At some point, there's saturation, and "herd immunity". For this virus, that's somewhere in the 60%-80% range of the population. US population is around 333,000,000, so saturation is somewhere above 200 million.
We're nowhere near that. Current known US total cases are nearing 2 million. Current estimates of undetected cases are in the 80% range, so current total cases is probably around 10 million. So about 20x more people have to get sick before herd immunity. That's well over a year away.
- Around 200 million Americans will eventually get it, barring extraordinary events (like a vaccine arriving in 6 months). We cannot change this.
- Around 0.5% will die, overwhelmingly those who had a low remaining life expectancy to begin with. This is a much larger body count than most people seem able to accept, but also much less dangerous to the average American than many believe.
- All we can do is alter the timetable to keep the hospitals functioning, which has been done successfully in many places throughout the world albeit at great cost.
There's both good news and terrible news in those facts but none of it really seems to be in the consciousness of the public or of leaders, whereas "oh no you can maybe get it 8 hours after a guy sneezed in a lobby" is everywhere. Along with "everyone who stays at home is a hero" as we set ourselves up for a second Depression.
You don’t have a normal economy if people don’t want to go out in public or the supply chain is interrupted as workers get sick. A pandemic will harm the economy no matter what we do, these efforts, it is hoped, will reduce the impact while also saving lives and the hospital system.
I have seen a number of studies putting the case fatality rate at 2-3%, but this number cannot be generalized to the entire population because current testing is skewed toward the most serious cases. [2]
Long-term mortality in the total population may look something like 0.66% * 0.7 assuming there are no advances in treatment (possible but unlikely, given the unprecedented efforts that are underway, and how naive we were on day one).
In general more recent studies estimating IFR should be more accurate, early ones came with a lot of caveats. If there is a better estimate of IFR than 0.66%, I would like to read it.
[1] https://www.thelancet.com/journals/laninf/article/PIIS1473-3...
Adjust your numbers for population. Per capita numbers for the US are far from the worse case scenario.
[1] https://www.reddit.com/r/CoronavirusUK/comments/fxgk8q/early...
Takeaways: Old population, Pre-existing conditions, Skewed testing
The paper has an overall estimate of the infection fatality rate for all ages of 0.2-1.6%.
https://www.tagesschau.de/regional/nordrheinwestfalen/corona...
That is incorrect. The worst flu wave to hit Germany in the last 20 years, 2017/2018, killed appoximately 25000 people (excess mortality). That's 0.03%, and it's not typical. Often, the number is much lower.
Note also that the results from the Gangelt study you're referring to are preliminary; there's a press release but no paper yet, not even in preprint.
0.03% or 0.1% or 0.66%, the number is low enough to be perceived as not too dangerous by general populace. Contrast that with 13% mortality rate for Italy that was widely spun a month ago. Now _that_ was scary!
"Significantly less than 200 million Americans catching it" is still on the table.
"Herd immunity" is just the point at which the reproduction rate of the disease falls below 1 because the average infected person does not encounter anyone else to infect (because everyone else has already been infected) before they recover. It's not a binary switch, though -- as the percent of the population that has recovered grows, the reproduction rate shrinks steadily.
This means, for instance, that if we had X infected individuals before the lock downs, and then -- because we can't stay completely locked down forever -- we at some point have X infected individuals again after the lock downs end, we're actually in a much better place, because the reproduction rate of the disease is lower and that X will become 2X much slower than it would have before the lock downs.
This also synergizes with stay-at-home orders, which also reduce the reproduction rate of the disease. Right now we're relying entirely on stay-at-home to reduce the reproduction rate to as close to (or below) 1 as possible, but in a month or two, once a portion of the population has recovered, we'll be able to rely on a mix of "herd immunity" and stay-at-home to achieve the same thing; it's entirely reasonable that we can drag it out for years, and that less than 50 or 100 million Americans will catch it before a vaccine is developed.
It isn't hopeless.
> Around 0.5% will die, overwhelmingly those who had a low remaining life expectancy to begin with. This is a much larger body count than most people seem able to accept, but also much less dangerous to the average American than many believe.
We're currently losing 12-17 years of life per death, based on the statistics out of New York. It's older folks, sure, but it's hardly just taking people off their death beds.
For example why not pursue a policy such as: kids can go to school unless they live with someone who is at risk (since we know that children are at very low risk for complications). Similar to how we managed chickenpox before there was a vaccine.
I think we need to explore alternatives to lockdowns because I believe the economics of lockdowns will make them impossible to continue for very long. At the moment people see this as a "be heroic and do the right thing" issues, but most people don't yet understand the severity of the economic impact and haven't yet been personally affected by it. Lockdowns will probably become politically untenable by May or June whether anyone today likes that fact or not.
Because kids are the most likely to ignore even simple ways of preventing infection. Then you get exponential growth via kids and the health system implodes.
It's tricky to take large bites here because of the short doubling time compared to the relatively longer recovery time -- in the ~3 weeks it takes your currently-infected cohort to recover, your infected population could increase 100-fold if you aren't careful. Mistiming things -- misjudging how many people are currently infected -- by a couple of days can be difference between exposing 10% of your population to the risk and exposing 15%. There's a reason pandemics are often compared to forest fires.
This will get easier as time goes on, as the doubling period gets longer and longer.
Based on current estimates of how many people will die in the US, you can infer that we currently expect 5-10% of the population to be exposed in this first wave; I believe what widespread testing there has been in Italy leans towards about 10% of that population having been exposed there as well.
But if you expose kids to COVID now, they will in turn expose their parents, who will expose their coworkers, etc, etc
I could see that being effective strategy if we didn’t have a vaccine in 30 years, but for now, most people are still susceptible.
Only if you would like to see the hospitals overwhelmed with cases that don't result in fatalities but kill lots of other people and also kill people with underlying conditions that would otherwise live full and productive lives.
We don't know that's inevitable. Korea, Japan and Taiwan suggest another outcome is possible, and while a vaccine may not arrive in 6 months, lots of other things might: better treatments, better masks, better testing, better tracing, etc.
> Around 0.5% will die, overwhelmingly those who had a low remaining life expectancy to begin with. This is a much larger body count than most people seem able to accept, but also much less dangerous to the average American than many believe.
Death isn't the only metric. A lot more than those dying will be the amount of people that are hospitalized in very serious shape. Some of them will have long-term damage, and some of them will die if the hospitals are overwhelmed.
The way to avoid that is to at least spread out the impact over time. Telling people it'll be okay leads to no one listening and the health system collapsing. That's human nature. Telling people it's the apocalypse means most listen and the health system survives. Welcome to humanity.
Really, if every jurisdiction in the world overwhelms their hospital capacity it won't be ok.
What seems to be a noble lie is the apocalyptic mindset, where the coronavirus is literally the only thing that matters and we must never ask if a particular mitigation is worth the cost. Many authority figures are promoting this idea, even though they clearly don't believe it themselves and couldn't formulate effective policy if they did.
The example of Sweden seems to disagree with this assumption. They never locked down, and the current mortality is (1,203 / 10,330,000) * 100 = 0.0116%
First case on Jan 31st, no lockdowns, death rate has already flattened. Where is that crazy scary exponential growth?
The thing about the lockdowns is that evidence so far indicates that stopping 80% of non-essential economic activities voluntarily is about as bad for the economy as stopping 90+% on a mandatory basis, but it seems that the health outcomes are much better in the latter situation.
You can't know that without testing the whole population, or at least getting some controls.
This also discounts the unknown possibility of disability, reinfection, and limited time immunity.
NY population is 18.8M. 0.05% of the population has died.
You’re citing numbers released by the city where they haven’t confirmed cases, so it doesn’t answer the OP’s question.
Just one example, tests of pregnant women coming into labor in New York show that 1/6 of them are positive for nCov, but 85% are asymptomatic or presymptomatic. (https://twitter.com/CT_Bergstrom/status/1249836446569181186)
Another example, Denmark is finding potentially as high as 30-80x undercounting of cases, which would bring the fatality rate down to 0.1-0.2% (like a bad flu season) [https://www.reddit.com/r/COVID19/comments/fxk917/covid19_in_...]
I've seen a few more of these data points coming out, and nothing is 100% conclusive, all the new data points are showing that it's less bad than mainstream thinking suggests.
This doesn't seem right to me. With social distancing now, and testing and tracing once we have enough tests available, it seems to me that we should be able to keep the total number of infected people well below 200M, even if a vaccine takes the expected 18-24 months. If there's a solid argument that this is not the case, I'd like to hear it.
2) Testing and tracing is working fairly well in S. Korea because they're treating it like a war, and they've been orgainzed since fairly early on. The US is not organized.
What most people, including HN, miss is that passing a test today means nothing about tomorrow.
Hospitalization is a more important factor as it the death count goes up dramatically without intensive care.
These comments always come with a healthy dose of "people aren't seeing reality!?!?", which is a bonus given the level speculation surrounding IFR and the variance therein.
The exponential curves are somewhat useful early in an outbreak. It's confusing right now because some places are still "early" and some places are way past the point where those curves are descriptive. So they won't describe NYC at all but they're still somewhat working nationally.
It's 2 million worldwide. The US is now above 600,000:
Eg, https://www.johndcook.com/blog/2010/05/18/normal-approximati...
Current Hospitalizations, which is the number we should be most concerned about, and is reflected by the orange dots on the state's graph, should start low, max out at a number, and eventually return to 0. That should follow something like a bell curve.
I think the confusion is between the "total infections" curve and the "current hospitalizations" curve. The difference between them is huge, and really important. The "Flatten the Curve" idea refers to keeping the number of hospitalizations at any one moment below some upper limit, ideally the state's number of available ICU beds. That's a bell curve.
I think the confusion is that if the CDF is the logistic function, then the PDF is the logistic distribution. This is what archgoon is saying.
Bell curve refers to a Gaussian distribution, which is different but looks similar.
https://en.m.wikipedia.org/wiki/Logistic_distribution
http://visionlab.harvard.edu/Members/Anne/Math/Logistic_vs_G...
However, in the case of a virus, you eventually run out of population to consume; so you tweak the model to be "Let the rate of infection be the probability that a infected person encounters a non-infected person" (nicely, for a large population, this will give you the same results initially). This is, approximately, proportional to the product of the number of people who are infected times the number of people who are not infected (think back to chemical reagents and reaction rates). That is, (1-infected(t)) * infected(t). A function who's rate of change is that is the logistic function (and you can verify by taking the derivative of 1/(1+e^-t))
This is a simple model, ignores geography, ignores population change, and ignores changing behavior. It basically pretends everyone is an ideal gas molecule in a volume. But if you want to model something that "grows exponentially with a limit" it works alright.
I don't know under what assumptions the rate of change would yield a bell (normal) curve. (e^-x^2).