Updating Herd Immunity Models for the US: Implications for the Covid-19 Response
medrxiv.orgKeep in mind that "herd immunity" isn't really immunity, it's the point at which Rt (the average number of people each infected person passes the infection on to) drops below 1.0 and the spread shrinks instead of growing. Rt is dependent on how people behave. When behavior changes, Rt can change as well. Each herd immunity level is thus dependent on health measures, which is why "reaching" herd immunity and then loosening up health measures won't work.
Edit: removed wrong information on R0 that's not really essential to my point
When non experts talk about herd immunity they mean herd immunity given no to minimal precautions. This is a well defined concept.
Yeah. The term "endemic steady state" would be more accurate, but "herd immunity" is the term everyone ended up using.
If that's an informal lay interpretation I don't think it can be well defined, by definition.
> R0, which is a constant inherent to the virus strain
I think you just devalued your argument.
I think a big part of the confusion here is how do you clarify the meaning of R0 versus Rt in a context where people don't know the difference, and are using R0 in a context where there is either a time component or obvious public health interventions.
R0 may be a weak theoretical construct, but saying that it's also contextual and not inherent to the virus doesn't do much to clear things up.
Thanks for pointing that out, I corrected the post. It wasn't really important to my point though, which is that we might have enough immunity to let the infections die without football games, while we still have growth with football games.
It would have been more effective if you left it at that. I'm trying very hard to bow out of HN discussions on COVID, you were making a good point in a bad way and it would be a waste to see it lost. Thank you for the correction.
R0 is not a constant inherent to a virus strain. It's a contextual number, determined by population and behavior, population immunity and other factors.
Rt is simply notation of an estimate of R0 at a particular time.
Either way, you're correct that "herd immunity", as used here, means the point at which time the infection rate begins to decline, and this is conditional on population behaviors. If people mix more freely, the estimate changes.
However, the observation that people don't mix uniformly still applies, even if they mix a bit more than they do now. To put it in a CS context, it's like debating the magnitude of the constant, when the algorithm has a fundamentally different asymptotic behavior.
> R0 is not a constant inherent to a virus strain. It's a contextual number, determined by population and behavior, population immunity and other factors.
From Wikipedia: In epidemiology, the basic reproduction number, or basic reproductive number (sometimes called basic reproduction ratio or basic reproductive rate), denoted {\displaystyle R_{0}}R_{0} (pronounced R nought or R zero),[20] of an infection can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.
The next paragraph calls out that R0 is not a biological constant specific to a pathogen as it is affected by environment and behavior.
> can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection
Yeah, and that is contextual number, determined by population behavior and other factors.
Yes, population immunity should not be on that list, but the population behavior, weatcher and what not are still influencing it a lot.
Population immunity has to be on the list: if the population has some level of immunity, it affects the observed R0. And we can't really measure immunity, other than in very crude ways (i.e. antibody tests for specific epitopes), so we can't control for it.
This isn't a political statement of any sort.
You can think of it that way, but we never know, in practice, what level of actual immunity exists in a population.
In any case, it's not relevant to my point: R0 is a contextual number, always defined by empirical data. It's not a fixed feature of the virus.
Exactly. OP is simply wrong about this.
> Each herd immunity level is thus dependent on health measures, which is why "reaching" herd immunity and then loosening up health measures won't work.
Yep. Here's a very approachable and well written paper on the topic: https://academic.oup.com/cid/article/52/7/911/299077
And my comment on it from 6 months ago: https://news.ycombinator.com/item?id=22818413
> Each herd immunity level is thus dependent on health measures
This seems like something you made up. Can you cite your source?
How exactly does your rule apply to non-humans? What "health measures" do packs of wild horses take when disease comes to the herd?
It doesn't apply to non-humans because herd immunity is not something that is at all related to non-human populations. The term was coined to describe a trait of human populations and was found to be difficult if not impossible to achieve in humans until we started to vaccinate.
When cows get mad cow, you quarantine and then kill potentially infected cows. Wild horses have behaviors. They may not change their behaviors when they find a virus, but that doesn't mean that the reproductive rate is independent of the horses behaviors. It absolutely still is.
I appreciate this paper because I feel like every single article about "herd immunity" completely misses the mark and makes some rather poor assumptions. These assumptions are likely made because they make COVID seem like a bigger deal which sells more papers and gets more clicks.
When an article discussing herd immunity assumes a completely homogeneous population I just shake my head and wonder how in the world this article got published.
Whatever happened to large-scale antibody testing? You'd think that someone would be testing a few thousand random people each week to see how many people have been infected to date. That sort of thing was being done in the US back in April.[1][2] But it seems to have stopped.
If you're going to talk about "herd immunity", you need that info to get anywhere.
[1] https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v...
[2] https://abc7ny.com/coronavirus-testing-antibody-new-york-ny/...
The antibody tests seemed to be consistently underestimating other measures of Covid-19 immunity, even conservative ones, so they're not considered as useful as we thought in April or May.
It's possible the tests aren't sensitive enough, or immunity is largely based on T-cells (more expensive to test for) rather than antibodies [0], or antibodies for other coronaviruses confer some level of immunity, or something else we still don't understand.
[0] https://www.eurekalert.org/pub_releases/2020-08/cp-mcc081720...
There was a recent study of dialysis patients: https://www.thelancet.com/journals/lancet/article/PIIS0140-6...
TLDR: "During the first wave of the COVID-19 pandemic, fewer than 10% of the US adult population formed antibodies against SARS-CoV-2, and fewer than 10% of those with antibodies were diagnosed."
That's for the month of July 2020, which is useful, but not enough. We need trend data for this.
The trends won't really tell you much as we get further into the pandemic. Just because someone lacks a detectible level of antibodies doesn't necessarily mean they are susceptible.
Unfortunately, even antibody testing is unreliable now because of waning antibodies.
(One might object and say that if someone has little to no levels of antibodies they must not be immune anymore, but immunity is complex and not solely determined by antibodies)
These models are fundamentally flawed in that they assume immunity or susceptibility are binary conditions. Based on recent research it appears a significant fraction of the population has at least limited immunity from prior exposure to other coronaviruses. They can still get infected but the immune system clears it more quickly and they tend to suffer fewer symptoms compared to immunologically naive patients.
Every model is wrong, some models are useful. Does this flaw make this model useless? On the flip side is it a useful approximation even if it's not completely accurate?
I believe this model accounts for such individual response:
"Heterogeneity in contact structure and individual variation in infectivity, susceptibility, and resistance are key factors..." (emphasis added)
In general, that's one of those anomalous diffusion problems. Unfortunately, there are too many parameters to estimate without simplifying the model. So the reasonable solution is to take a maximum expected value of each and you can still be wrong.
Important data we do not know is chiefly how effective SARS aerosols are and at what range and time.
Gabriela Gomes was one of the first epidemiologists making this observation (as far back as May), and has found even lower thresholds (10-20%):
https://www.medrxiv.org/content/10.1101/2020.04.27.20081893v...
https://www.medrxiv.org/content/10.1101/2020.07.23.20160762v...
https://www.medrxiv.org/content/10.1101/2020.09.26.20202267v...
Well 20% of NYC had COVID and we are currently seeing outbreaks in communities that have relaxed social distancing measures, so it seems obvious that her calculations are incorrect.
That’s part of the thesis of the article. Some populations mix more than others. In the mixing populations the threshold will be higher. In populations with limited mixing, lower.
20% of NYC can have the virus and the general level for herd immunity can still be what is postulated. NYC is incredibly dense compared to the rest of the USA and as such will naturally have more mixing and a higher threshold than say Topeka, Kansas
The USA as a whole is already at ~10-15% and the rate of infections increased since late summer, and we are not even in flu season
Just because the US average is X% doesn't mean that the every community in the whole country is X%. There's clearly heterogeneity in the data.
Ok but I assume that is taken into account in your 15-20% estimate
I really struggle reading academic studies still...
"Heterogeneity in contact structure and individual variation in infectivity, susceptibility, and resistance are key factors that reduce the disease-induced herd immunity levels to 34.2-47.5% in our models."
I THINK that means, in addition to how infectious COVID is, and how susceptible and resistant people are in general, one of the other things that impact herd immunity is "contact structure" and it tends to be sort of limited. There seems to be plenty of "Heterogeneity in contact structure" studies done on many other things out there, so it looks like this is something that's already understood. If I understand it correctly, it means that most people have limited contacts, and while we all might be "6 degrees" from everyone else, we're not directly contacting all those people, and so that could help with herd immunity. So that maybe reduces the number from 74% to this 34-47% number, which better.
Does that mean "Heterogeneity in contact structure" is different for people based on things like how often we go out, where we go, how we travel and where we live? e.g. a subway/bus trip in Manhattan, NY is different than driving alone in Manhattan, KS.
Yeah, that's what it's referring to. People with more points of contact are both more likely to catch the virus and have a larger impact on herd immunity once they're immune.
An example of applying such a concept: vaccinating healthcare workers ahead of otherwise isolated people.
I presume many healthcare workers see orders of magnitude more people per day than average and those people are more likely to be sick (else why are they getting healthcare?) .
https://www.medrxiv.org/content/10.1101/2020.07.23.20160762v... was a similar study a couple months ago that estimated 10-20%, rather than the 34.2-47.5% in this paper. The bigger question is how long immunity lasts, which is still not known.
Tests this year on blood from people who had SARS in 2003, indicated that 17 years later they still had T-cell responses . On the other hand, coronaviruses which are experienced as "colds" apparently have a lot less retention of immune system recognition. Optimistic scenario is that the immune system is "smart" enough to recognize which infections are a big deal which must be remembered forever, and which are not. Pessimistic scenario is we got lucky with SARS, and may not with Covid-19.
Immunity isn't a binary condition which lasts for a certain length of time and then stops. It exists on a spectrum. The best evidence we have indicates that most recovered patients will retain a significant level of immunity for at least several years.
https://www.jimmunol.org/content/early/2020/09/03/jimmunol.2...
So, according to this paper 34.2-47.5% of US citizens need immunity before the pandemic can be declared over? So best case scenario we can achieve herd immunity with roughly 100M infections/recovered. USA is currently at ~8M infections/recovered, so that means we are roughly 8% of the way to herd immunity (best case).
At the current rate of +50K infections per day, that's 20 days per 1M infections, so we need 20 days * 92 = 5 years before we achieve herd immunity (best case, assuming no vaccines)? That doesn't seem right.
Well, we could get to 2MM faster if we opened everything up for the less-vulnerable populations (i.e. everyone under 50 without obesity or heart conditions) for whom the survival rate is above 99.99%. You might then be looking at 500k a day rather than 50k.
That is a good strategy. Unfortunately, ppl with survival rate above 99.99% might live with more vulnerable population. It'll be difficult to figure out how to effectively quarantine the more vulnerable population away from the less-vulnerable.
Why not allow healthy people to inoculate with a reasonable dose of the virus so they can control the timing, and then self-quarantine? I would have done that months ago if it was allowed. Even if it only has a partial chance of conferring immunity, doing that would have helped fewer vulnerable people get sick, but instead public health authorities are still clinging to “informed consent” nonsense, as if turning the world upside-down isn’t another serious risk to mitigate.
Maybe I'm missing something but it seems like you're just independently inventing the concept of a vaccine.
Or a Pox Party: https://en.wikipedia.org/wiki/Pox_party
Not that outrageous.
Jail inmates in California did that in an attempt to get released.
https://www.cnn.com/2020/05/11/us/california-inmates-coronav...
Who's stopping you from doing that? Pretty much all the restrictions are on the honor system so if you think that's a good idea go ahead.
The restrictions prevent me from buying a reasonable dose at the store. How am I supposed to do it, go around licking bus seats? I don’t know anyone positive in town, and asking in public forums has not gotten results.
So you think that, like, 7/11 should be selling pure novel coronavirus? Not really the same idea as a pox party and seems like it has obvious downsides to just make that available to anyone.
Travel is still way down. There are many vacant hotel rooms.
Why the massive downvotes?
Because no demographic has been demonstrated to have a 99.99% survival rate?
Source? 0-4 and 5-17 are definitely that high. The 18-45 demo is different, but there's a lot more heterogeneity in that population as well.
I cannot fathom how your link could possibly support your contention that 0-4 and 5-17 are "definitely" a 99.99% survival rate (IFR of 0.01% or 1 fatality out of every 10,000 infections). I suspect you are misreading the table? Care to elaborate?
Taking the data from the linked table for say 35-44 year olds is 1,798 deaths out of ~43 million population bracket, excluding influenza cases. That’s a 99.9956% survival rate. Taking all covid-19 and influenza it’s still a 99.9903% survival rate.
Nope, you're not even close to calculating the survival rate correctly. The population of ~43 million is the entire population, not the set of people who were infected.
Schucks, you’re right, I didn’t read the fine print on the CDC site. Still it’s not as far off as that sort of error would seem to indicate.
According to this Sep 2020 article on reason.com (1) they quote CDC numbers of a 99.98 survival rate (0.02% IFR) for 20-to-49-year-olds. Or a 99.997% survival rate (0.003 IFR) percent among people 19 or less.
1: https://www.google.com/amp/s/reason.com/2020/09/29/the-lates...
Glad you took another look!
How about this one: https://www.nber.org/papers/w27597.pdf?
I think in Italy they had 100% for 10-19 bracket.
In the Netherlands it's 100% for the 0-14 bracket. 1 out of of the 6500 deaths so far was younger than 25 (he was in the 15-19 bracket).
Are you seriously suggesting we lock over 110,000,000 people (34% of the population) in close quarantine for the duration? How do you plan to feed them? Get them medical care (be sure you don't overwhelm the system with the under 50s that get sick)? Keep them from rioting against their captors?
What happens when you decide it's good enough and release them, and the residual infection sweeps through that population like wildfire?
>Are you seriously suggesting we lock over 110,000,000 people (34% of the population) in close quarantine for the duration?
As opposed to 100% of the population? It sounds like an improvement to me. I'm suggesting relaxing restrictions for a part of the population, not increasing them.
Who is suggesting 100% of the population be under close quarantine? We haven’t seen anything like that since April.
Bay area resident here. My kids can't go to school. I barely go to the store. Even going for a walk is tricky, as unless you take massive precautions (which I'm fine with, btw) everyone yells at you. I basically have no human contact outside my family, and it's been like this for six months. So, yeah, for me the situation couldn't really get much more quarantined.
The Bay area sounds excessive, but consider that under the ancestor's idea, you couldn't do that if you were concerned with reducing your risk of infection.
I'm 53. I have asthma and I really hate hospitals. Right now, I have been going for groceries about once a week. I've gone to some appointments, but I've cancelled others. I've been getting take-out some, and I even went on one shopping expedition for craft supplies. I'm relatively comfortable with that because, while I have to assume everyone else is potentially infected, I can also assume that most of them are taking steps to protect me---a mask is significantly more effective at preventing spread from someone infected than it is at preventing an infection of the wearer.
Close quarantine means no going to the store at all. Not going for walks. It specifically means no human contact outside the people you are quarantined with.
That doesn’t sound like close quarantine at all
If you think 100% of the population of the United States is in quarantine now, I suggest you check the definition of quarantine.
If you relax "restrictions" on a part of the population, more of that population becomes infected. If you do not increase the restrictions on the remainder of the population, the higher prevalence increases the transmission rate in that remainder. And thus deaths.
I'm sorry if my existence is inconveniencing you.
You would have to increase restrictions for the vulnerable group because if you allow them to mix at all (even at today's levels) with the "free spreading" group they are going to have much worse odds than they do today. Since you're intentionally trying to increase the proportion of sick people in the less-vulnerable group. People like the "grandma living with family with school age children" or the "30 year old immunocompromised cancer patient with a roommate" get fucked if all those people around them (and around them, etc) simply go back to normal and you don't more actively isolate them.
This might not be a terrible idea, though, if compared to a several-year-extension of what we have now... because over time, the cumulative probably of exposure for the vulnerable will just keep rising and rising if we stay at something like the status quo.
But... that's where things like vaccine and treatment development come in. If a vaccine makes catching it much less likely in 6 months, or treatment improvements make it much less deadly even for the vulnerable in six months, then it's worth spending another 6 months in the current situation.
No one is seriously suggesting we forcibly lock vulnerable people in close quarantine. Instead we should provide those at greatest risk with free hotel rooms if they want to quarantine on a voluntary basis.
Where they would have to stay. For the duration. With minimal contact. If they don't want to take a significant chance of dying.
This is roughly what is called for here by some of the leading experts: https://gbdeclaration.org/
These are not "some of the leading experts". https://www.wired.co.uk/article/great-barrington-declaration...
Wired? Seriously? I don’t even trust them for tech news.
Here are the authors. They are well credentialed
Dr. Martin Kulldorff, professor of medicine at Harvard University, a biostatistician, and epidemiologist with expertise in detecting and monitoring of infectious disease outbreaks and vaccine safety evaluations.
Dr. Sunetra Gupta, professor at Oxford University, an epidemiologist with expertise in immunology, vaccine development, and mathematical modeling of infectious diseases.
Dr. Jay Bhattacharya, professor at Stanford University Medical School, a physician, epidemiologist, health economist, and public health policy expert focusing on infectious diseases and vulnerable populations.
And I'm sure at some point one of them is going to publish an epidemiological model in support of their position, and not a press release. Until then, they're not even at Wired's level.
Here's a published epidemiological model that supports Kulldorff, Gupta, and Bhattacharya position. Though the author is not one of them. https://journals.plos.org/plosone/article/peerReview?id=10.1...
One of the authors' twitter thread with the paper's summary: https://twitter.com/WesPegden/status/1288140129677332482
That paper doesn't consider reinfection risk or non-fatal outcomes.
There are many problems with the GBD, but the simplest is that we don't know who the high-risk groups are. Yes, we know age and certain categories of pre-existing condition make for higher risk of death. But we also know that perfectly healthy young people end up with strokes, heart damage, and lung damage, and we're not really sure why. We don't know why some people end up with debilitating symptoms months after infection.
We don't even know if herd immunity is actually possible, or if we'd be committing ourselves to years of intermittent lockdown controls as local outbreaks come and go.
This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible. It needs monitoring, compliance, and reactiveness that we demonstrably can't (or won't) implement - if we could, we wouldn't be in this mess.
Besides which, neither this paper nor that supports any idea that these three are "leading experts". As far as I can see they're vocal and have a history of being proved wrong by events.
> but the simplest is that we don't know who the high-risk groups are
We absolutely do. We have such a wealth of data and the signal is very strong.
> That paper doesn't consider reinfection risk or non-fatal outcomes.
That's because reinfection is extremely rare and risk for non-fatal outcomes is typical of other influenza like illnesses. An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
> We don't even know if herd immunity is actually possible
Yes we do. Pretty much every disease tails off. The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%. The 60% number quoted early in the pandemic was assuming homogenous population with equal susceptibility and perfect mixing.
> This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible.
All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
> We absolutely do. We have such a wealth of data and the signal is very strong.
We know who is likely to die. We do not know who is at risk of a life-long debilitating illness.
> That's because reinfection is extremely rare
We don't know this. What we know is that reinfection with a different strain is rarely detected, and that's a long way from the same thing.
> risk for non-fatal outcomes is typical of other influenza like illnesses
This is false.
> An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
At best this is optimistic. We know some (less than half) have a T-cell response. We don't know yet if that response is beneficial, harmful, or has no effect at all. It would be premature to start any sort of public health intervention founded on this assumption.
> Yes we do. Pretty much every disease tails off.
This strongly depends on the reinfection rate. Which we don't know.
> The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%.
This is false. To get anywhere near 20% you need to know the effect of the T-cell response, or have some other mechanism for discounting a large portion of the population.
> All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
Have you read either of them? Both models in this thread are predictive models of situations that haven't happened yet. Both use real data (from the US and the UK). Neither can describe reality, so do we throw them both out? That leaves the GBD lot with no epidemiological support at all, which would make my point rather concisely.
We simply don't have enough information to know whether the GBD proposal is safe or, even if it was, whether it could be implemented, and it's all the more suspicious because its three proponents have been making very similar arguments against general lockdown since at least April, when we knew dramatically less. They do not seem to have changed their stances based on new information, which moves the GBD out of science and into politics. Only they're leaning on their academic credentials to lend it airs of legitimacy it can't back up, which makes it complete, utter bullshit that nobody should pay any attention to. It's preying on desperation and optimism to deepen social division and reinforce political hysteria at the worst possible time. No credible health authority is paying any attention to it, nor should they. Please don't bring that sort of content to HN.
Actual infection count is probably far higher than 8M, isn't it? That's just number of positive tests.
Yes. The actual number, per the CDC, is probably 5-10x higher than the current confirmed infection count.
https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/comm...
(fyi: they bury the lede on this page: you have to click through to their horribly slow interactive viewer widget to see the multiples.)
I thought I remembered reading early on that icelandic testing was showing that 50% of all people infected there showed no symptoms. Then add on how many people show symptoms but its not severe enough to get tested.
> Then add on how many people show symptoms but its not severe enough to get tested.
Earlier this week we took my son in because:
1. Our neighbors have recently recovered from Coronavirus
2. Our son had a cold / fever around the same time lasting several days
3. He had several days of diarrhea after the cold symptoms subsided
I talked to my coworker whose wife is a doctor and he relayed the symptoms to her and she said it very well could be a mild coronavirus infection.
We weren't concerned for his health because he's handled it just fine, but we figured it would be good to know in terms of avoiding spreading it to other people.
So we called and set up an appointment. They got us in like an hour and half after we called.
Got there and the doctor said, "Nope, no need for a test."
At this point I'm extremely skeptical that the number counts are in any way accurate. Also I've lost faith in the "we're not doing enough testing" argument since we went and tried to get tested and they turned us away.
> Also I've lost faith in the "we're not doing enough testing" argument since we went and tried to get tested and they turned us away.
That does not imply that there is enough testing. That just implies that your doctor or health care system is unwilling to test someone in your circumstances. "Not doing enough testing" is not referring to just test kids not being available, it is also referring to health system policies and willingness/unwilingness to test people.
I guess to clarify, it has been my impression that the healthcare industry are the people telling us we are not doing enough testing.
We attempted to get tested and they said no, not worth the bother.
That seems contradictory to me.
The healthcare industry (and the CDC) and (almost?) every qualified expert is arguing that we are not doing enough testing. And we aren't. Part of the reason we are not doing enough testing is that we simply don't have the capacity and Dr.s' are being told that capacity is restricted and tests should be carefully considered on a case by case basis. The above is non-uniformly distributed in that there are some areas of the country where there is sufficient capacity and others where capacity is woefully deficient. Some sort of competent federal leadership on testing capacity/distribution would help tremendously.
If we had more testing capacity what would that change? What would it allow us to do that we aren't doing now?
Japan did very little testing and has a relatively low death rate so far.
We could basically live a normal life with massive testing and tracing in place. Korea and Taiwan have strong testing and tracing measures and life there is basically back to pre-pandemic behaviors except you need to scan a code on your phone (to enable tracing if a later detected infection is traced to that location) before you enter a bar/restaurant/night club, etc. China had an outbreak in Qingdao (6 total cases), so they tested all 9 million people in 5 days. That's isn't even that hard to do.
It depends on where you are. If you have a low enough case count and a well functioning health department that is doing contact tracing, then it's helpful to confirm that your kid is positive.
If you're in an area where neither of those things apply then, for a child a test isn't going to change anything. It's not going to change their treatment. If it's positive they'll tell you to isolate, but if it's negative, there's a high enough false negative rate, and there are enough cases that it's still very likely they have it, so you'll still want to treat it like they do.
>That seems contradictory to me.
Not necessarily, it could mean that the people that need to get tested aren't all getting tested.
The epidemiologists and some in healthcare industry are saying that.
The reason is not that some people are not willing to get test. The reason is also capacity, aldo policies on who is allowed to get test etc. Your case is literally someone who should be tested and was not, hence "not enough testing".
Here, I can get comertial test if I am willing to pay. No one will send me home. And if I was in contact with covid infected person, I get test for free (it is mandatory). And if I live in household with sick person, I have mandatory quarantine.
Sounds like where you live is no contract tracing and thus not enough testing.
Healthcare is not a monolithic entity, so different parts of it saying different things is coherent. It probably isn't the case, but it could even be that doctor deciding the test wasn't worth doing.
It can also be the case that insufficient testing is available in your area.
I know a group of people that were tested because of potential exposure (they were tested based on 1 person reporting symptoms; the additional tests were done prior to that result coming back).
Well, then add on the number of people who just don't get a test regardless. My child had a fever a few weeks ago, it was an ORDEAL finding a place to get him tested. You have to be quite motivated.
If you're not sick enough to require hospitalization, and especially if you're un-insured and poor and don't want to pay a couple-hundred bucks out of pocket, then you'll probably just ride it out and never get tested. I'm sure this is the state of things for tens of millions of Americans.
>My child had a fever a few weeks ago, it was an ORDEAL finding a place to get him tested. You have to be quite motivated.
That's because the test won't change anything. If it's positive, they'll tell you to isolate your kid. If it's negative, there's a high enough false negative rate that you'll still need to basically treat them like they have COVID (stay home and isolate).
Incorrect. Not being treated early is likely a main factor between likelihood of medium to severe.vs no to mild symptoms, beyond immunity. And isolation of sick patient from viral reservoir (such as fomites and other asymptomatic sick) is known to be important too, as it changes the viral inoculum dose.
Normal healthy children who don't develop a severe case aren't treated at all, so talking about early treatment is irrelevant. I literally just talked about this with a close family member who is a Children's ER doctor.
>And isolation of sick patient from viral reservoir (such as fomites and other asymptomatic sick) is known to be important too, as it changes the viral inoculum dose.
This isn't known to be important. If a person has been infected long enough that they are already symptomatic then "viral inoculum dose" is irrelevant.
Based on New York seropositivity, there would be a 5-6X multiplier on cases I believe. However that could change since they were test constrained when generating lots of those cases (downward?), and demographics of infected have changed (upward? younger may push multiplier higher as they are more asymptomatic and potentially get tested less).
> USA is currently at ~8M infections/recovered
CONFIRMED cases. The total number of cases is probably 5-10x that.
I live in the Deep South, and honestly I suspect that our curves have fallen simply due to a "limited" herd immunity effect (i.e. the groups of people most likely to catch COVID have already done so in large enough numbers). I certainly haven't observed any significant change in behaviors since the July peak, yet the numbers are falling like a rock regardless.
Deaths are probably more instructive – just to get a ballpark number. If only because deaths are less likely to overlook too many people.
If the IFR is around 1% we would expect around 1,000,000 deaths if one third of Americans have to be infected for herd immunity. So that would suggest the US is 20 percent of the way there.
Given the haphazard way of calculating these numbers I would, however, put huge error bars around some (something like ±15 percentage points at least).
I agree strongly with this type of analysis. The data we're seeing is strongly skewed by all kinds of biases. Looking at deaths at least removes one big piece of bias.
I too suspect areas are achieving some limited herd immunity. I don't think the behavioral changes adopted in the US have done much. Mostly, I just think less social people aren't getting it. More social people are.
Our best estimate of IFR is closer to 0.6%.
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scena...
I have a hunch that in reality the south has had flattening curves because the super hot summer weather is over and people are no longer spending most of their days in air conditioned buildings and are spending more time outside.
And the opposite is now happening in the north (again). People come inside as the weather gets colder, and respiratory infections in general get far worse.
> I certainly haven't observed any significant change in behaviors since the July peak, yet the numbers are falling like a rock regardless.
Picking FL as an example: deaths are down only about 50% since the peak in August (7-day averaged), and the numbers are surprisingly "sticky" (in the sense that they're not going down all that quickly.) For the record, FL lost 139 people yesterday; that's nearly the capacity of a 737.
For the record, FL lost 139 people yesterday
139 deaths were reported; those deaths actually occurred over the last several months. Which means we won't know how many actually died yesterday for a while, but it's likely well below 139. Jennifer Cabrera posts regular updates with dates of deaths, e.g. https://twitter.com/jhaskinscabrera/status/13139124858340884...
You make this out like I've picked on some rare outlier day, but the state has had multiple days in the past two weeks with even bigger death numbers. The 7-day average is a pretty substantial 85. If your point is that the drop since August has been more substantial, then I would need to know that the older, higher numbers were not also subject to the same delays.
https://public.tableau.com/profile/peter.james.walker#!/vizh...
You make this out like I've picked on some rare outlier day
Sorry, I didn't mean to convey that impression. Florida has been regularly reporting daily death counts that include deaths from several months ago.
I would need to know that the older, higher numbers were not also subject to the same delays.
They were, in the other direction. If you look at the date-of-death chart in the thread I linked, you'll see that for a few weeks there were consistently over 200 actual deaths per day, while the reported 7-day average in your chart never reached 200. The delay means that the reported count will be lower than the actual count when deaths are rising, and higher than the actual count when deaths are falling.
And of course we can't be sure which of those categories we're in at any particular point in time; if deaths do start to increase again, it may not be noticeable in the reported numbers for several days. But based on the hospitalization trend I believe it's probable that the current reported numbers overstate the actual deaths.
We know that coronaplague spreads through superspreading events, person-to-person transmission is wildly variable, most people don't infect anyone, but some give it to a dozen.
You'd like to know if people are wearing masks at church or if family get-togethers are now outdoors.
I think the common perception on HN, Reddit, etc is that COVID spread in the U.S. is primary a matter of conservative people flouting its seriousness. I understand the satisfying appeal of that picture.
However, I'm looking at my local health department's ZIP-code-by-ZIP-code infection map of the metro area. And it seems almost entirely correlated with poverty, not privilege. Infection spread seems mostly due to "essential" workers continuing to work. That's a problematic thing to point out, because I don't think we can or will solve for that. But it's plain as day.
I think the reason that HN, Reddit et al. have this impression is that state policies in conservative states are explicitly less restrictive than those in less conservative states. Florida just removed all stadium event attendance limits, for example. Within any given state there are all kinds of political beliefs and economic situations that may be more or less correlated with spread, but state governance is a very big variable.
Is there any evidence that level of restriction correlates with severity of outbreaks, though?
I mean, this seems to be the basic understanding of every single epidemiologist and public health expert, and also correlates with everything we know about the physical mechanism of how COVID spreads.
To me it seems like a covid infection is inevitable. You can delay it, but if you look at the state-by-state figures: states that successfully delayed for a while eventually got hit, states that got hit did not get additional infections.
I get the idea of flatten the curve but states that already got hit hard don't really have any reason to impose additional restrictions they wouldn't do anything.
And yet New York and California, both states with primarily liberal policies, have more cases than anyone else.
It's pretty obvious to people watching that the impression was created by liberal media that just wants to bash conservatives.
A cursory check of the numbers shows that it has no basis in reality.
California is roughly the median state in terms of total per capita cases at this point.
Top 5 states:
Louisiana, Mississippi, Florida, North Dakota, Alabama
California under reports deaths quite a lot though, they are much worse at reporting deaths than average for USA.
https://www.nytimes.com/interactive/2020/05/05/us/coronaviru...
I was using COVID case data, not COVID deaths.
In any event TX, NJ, and FL all have similar reported COVID deaths, but CA has fewer excess deaths than TX or NJ. Some of that is probably due to e.g. reduced automotive mortalities under more stringent lockdown, but I don't think the data you posted indicats that CA is e.g. worse than the other 3 states at reporting COVID deaths (otherwise we would see a much higher excess death count in CA compared to those 3 states).
Anecdotally, out here in the rural wilds of Alabama, most people seem to be wearing masks. I recently went into downtown Huntsville and no one was. Poverty and essential workers may be a significant component, but given the behavior of the young and affluent here, I don't think that is all of it.
Yes, you'd say that the Red areas are on aggregate older, poorer and less likely to work from home.
That said, at my regional college in the Deep South, case numbers have dropped noticeably since the beginning of term - instead of 30 cases every day we have now ten, and there have been remarkably few outbreaks at frathouses, all that despite no organized testing.
Conservative people are much more likely to live in rural areas. Liberals in more urban areas. Viruses spread much more effectively in higher population densities.
So you'd really need to look at per capita infection rates and control for density, not absolute numbers.
For some anecdata, in my moderate county we have a mask mandate. The surrounding less populated and much more conservative areas don't. Since the mask mandate was put in place more than half of the cases in my county's hospitals have been from the surrounding counties. Despite that fact that they have an order of magnitude fewer people, and despite that fact that we have far more people living in poverty in absolute terms than they do.
16% is a good estimate of US infection rate.
https://covid19-projections.com/ (Excellent source during these times. Sad to see them deciding to stop moving forward but it's good for now).
This also assumes that acquired immunity from an infection is complete and permanent. Immunity to other coronaviruses decays over a year or two -- meaning that before the five years are up in your scenario, there would be a significant cohort of prior victims open to reinfection.
See, e.g., https://www.nature.com/articles/s41591-020-1083-1
So, maybe that means by slowing the infection, what we're really doing is ensuring a neverending slow wave of infections ... forever? Maybe what normally happens with these kinds of viruses is they infect everyone then die out? And if you slow that, they never die out?
I don't think so. For instance, coronaviruses are among the causes of common cold. That one seems to have been with us for a while.
OTOH, a large number of infected people means a large pool of viruses to mutate into potentially re-infective strains.
No, it means that the expectation that "herd immunity" will be naturally reached, without an effective vaccine, is a pipe dream. Before vaccines, no population ever reached "natural herd immunity" for polio, which has similar R values; you need effective vaccines, effectively delivered, to get that result.
No, we are undercounting the number of people who have it (asymptomatic or not) by a huge factor. at one point it was by a factor of 10, but I doubt that is still the case.
This is why I watch florida like an eagle, because if it stops going up there we know we got a very good estimate to know when the top is.
~2.5-3% (730k/21.5m) of Florida has had it and it's slowed dramatically (use to be like 16k/day now down to 3). It feels reasonable that herd immunity starts slowing down the virus pretty fast somewhere around 25-30%. It seems reasonable it may come to a complete halt by 47%.
200k dead, infection fatality rate estimates seem to vary between 0.1 and 1%, so there should be at least 20m infections.
This is fag packet math, so I'll spare any exact figures.
But if the second wave data for the UK is anything to go by, confirmed cases vs actual cases was at _least_ 5x for the first wave.
There obivously are other factors, but with increased testing that multiplier only climbs, bringing herd immunity numbers actually within reasonable grasp.
I strongly suspect there have been similar effects in the US.
>This is fag packet math
Is this what they call napkin math in the UK? Back of the cigarette pack math?
From https://en.wikipedia.org/wiki/Back-of-the-envelope_calculati...: In British English, a similar idiom is "back of a fag packet".
To be consistent I think it should be "fag packet maths".
(People scribble on cigarette packages? Aren't those waxed cardboard or something? I wouldn't expect a pen to work well on those.)
FWIW they almost certainly didn't write on a cigarette package in this instance ;o)
It's just the name of that type of maths because people used to use whatever small, available, piece of paper was around - so cigarette packs some time ago (you definitely could write on them with a ballpoint pen (ie un bic).
Getting tangential, napkins to me in the UK have always been cloth, and we have serviettes (a French word, meaning sheet IIRC) made of paper to wipe our mouths with.
Our language gets more and more influenced by USA "English" usage, so perhaps youth would just call it a napkin. People do say 'paper napkin' but without the qualifier it's a fancy piece of cloth [to me].
That's a two-way street https://notoneoffbritishisms.com/Our language gets more and more influenced by USA "English" usage,I can't decide whether to use the term cutlery (mainly UK) or silverware (mainly US). They're both inaccurate, especially when asking for plastic/disposable versions.
If it includes a knife then "cutlery" seems accurate? Sometimes I'll just make a compound of the items "spoons-and-forks" (when it's pasta for tea [tea meaning evening meal where I come from]).
'Utensils' seems accurate, although imprecise.
This is actually the one I've always jumped to (in the US). "Silverware" and "cutlery" have always had a fancy restaurant vibe, rather than a be a word for an everyday object.
I carry a Fisher Space Pen in my pocket with my keys. It can write on wax paper. The ink cartridges are pressurized.
> At the current rate of +50K infections per day, that's 20 days per 1M infections, so we need 20 days * 92 = 5 years before we achieve herd immunity (best case, assuming no vaccines)? That doesn't seem right.
I can't remember exactly where I heard it, but I believe robust herd immunity in human populations has never been achieved for a virus like this without the widespread use of a vaccine. Which makes sense, because there's evolutionary pressure on viruses to adapt, and so many diseases remained endemic and common until vaccines where introduced for them.
Edit: in response to the dead reply: the Spanish Flu didn't disappear. It killed tens of millions (out of a much smaller world population) and persisted for decades as a seasonal flu. IIRC, it didn't get eclipsed by other strains until the 50s.
It technically didn't disappear, but mutating to the level of regular seasonal flu is almost as good.
I wonder if this also means that only 35% of us need a vaccination to declare it over. I had previously read 60-70% were needed and that 35% of Americans get a flu shot. That depressed me.
EDIT: seriously, downvoting this comment? Can't imagine why and would like to know.
No, immunity through vaccination is less heterogeneous and more random.
With natural infection, the socially active will be infected and removed first, which lowers the fraction needed.
I don't understand why more people do not get flu shots, unless they have no medical insurance and cannot afford the cost.
It's not always effective, because they have to predict when they make it what strains of flu will be prevalent in the next flu season, and they don't always get that right, but in that case you are no worse off than if you had not gotten the vaccine. But when they do get it right, it can save you from getting the flu. That's certainly worth a few minutes getting it an a couple days with a sore arm.
With a COVID vaccine, I doubt it will get anywhere near the same fraction of takers as the flu vaccine.
1. The anti-vaccine crowd probably won't take it.
2. The "COVID is no worse than a mild flu" crowd probably won't take it. Even if they do, it won't be at a higher rate than they take the flu vaccine.
3. The "COVID is a hoax" crowd probably won't take it.
4. A lot of the people who believe COVID is real and serious will probably be turned off if the approval seems to have involved politicians forcing approval over the objection of scientists who say it is not ready yet. (Especially if those politicians have also been pushing the "no worse than a mild flu" narrative).
Aren't flu shots usually free? The last few I've gotten were.
Flu shots are covered by insurance without a copay or coinsurance in the US, but there are about 30 million people who do not have health insurance.
> With a COVID vaccine, I doubt it will get anywhere near the same fraction of takers as the flu vaccine.
It will if closures are lifted but vaccination is made a requirement (either privately or by government mandate) for on-site work, schooling, etc. The flu vaccine is treated as primarily a personal preventive medical treatment; while it is encouraged for public as well as personal health reasons, there is no force put behind it. But it is quite possible, and there is plenty of precedent for, public health measures to be mandated (especially a conditional mandate, like for schools, work in particular conditions--including any public content or even on-premises work) and enforced.
45 states allow skipping their conditional mandate for schools if the parents cite religious objections. I expect their COVID mandates will have similar exceptions.
The vaccine won't be 100% effective. The FDA just approves vaccines that are at least 50% effective, and with the margin of error it's possible that they will work in 30% of the population.
> infections/recovered
This is a false binary state space.
The implication here is that having "tested positive" is equivalent to having an "infection". An infection, by definition, is an alteration of the biological state of the entity with observable side-effects (symptoms). It is patently false to assert that "testing positive" for this virus is 100% indicative of an "infection".
Further, "100M infections/recovered" implies that this infection can lead to "recovered" implying that the other alternative to recovere is a terminal/chronic condition. I guess this makes sense if we agree that many millions of "infected" immediately "recover" after testing positive, given that a substantial subset of those who "test positive" are "asymptomatic", reasonably understood as not-ill, not-sick, not-infected. Thus insta "recovery".
My overall point here is that the permitted vocabulary of speaking and reasoning about this phenomena is inexplicably illiterate. Whether this permitted simplistic vocabulary of discourse is by design or a symptomatic of the state of humanity, the inevitable consequence is a degradation of analysis and sub-optimal solutions.
No, that is not the definition of “infected.”
What is the end-game of "flattening the curve"?
Is it:
- everybody will eventually, but much later, get covid and become immune?
- Keep the infected number low until a vaccine is developed?
- Something else?