Estimating unobserved SARS-CoV-2 infections in the United States (opens in new tab)

Therefore it's highly likely that for a given death it is more likely to be incorrectly categorise as non-COVID when COVID was responsible, than to be incorrectly designated COVID.

What is the reason behind this?

At the beginning, the rate at which the number of deaths was growing was 26% per day, or doubling approximately every 3 days. This means that in the two weeks that it takes for the average person that is going to die of covid to die of covid, the number of people infected has grown by a factor of 2^4 to 2^5. So by the time that 30 people have died, It is reasonable to suspect that that the number of infections had grown by an order of magnitude since those people were infected, and those people are 1.5% of the people who had been infected two weeks ago. (This back of the envelope calculation is very sensitive to changes in the time to death distribution for people who have contracted covid, particularly to number of people that die fast.)

Furthermore, your infection fatality ratio is entirely wrong. My 1.5% was very optimistic. South Korea has the most exhaustively tested population on earth, and their case fatality rate is 2%, and it's worse among cases that have reached an endpoint. The virus could have mutated and attenuated since then, but other evidence suggests that the New York strain was more lethal than the SK strain, not less.

The Sciencemag paper that you have linked relies on a "seroprevalence of 3%", despite the parenthetical statement right next to their assumption that the confidence interval on that seroprevalence is between 0 and 3 percent. So not only have they chosen the maximum value for seroprevalence in that interval as their assumption, but the interval actually includes zero. Antibody testing cannot say with 95% confidence that any of its positive results were not false positives. That's a pretty bad test.

The takeaway to me is that unless you are willing to exert extremely strict border controls, localized quarantines and hard lockdowns of hotspots, and pervasive test & trace, indefinitely, then you wont keep Rt below 1 without the benefit of some herd immunity.

Hence lockdowns should either be as minimal as possible, encouraging low-risk populations to be out and about while high-risk populations shelter.... Or, you need an extreme and extraordinary response until widespread effective vaccination.

Small island nations may find they can choose the second path (although it’s not guaranteed, Hawaii has recently failed at it) but the vast majority of the world should choose the first.

And we should stop politicizing it, because largely everyone is in the same boat and will hit all the same endpoints.

The CDC officially became aware of coronavirus on Jan 1, and the first documented case in the USA was Jan 20. Now, we could have all had the flu, but really the flu doesn't have such a horrible deep chest cough like we all had in my opinion.

Given that some people say coronavirus was circulating as early as November 2019, I think we all had something more than the seasonal flu. By march 2020, the only people I knes who were sick were in nursing homes.

And it seems from the hostility of your response ("delete the comment as misinformation") that you find it incredible that herd immunity could exist with less than 1 - 1/R0 of the population infected? But even ignoring any pre-existing immunity, that calculation assumes a homogeneous and well-mixed population. That's clearly not the case, since some people (a nurse in a crowded ER, a police officer, a store clerk, a nightlife enthusiast, etc.) have far more contacts than others (a remote worker who gets stuff delivered). People with more contacts will get infected first, with disproportionate harm, and then become immune first, with disproportionate benefit. Many papers have modeled[2] this; though no one's found a great way to measure that heterogeneity yet, so for now, it's hard to say much beyond that the effect exists, and is potentially big.

And finally, herd immunity isn't a binary threshold, especially in a heterogeneous population. As others have noted, even places without enough immunity for R < 1 will still have slower spread than in a naive population, or may get to R < 1 from the immunity plus slightly more cautious behavior. Conversely, places that do have R < 1 overall may still have pockets of spread in sub-populations with unusually high R0. In any case, it's no conspiracy theory to believe that NYC developed significant amounts of immunity along the way to its ~24k (about 0.3% of the population!) deaths.

1. https://www.nature.com/articles/s41586-020-2550-z

2. https://www.medrxiv.org/content/10.1101/2020.02.10.20021725v...

I would suggest in future supplying this to the conversation yourself as it makes for a more productive discussion.

[1] https://www.nih.gov/news-events/nih-research-matters/immune-...

Can't comment on the other stuff.

Sure, the confidence interval is ridiculously large but the paper is open about its methods and conclusions. It's definitely in the "more work is needed" category, but I don't see how this information should not be published.