It's also possible that people happened to develop antibodies for a different coronavirus that are also effective against SARS-CoV-2-- meaning they're true positives (effective antibodies) that don't indicate an infection with SARS-CoV-2.

This happened with someone that survived SARS-CoV having effective antibodies against CoV-2: https://www.nature.com/articles/s41586-020-2349-y

Why not the fact that perhaps the virus found its way in more vulnerable populations, such as hospitals and nursing care homes (44% of the current deaths)?

If that happened, the population would be very largely skewed towards the most vulnerable (you usually don't go to ER or a hospital if you're healthy) and that would explain the uptick in deaths.

There's no other idea I can offer to explain why a disease with an estimated IFR between 0.5 and 0.9% would cause an order of magnitude more deaths.

For that 44% number, what data set are you using?

I have been idly tracking the data here in WA. 90% of the deaths have been in 60+ years of age. Just ~30% percent of the cases. In that age range, the CFR is staggeringly high. I don't know the data for the numbers from nursing homes.

Sorry, forgot to mention it. These are the numbers from Italy's Istituto Superiore di Sanità.

No worries. Just curious if there was a global source for that.

From looking at the numbers, I can't help but think we really failed protecting the elder population.

>That is an unlikely hypothesis, given that we didn't see an exponential explosion in deaths

The unmitigated doubling time of COVID-19 seems to be around 2.5 days. So if about 1% gets hospitalized and people noticed after 10 or so patients with the same symptoms (that's before any of them died, mind you) you'd need about 1000 people infected. To go from 1 infected person to 1000 takes log2(1000)=9.96 doublings. So if the doubling time is 2.5 that would be 25 days or over 3 weeks before anyone would notice.

Some of these numbers are a bit of a guess but I'm trying to show that "exponential explosion" can be very small and slow at the very beginning of an outbreak.

The variance in actual increase rate is probably way nosier with fewer subjects. I.e. in the beginning you can probably hover at a somewhat constant number of infected for a while before it takes off.

On average an infected person seems to spread it to 2-3 persons, but we don't know the variance. It could be something like 8/10 spread it to zero persons, 1 to 4 persons and 1 to 16 persons. With low number of infected that would make the increase very noisy. In fact, it has to be so, otherwise Covid19 would explode everywhere and not be more or less containable. It has to catch momentum or whatever.

There is also the potential issue that some of the hot spots are like that because they got seeded by multiple super spreading events.

As in unlucky happenstance means three infected people in a tour group show up and infect two to three dozen people each over a weekend. You go from 3 to 100 cases in two days. R instead of being 2.5 jumped to 30!

After a week just with normal spreading you have close to a 1000 people ill.

I can't find it anymore but there was an article here published a few days ago that essentially estimated whether infections with SARS-CoV-2 come from homogeneous transmission, i.e. everyone who has it infects a few people, or from highly concentrated transmission, i.e. there are a few people who infect a large number of others. The latter seems to be the case for SARS-CoV-2. This to me makes me believe 2. is correct. We know that this disease is non-lethal for the large majority of people below 40-50. It is thus not unthinkable that it was able to spread widely in those groups before finally hitting more vulnerable parts of the population all at once in March, leading to the devastating effects we observed. Additionally, in a model where everyone is equally likely to be infected, the number of deaths would reflect the percentage of people having the disease. That is why people think there is an upper limit of a few percent of infected. This however breaks down entirely if there are clusters of cases within different subpopulations.

I believe you mean this article: https://www.sciencemag.org/news/2020/05/why-do-some-covid-19...

#3 sounds both plausible and absolutely dreadful to me (I’m in Sweden where the de facto “strategy” is still herd immunity)...

#1 and #2 sound not entirely implausible to me though... Severity of disease seems to vary a lot with infection dose. Is it really that far fetched that when infection doses are very low the disease is mild, but as the virus level in the population rises and infection doses go up the disease becomes a very serious public health issue?

Does anybody know any data that supports/refutes my thinking above?

Is there evidence that infection dose matters? Last I heard, that was specifically not the case.

With what we know of the age dependence of the CFR, it is not unreasonable to think #1. In fact, until it broke into long term care facilities, it would fly under the radar fairly well.

Remember, for #2, it is not that most people stay asymptomatic. They are usually pre-symptomatic, and do have the symptoms later. Just for the majority of folks <60, they are a bad cold. If you are under 20, not even a bad one.

And from some comments down thread, #3 is as likely to be wrong with these having more false negatives. So, tough to say.

It is not #3, if we believe what they say in the study:

"The test had a 98.3% specificity and 100% sensitivity"

I.e. only 1.7% likelihood of false positives

EDIT: And no case was missed

I'm betting we didn't see an exponential explosion in death - ever. Look up the gompertz curve. It's very convenient for politicians to have everyone believe counterfactuals of unbounded exponentials.

I like #3 too. And really it is worth waiting for expert opinions rather than getting too excited - maybe people develop antibodies in response to some other cold virus that has a similar external surface to COVID-19. Who knows? The world is large and there is a lot going on. These 98 and 100% style test sensitivities don't magically make the world a controlled lab-like environment. I'm willing to bet crazy things sometimes happen that weren't tested in labs.

4. The current beliefs about lethality and R0 are way off

What are those beliefs and how do they have to be off to explain this and other data?

What if this "exponential explosion in deaths" is because people didn't receive medical care for various other conditions and thus deaths spiked?

This would require the other conditions to frequently enable death with COVID symptoms which wouldn't have happened with the condition being treated. For example patient has liver cancer under treatment, treatment ceases and they contract Corona-20, the untreated liver cancer then enables progression of COVID and death, whereas when liver cancer is treated then progression wouldn't have occurred.

Seems rather more complex than the common account.

We know #2 is true for younger people, as the mortality rate is almost zero unless you're in a nursing home.

Don't know why you're being downvoted.

According to Spanish government's estimates[1] based on their own seroprevalence study:

For the age group under 50, the estimated IFR is about 0.003%

For the age group 50 - 69 yo, it's 0.04%

And for those over 70, it's 4.1%

The study is considered to be well designed by Carl Bergstrom[2], for example.

[1]: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/a... Look for the table on page 15.

[2]: https://twitter.com/CT_Bergstrom/status/1261041466882678784

Please cite your data on the mortality rate.

According to Spanish government's estimates[1] based on their own seroprevalence study:

For the age group < 10 yo, the estimated IFR is 0.002%

For the age group 10 - 19 yo, the estimated IFR is 0.003%

For the age group 20 - 49 yo, the estimated IFR is 0.002%

For the age group 50 - 69 yo, the estimated IFR is 0.04%

For the age group > 70 yo, the estimated IFR is 4.1%

The study is considered to be well designed by Carl Bergstrom[2], for example.

[1]: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/a... Look for the table on page 15.

[2]: https://twitter.com/CT_Bergstrom/status/1261041466882678784

It also matches the CFR you can calculate from the Seattle Times. (Don't know if we have a good handle on IFR, but I can go off published data for CFR, right?)

op00to: I'm curious. What I said is well-known.

Were you trolling, ignorant of current news, or what exactly?