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Comment by JetSetWilly | Hacker News Reader

This paper seems to have every imaginable scenario of which 2022 is a worst case.

But, now actual data is starting to come in - such as the extremely interesting serum testing in Scotland (1) - which suggests that the infection rate is much much higher than previously supposed. The Scotland serum testing shows that around 35000 people were infected at a time when only 110 cases were known officially, and strongly suggests that at least 10%-20% of the country have been infected by now.

Similar data has been suggested by the Danish serum testing.

So I'd be interested to see what comes out of this Harvard model if they put in the numbers suggested from serum testing.

1. https://figshare.com/articles/Serological_analysis_of_1000_S... )

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And also a similar study in Germany with similar results [1]. The result is of course to mechanically reduce the fatality rate to a number close to that of the flu (the problem for hospitals being not so much the fatality rate than the high number of infections), and well within the 0.1-1% range that US health authorities have been advertising for a while.

Regarding models, it seems 0.15% is the new basis [2].

Even on the optimistic end of the range (0.1% fatality), with 13k deaths, that's 20% of the UK population infected, a long way from achieving herd immunity.

The other thing is that I invite people on hackernews to better control their anxiety. It is not so long ago that pointing that case fatality ratios in jurisdictions that didn't do mass testing and where there seemed to be many unreported cases with mild symptom was overestimating the fatality rate would get you treated of conspirary theorist. South Korea had been measuring a fatality ratio in that range (~0.6%) over a month ago and that was probably already an upper bound (due to not testing 100% of the population).

[1] https://mobile.twitter.com/AmeshAA/status/124944656101010637...

[2] https://mobile.twitter.com/AmeshAA/status/124902067755179622...

The people who were treating that hypothesis as an absurd conspiracy theory last month probably won’t own up to it, but I do hope they are reading.

That's not really what those people were saying.

It doesn't matter if the number is 5% with a low infection rate, or 0.5% with a very high infection rate: both will lead to very large numbers of dead people unless we take severe action early on.

And so far they've been right. Covid-19 is causing huge amounts of excess mortality, even when compared to a really bad flu year.

Source? Europe shows excess mortality spiking to a similar level as the 2017-2018 flu season: https://www.euromomo.eu/

And that’s with elective procedures being cancelled and everyone scared to go to the hospital for any reason. Here’s some new data indicating that half of the excess mortality in England might not be caused by COVID-19 itself: https://archive.is/2eKCW

NYC population is 8.4 million. Their death rate is 6,589 (Apr 13). 0.1% of 8.4 million is 8400, that would mean almost everyone in NYC has been infected, I'm not sure that is feasible at the moment.

https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...

It would only take a 0.25% fatality rate to imply 30% of the NY population to have been infected, which is plausible.

South Korea’s fatality ratio at the time was obviously lagging, and has since gone above 1%. The true infection fatality rate might still be low but certainly the final case fatality rate won’t be, even with their mass testing.

As of April 9, according to Wikipedia, the CFR of covid-19 in South Korea reached 1.95%

The early pointing to South Korea’s CFR as low turned out to be misleading in the end due to I guess some issues relating to the data (probably because many cases were ongoing and the persons had neither died nor recovered).

That's a lot of interpretation from 6 cases. There's a lot of serological testing being done right now, and we'll have hard data soon, but from what I see from expert epidemiologists (disclosure: I'm not one myself), the idea that we have a vast number of completely asymptomatic cases is wishful thinking.

Ah, but if you do a sample of 1000 then even low numbers of positives are still statistically significant. The poisson distribution on just 6 might be somewhat wide but you can give a reasonable confidence interval (ie 0.3-0.9%) - and either end of that interval still suggests a lot of undetected cases at a time when there was hardly any officially.

What you can't do - statistically - is say "oh there were just 6, that means it all could just be a coincidence and the prevlence is actually very low".

Especially when - if you dig into the numbers they make sense. First, they were spread over a 2 week period, with all the positives being in the second cohort - which is what is expected if there's an exponential explosion of cases going on. Secondly, 4 of the 6 positives are in the Edinburgh area which is the most internationally connected and affluent area of Scotland.

So I really don't think it can be breezily dismissed just because you don't like the data. But as you say, we'll have a lot more serological data before long so lets see.

edit: removed for potentially being misleading.

Cross-reactivity is an issue in the ELISA test (the actual antibody test).

But they validated their results with a second step, the "pseudotype neutralisation assay". This test is highly specific for SARS-CoV-2 - as I understand it, they essentially introduce the virus to the hypothetically immune blood samples and then test that it is indeed neutralised by antibodies.

So, I think this Scottish serology test is pretty robust and accounts for these issues with cross-reactivity and specificity of the testing. In addition - they used a control sample from December 2019 none of which tested positive.

Yeah, I read more into their assay and I'm inclined to agree with you.

0 out of 100 controls and 6 out of 500 gives a very wide confidence interval. You could easily get those numbers with 1% false positives and no true positives in either group. Is your prior for false positives low enough to rule that out?

They did two independent assays so I would think the false positives rate should be far below 1% - certainly the authors seem to think so.

Secondly if they were false positives, I would expect them to be randomly distributed. Not concentrated in the affluent capital city, and not solely in the second cohort with none in the early march cohort (as you would predict if exponential growth is occurring).

Why even test 100 controls if your conclusion relies on a prior that false-positives are much less than 1%? I think that shows the author's are worried about false-positives and have failed to rule them out sufficiently.

Your second two points are fairly compelling.

Here's an expert thread (for example): https://twitter.com/richardneher/status/1250533354560159745

The tests might be inaccurate. Current antibody tests seem to conflate the common cold with covid-19, as they are both coronaviruses.

https://edition.cnn.com/2020/04/14/health/coronavirus-antibo...

That is not the case for this study, see here for why:

https://news.ycombinator.com/item?id=22879363

That is good news if that's the case!

There is a study in Germany done on an outbreak in Heinsberg that has a similar result.

When “sero samples” are done like this is there any effort to notify the donors their blood has been tested and come back positive for the antibodies? Ideally donors would be notified either way but at the very least I think the people with positive results ought to be informed.

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