid-19-data.page

I wouldn't be surprised if NY continued surging in the coming days.

Also, I wonder, could there be any effect of a population decrease from an exodus from NYC?

So the assumption here is that New York will be the last big outbreak in the US.

The actual assumption is that with current social distancing, any outbreak will die down on its own on approximable dynamics. There is good evidence that this is so.

When we end social distancing, all bets are off. See http://www.healthdata.org/covid/faqs#length%20of%20the%20epi... to see that they are painfully aware of this and are explicitly not trying to model that.

All right, that seems much more reasonable to me. Thought you said the peak for the USA in general was imminent, and that's not at all obvious given that the social distancing policies going forward have yet to be decided.

It might be the case, but that assumes that effective measures to control the epidemic will keep being applied everywhere.

Taking your post at face value, I would say we know it’s twice as infectious due to more testing and/or better data. So we know that more people have been infected.

We didn’t discover a bunch of dead people that were infected. So the denominator increased relative to the numerator.

Not stating this as fact.

The preprint of this article was published on Feb 7th: https://www.medrxiv.org/content/10.1101/2020.02.07.20021154v...

The authors of this study are almost certainly not the only ones who "knew" it was twice as infectious based only on the data available back in early February, before anyone outside of China was testing at all.

Being highly infectious is one thing but a virus’s deadliness is another. Something can have a really high infectiousness rate but not be very deadly, like common cold. The infectiousness they are citing her is the infectiousness that exists under normal conditions. So if you social distance, you can lower the infectiousness. Projected death rates are going down because distancing is working and we are lowering the virus’s infectiousness.

> because distancing is working

Really? Because all I’ve been hearing for weeks is nonstop complaining about how the US started social distancing measures way too late, how no one is taking it seriously, etc.

That's not a counterpoint, though. That can all be true at the same time as it being true that the situation could be a heck of a lot worse right now.

"but we're consistently lowering our death projections every day?"

'The first casualty if war is the Truth'.

Whatever the government tells you during a time of crisis is a form of propaganda, 'for your own good' so to speak.

Even if individuals are intelligent, crowds are not.

Whatever they say is very calculated and controlled, as if to achieve a specific outcome. So imagine a very cynical view of political messaging in normal times, but now tilt that towards a more truly civic situation wherein maybe it doesn't seem quite so cynical because, well, there is actually a crisis.

The 'numbers' projected a few weeks ago will have been constrained by a) what people could handle without panicking, b) what kind of numbers might get them to actually behave properly, c) what will save every political leaders skin (i.e. give bad news then everything after that seems like good news), d) how much we can bend reality without hurting their own credibility by being perceived as lying.

It's extremely hard and politically risky to 'shut down a country' and get millions of individual actors to 'buy-in' to behave as we want especially if it means annoyance or personal hardship. Political leaders are used to acting in a very populist way, and basically right now they are doing the extreme opposite. Every day, politicians have to act against their best populist instincts. It's hard to overstate what a sensitive time this is, it could go sour very quickly.

So take everything with a grain of salt, knowing that whatever is being said is calculated and 'all projections' are filtered somewhat. Ostensibly 'for our own good'.

I think there are often more qualified numbers published out there, but you have to go right to the source, if available.

Edit: the 'masks' PR and policy is probably the best example of that. In reality, there's not much harm, and likely a little bit of good that can come from masks. But the strategy was to get PPE to the front-line health workers who have a greater need and were in a real crisis, so, the public messaging was 'no masks'. But the 'truth' of masks started to creep to the fore, more were asking questions, moreover, the PPE situation started to stabilize a little bit and 'poof' all of a sudden 'masks are good'. Now they are telling us? The Canadian Chief Medical Officer literally did a 180 on that, sounding a lot like Donald Trump in his total about turns. The communications strategy early on was fairly clear and it made sense, but it's a little uncomfortable to see your so-called leaders only make decisions 'after everyone else' so as to avoid taking and risky or blame (Canada wouldn't budget on it until most other countries did first). If you read the fairly confident communications about masks from several weeks ago and compare them to the messaging now - you see a problem that very only makes sense in the context of "purposeful misdirection for the 'public good' ". Masks did not 'get safer' and nor did our understanding of them change. What changed was who ostensibly needed them the most.

I think it will go sour within a month maximum, anger is already bubbling. The numbers with shelter in place are trending toward fewer deaths than a bad flu season. Going to be hard to convince people to shelter in place much longer paradoxically with those types of numbers in early May.

Stimulus helps, but a clear exit strategy for reopening segments of the economy is going to have to be presented.

Fully agree. Now that we have actually 'flattened the curve' more than likely due to our actual behaviour 'things don't look so bad'. I am already feeling that and it's affecting my behaviour: today I almost bought take-out, whereas 3 days ago my personal policy was 'no'.

Given the reality of '2cnd wave' and also the gov. is going to run out of money to print and special programs, the next few months of 'undoing the ratchet carefully' is going to be the public/PR exercise of a lifetime. We'll be studying it literally for generations.

The IHME model has been revised every 2-3 days.

It has always assumed that lockdowns would be everywhere and would be observed.

Every new revision in the last 20 days has been a revision downward in death rate.

10 days ago, the 95% confidence interval was 100k-240k deaths with lockdowns observed.

2 days ago it was 45k-145k, with prediction of 81k.

Today is is 37k-137k, with prediction of 60k.

IHME has no incentive to downplay the severity, yet every time the update the model, they have to adjust it downward because reality had failed to keep up with the model.

Meanwhile, reporting of fatalities has actually gotten looser, with all deaths of tested-positive or presumed-positive individuals reported as covid deaths, regardless of cause or comorbidities (that became universal yesterday, and is why yesterday saw a big increase in deaths despite a huge dropoff in hospitalization over the last 6 days)

Yes, I agree with all of that - but you are misreading my point.

In the OP people are talking about 'the models'? Well, who's models exactly? What is the official one? And which ones are going to get widely communicated and have authority in the eyes of the public.

I am not indicating that some group of University researchers, somewhere in America are 'in cahoots' with Donald Trump's political team, for example.

IHME, for example, surely will just publish whatever they think, but this is not 'communication' and not at a national level. The plebes are not reading their data.

What information the government collects directly, the information they chose to highlight in press briefings, the information they want to be communicated in their talking points, how it is presented ... this is controlled and filtered.

The IHME model is, at least in America, the most widely reported model, and is reportedly being relied upon by Dr. Fauci and the Trump Administration for the bulk of their own forecasting.

I tend to read a lot of left-leaning and right-leaning social media, and IHME's model is the only one that I routinely find both camps talking about and linking to.

Admittedly, on the right, most of the pointing has been to mock or demean it, showing the apparent continual need to revise downward as some sort of evidence of malfeasance or bias -- but even so, the model's predictions are being reviewed regularly on both sides of the spectrum.

On the left, I've also seen people remark on this particular model -- usually holding it up as proof that covid is a serious, scary problem that the Trump Administration is being too casual about.

Each group seeing the model differently, and drawing different conclusions from it -- but people are, in fact, looking at this model and it's predictions regularly.

You can call people you don't like "the plebes", and dismiss them as rubes -- but doing so tells me more about you than it does about them.

my pet theory is the malaria drug is actually working. we won't get the final say until the trials are over however.

If it is effective, believe me you won’t hear about it before the elections.

It probably is working, but it also seems likely that locking everyone in their houses reduces spread -- so three weeks after lockdown, there are fewer sick people to find each day.

It actually is likely that the decrease in cases is more dramatic than the numbers show -- now that the acute pressure on the healthcare system in New York has abated, they're probably detecting a higher percentage of their cases.

(And on top of that, testing has begun increasing again -- we tested 127,000 people today, up from 107,000/day just 6 days ago.)

In case anybody is still looking at the case numbers (especially in NY) and wondering why they keep kinda not going down even though things look better, we are actually still increasing testing rates (despite what you may have heard)

As I said above, we tested 127,000 people yesterday.

We're at 142,000 today already.

... and we're at about 170,000 tests today!

Yes, I thought the "masks are bad" message was pretty weak. All the while, hundreds of millions of people in Asia who have been through SARS and Bird Flu and who knows what else are wearing masks all the time.

> Can anyone explain how the virus is twice as infectious as we thought, but we're consistently lowering our death projections everyday?

This article is talking about R0-- a measure of contagion without considering interventions like increasing mask wearing, quarantines, or physical distancing.

The higher the R0 the harder we have to work to push the effective R below 1, as is required to stop the spread... and the wider and faster the spread will be if we don't mitigate it.

One explanation is that >50% of people have already caught it.

This seems a bit odd. If it was my job to estimate R₀ for the coronavirus, I'd look at Singapore, Korea, Australia, New Zealand and so on, where there are thousands of patients, and almost all of them know when they caught it and who they caught it from. Why would you choose to study a place where people don't know those things?

Because that is the definition of R0. Once people are aware and change their behavior R changes as well and is not readily translatable to a different setting.

So you want to measure transmission rates under normal interactions, not during shelter in place/everyone wearing masks?

Yes.

From the article:

Dr Feigl-Ding explains that R0 is the “R reproductive number at time 0 before countermeasures”.

He points out that this is not the R(effective) at current time under mitigation measures such as distancing and testing, tracing and quarantine, which are expected to slash chains of transmission.

Title correction: this isn't a CDC study, just published in a CDC journal

I think they're pretty reputable, regardless.

"Author affiliations: Los Alamos National Laboratory, Los Alamos, New Mexico, USA"

Also the Bios of a couple of the authors at the end:

"Dr. Sanche is a postdoctoral research associate at Los Alamos National Laboratory, Los Alamos, New Mexico, USA. His primary research interest lies in complex disease dynamics inferred from data science and mathematical modeling. Dr. Lin is also a postdoctoral research associate at Los Alamos National Laboratory. His primary research interest lies in applied stochastic processes, biological physics, statistical inference, and computational system biology."

If people weren't dying, it would be hilarious had badly understood this epidemic is. Months since the start, we have no idea how infectious the virus is, how many people have been infected, how deadly it is, how effective (or not) containment is, how many people will need to be hospitalised or how long for.

It's actually incredible. I'm very cynical, but I wouldn't have believed Western governments could have floundered so badly if I'd been told even a month ago...

It doesn't help that they leaned heavily on Chinese numbers. Even with data from southern Europe that is likely to be more accurate, people relied on numbers coming out of China because they were early and more numerous... But not enough thought given to openness, and now we've been trying to correct interpolations and extrapolations with a fraudulent starting point for the past two months.

China causing this should be a massive scandal. China lying about their numbers so we can't deal with it effectively should be second bigger scandal.

Historically, I've been quite pro China (until they started rounding up Muslims recently at least). But this is totally unacceptable.

</rant>...

Do you have any counterexamples where novel germs or viruses were better understood in a shorter amount or time? From my POV, it just looks like a genuinely hard problem right now.

I wouldn't dispute its hard. I don't think coronavirus has any comparative example. Nothing this contagious/disruptive has occurred in modern times. I just find it amazing that we're 6(?) months since the first diagnosis, and we know so so little.

Maybe that's life, but maybe if China was honest or the US got its testing together we would not be here?

I hear a lot about how we may be seriously underestimating the CFR denominator, due to undiagnosed mild cases. Ok I get that.

What I don’t hear much about is that the numerator is also probably understated at any point in time for several reasons.

First I don’t trust the China data I think it’s understated and that’s the oldest, most mature data we have.

Second, in an exponentially growing disease with something like a 6 week course from infection through to mortality/recovery, we will always have diagnosed cases that are 6 weeks, or whatever the true course is, ahead of the final death tally and as the diagnosed cases are rising so rapidly, including those weeks and weeks worth of diagnosed but unresolved cases could add up to a huge amount of error to a simple deaths/cases CFR analysis that would significantly understate what the final CFR will look like. Cohort analysis does not appear to have caught on in the CFR calculation world from what I’ve been reading.

Third, I’ve read that there are likely a significant number deaths that are probably attributable to COVID-19 that are not being counted as Covid-19. The death rate in northern Italy over the last month, even when all the COVID-19 attributed deaths are removed, is significantly higher than it has been in similar periods in the past I have read. I believe the same is true in New York City. So if these stories are correct, there are likely more COVID-19 deaths than are being counted.

So while it is probably true that the denominator is understated, it seems to me that it’s also very likely true that the numerator is also understated making it very difficult for me to believe any of these estimates are very accurate until both these issues are addressed.

Has anyone seen anything that explains some of these issues and calculates a cohort-based death rate, which somehow estimates or adjusts were incorrect, time shifted or under counted Fatality data?

No one thought that it depends on people's habits, say common social norms? Say in Asia it was commonplace to wear masks even before epidemic and it is considered polite to stand far away from one another, and people bow instead of shaking hands. Quite clearly there, or say in Sweden with their tradition of having a large privacy distance, R0 is much lower than in Italy where people hug and kiss seeing each other.

Twice as contagious = half as deadly?

depends on the death lag, if you are in the exponential growth stage of the outbreak that it.

Much, much less than half. Contagiousness affects the exponential spread.

So maybe cov2 is on level with the flu after all.

I’m not a doctor, but I’d bet it’s closer to a cold - it spreads much faster, but may be less lethal. Also, when a virus jumps between species, its virulence can be elevated during the initial period of spread. So it might be a “really bad” cold, at least early on:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4361674/

I wonder why such panic then.

If it's really not that bad, I would guess that the panic is because this is the first time we've been able to observe a brand-new cold virus as it spreads around the world. When the general public starts to understand all of the damage that these things do, and how quickly they can spread, it's understandably pretty scary. Plus a whole bunch of other factors.

If that is true China cases will sky rocket again this month.

There are plenty of anecdotal stories that show that it spreads very quickly. A highly publicized story in Germany was about one of the first ten cases here. This guy merely sat behind a patient and got asked for a salt shaker. That was his only contact. And I know of a company where someone who returned from a vacation in Italy was in a meeting with 6 people. 5 of them got infected.

Here's the study from the Diamond Princess which elucidates what the percentage of the population is asymptomatic: https://www.eurosurveillance.org/content/10.2807/1560-7917.E...

And now we wait for the other shoe to drop: serosurveys showing that at least one-third of the US population has already caught COVID-19 and recovered.

Excellent. The faster it moves through, the sooner we'll get back to work.

You've got to do the math on that. Estimate the percentage of people that have caught it. Then compare that to how 82% people need to catch it in order to have the bare minimum of herd immunity. And factor in that health systems in many regions are already overwhelmed. If you want to get to herd immunity just from catching it, you'd have a health disaster that is impossible to imagine even now.

I suspect in 2 years time the death toll will be about the same for everyone. What we will look at is how countries handled society while it happened.

This 'overwhelmed' the system is a deadly meme, the numbers don't work out.

The best model might be wear masks, physical distance, keep hands clean, testing and tracing. Ban really large events.

I don't see how you can say the numbers don't work out, unless you believe hospital systems are lying when they say they run out of beds/equipment.

Try to work out the actual numbers for strict lockdown vs not strict and model it over two years being practical. No hand waving.

Include the second and third waves for instance.

You won't see the difference you think.

Vaccines may never happen ( but post a second winter is a best case ) and treatments don't make things much better.

Once you get 1000000 dead vs 1050000 two years later we can talk about the additional dead from being unable to do things like cancer surgery.

Or asthma attacks, or infections from broken arms, or "minor" heart attacks, or anything else that people can fully recover from but still need immediate assistance with at risk of death.

You're going to have to show more of your work if you're convinced there's negligible difference. We know that even now some hospital systems are overwhelmed, at a point when the worldwide population has room for another 10-13 doublings. We also know that mitigation has already proven to bring effective R0 down to close to 1.

> The best model might be wear masks

There's not much evidence that masks help, and there's lots of evidence that poorly worn masks cause harm.

There's some evidence that home made cloth masks are worse than nothing.

https://www.medrxiv.org/content/10.1101/2020.03.30.20047217v...

> MacIntyre 2015 25 also included a trial arm with cloth masks and found that the rate of ILI was higher in the cloth mask arm compared to medical/surgical masks (RR 13.25, 95%CI 1.74 to 100.97) and compared to no masks (RR 3.49, 95%CI 1.00 to 12.17).

>There's some evidence that home made cloth masks are worse than nothing.

>MacIntyre 2015

That paper is studying whether wearing a mask protects the user of the mask. But the purpose of universal wearing of cloth masks is mainly to protect people from the wearer. (Many people infected with COVID-19 are contagious without showing symptoms.)

And the study wasn't really "cloth mask vs no mask". It was "cloth mask" vs "sometimes wearing a medical-grade mask and sometimes wearing no mask":

> (1) medical masks at all times on their work shift; (2)

> cloth masks at all times on shift or (3) control arm

> (standard practice, which may or may not include mask

> use). Standard practice was used as control because the

> IRB deemed it unethical to ask participants to not wear

> a mask.

...

> Cloth masks resulted in significantly higher rates of infection than

> medical masks, and also performed worse than the control

> arm. The controls were HCWs who observed standard practice,

> which involved mask use in the majority, albeit with

> lower compliance than in the intervention arms. The

> control HCWs also used medical masks more often than cloth masks.

> But the purpose of universal wearing of cloth masks is mainly to protect people from the wearer.

Cloth masks definitely don't protect other people from the wearer. What's the mechanism of action there? Someone coughs, but that shitty cloth mask i) catches everything and ii) the wearer doesn't fiddle with it all day and then touch everything around them?

Finally, getting some decent data!

This means that “herd immunity “ is a non-starter: natural immunity through infection/recovery requires large fractions of the high-risk population to be infected, and vaccines are too far out (economic destruction would occur before vaccines may exist).

We must pursue large-scale testing on a “total war” basis, with the goal of containment and extinguishing the virus.

Herd immunity was a non-started way before this. Just based on death rate it's an insane strategy.

Even if the death rate is 1%, then in the USA, to have even 60% infected and recover, you need to have 192 million infected.

1% death rate = nearly 2 million dead.

But it would be way higher than 1% death rate with these sort of numbers because most of these people are not even getting a hospital bed, never mind ICU.

Didn't a study show that the death rate is a magnitude lower than previously thought? Something like 0,6% IIRC.

even if the death rate is that low, you have to account for the fact that health systems in first world countries are overloading with the initial stages of the spread, leading to more people dying because there's no medical attention to patients who would otherwise make it through the disease.

In Italy they have 10% of people who they know have the virus dying (deliberately not calling this death rate) but they are testing pretty well and that seems very, very high. Can there be 10X more people with the virus than they know about? Not sure.

Also, OK let's call it 0.6%. Only just over a million deaths in the US alone then, even before health systems overwhelmed.

Still a non-starter in my book.

Italy is testing at a rate less than NY. I live in NY and I personally know over a dozen people with presumed COVID-19. Only one has been tested as he is a first responder.. The others are not in the stats. I know this is anecdotal but it appears that there are WAY more people that have this than the official tested positive number.

It seems once this takes a hold in an area that it really gets a lot of people. Me and my wife also had lung congestion, cough and brief fever but will never know if we had it until widespread antibody testing occurs. But it seems very likely there are many more cases than reported and 10x could be possible.

I'm in London and it's the same here. No-one I know who has symptoms has been tested.

Tipping the scales back the other way - the official deaths only include people dying in hospital, so the true death numbers will be a lot higher, especially places with overloaded hospitals.

There's a study in italy where they think real deaths are double the official count.

> However, journalists and scholars have crunched their own numbers. L’Eco di Bergamo, a newspaper, has obtained data from 82 localities in Italy’s Bergamo province. In March these places had 2,420 more deaths than in March 2019. Just 1,140, less than half of the increase, were attributed to covid-19. “The data is the tip of the iceberg,” Giorgio Gori, the mayor of Bergamo’s capital, told L’Eco. “Too many victims are not included in the reports because they die at home.”

https://www.economist.com/graphic-detail/2020/04/03/covid-19...

Paywall walkaround - https://outline.com/pUGx8d

Conversely, my jurisdiction (Alberta) has been testing a lot of symptomatic people, and ~98% of people with COVID-19 symptoms have tested negative.

In VA we've done 30,645 tests and have 3,645 confirmed cases. That tells me that 88% of people tested were negative. Either they're spreading a whole lot of "potentially" exposed people or there are a lot of people with symptoms who actually have something else.

Norwegian numbers are similar. Mostly only healthcare providers with symptoms, or close contacts with confirmed cases, have been tested. 95% of the tests are negative.

> Still a non-starter in my book

Nothing should be a non-starter without an examination of the other side of the balance.

0.6% is maybe 5 years' worth of flu, or about 10 years' worth of road deaths. It's undeniably a disaster, but it's in the same ballpark as things that society shrugs off as normal. If you gave the public a choice between that and 18 months of shelter-in-place, I'm honestly not sure what the right answer would be.

Ugh. We're really still talking about this? It isn't an available choice because all sorts of terminally bad 2nd order things happen when the medical system shuts down. Pitchforks and flaming torches, etc.

All sorts of bad things happen when you put everyone under a sustained lockdown for months too. So I'm not convinced that's an available choice either.

Yes, because a giant social experiment where we try to enforce global isolation is not something we should be taking lightly. The approach we should be taking will not be even close to clear until serological testing, and even then it's not going to be obvious. Stop pretending it is.

What do you think the impact of waiting on a vaccine is?

Global financial collapse -- not recession, collapse, will kill millions as well.

Pure eradication is a pipe dream and potentially the most ascientific strategy.

The argument of just letting it run its course also assumes that the medical facilities will not be needed for anything _else_ during the epidemic. That's obviously a ridiculous assumption.

In addition, there will also be no medical care for patients who get heart attacks, car crashes, etc. So the mortality of those things will skyrocket.

If young healthy super socieal superspreader people would be grouped together and separated from older people (for example in university colleges), there may be a way to achieve partial herd immunity of people between 18 and 30 years old who prefer to go through the virus instead of wearing masks.

The problem is that even for non-asymptotic people who don't die, just going through the virus sucks a lot and may have long term damages.

The death rate is ludicrously overestimated.

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