Comment by jhpriestley | Hacker News Reader

Comment by jhpriestley | Hacker News Reader

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I've been reading about this virus daily since February and my impression has been the opposite. It's a very boring virus which has offered very few surprises. We knew since February that it is a flu-like respiratory disease with high rates of hospitalization and death, especially among the old. The initial estimates for fatality rate were about 4% and that has now been narrowed to 1% based on better testing and treatment. Social distancing and masks are effective at controlling the spread. All of this has been known since February/March, most of it is spelled out here: https://www.who.int/docs/default-source/coronaviruse/who-chi...

At the same time, there has been an endless parade of contrarians trying to make the whole thing into a big mystery and muddy the waters for some reason. There was the theory that the virus was spreading much earlier than expected. There was the theory that most cases were asymptomatic and that immunity had already been reached. The theory that lockdowns are not effective. The theory that there were different strains with highly different behavior. Some vague theory about t-cell immunity.

None of these contrarian theories have been supported by any real evidence, but there is a large appetite for them and people will seize on any puzzling number to try to rethink the whole picture of the virus.

This doesn't align with my tracking of virus facts.

1) the death rate is still unclear because very few localities have done sufficient and/or the right kind of testing to answer this in a definitive way.

2) initial estimates about hospitalization across age cohorts seem to have turned out to be fairly far off. Tracking the stats for my own state (New Mexico; admittedly a fairly small population), we sometimes have almost flat hospitalization rates across age cohorts.

That report was dated 16-24 February 2020. It was authoritative at the time of writing (hard not to be given the limited breakouts outside of Wuhan at that time), and the reporting on the physical structure and mechanisms of the virus have remained largely unchanged AFAIK.

But the epidemiological aspects of covid19 have, I think, changed quite a bit since that report. The report doesn't in fact coe down strongly in favor of masks:

"The relative importance of non-pharmaceutical control measures including masks, hand hygiene, and social distancing require further research to quantify their impact."

and in the section titled "Knowledge Gaps" includes "Effectiveness of the public health control measures and their socio-economic impact ... * Wearing mask in general public", along with most "lockdown" type policies.

1) I'm not a researcher and I can't claim to have a comprehensive view of the evidence, but I think there have been enough high-quality studies based on seroprevalence to give a good estimate. This one https://www.medrxiv.org/content/10.1101/2020.08.06.20169722v... found an IFR of 0.83% in spain, or 1.07% if counting excess deaths. This one https://www.medrxiv.org/content/10.1101/2020.06.27.20141689v... found an estimate of 1.39% in NYC. The WHO has estimated 0.6%. These studies are based on representative antibody surveys with good statistical power, they should be accurate. There are factors like demographics and comorbidities that can push the number much higher or lower in a given community, but the range does not seem that wide to me.

2) I haven't been tracking hospitalization data very closely, you may be right that this has changed ... I've seen little discussion of it.

I agree that mask use in particular was controversial for a rather long time, with bodies like the CDC seemingly dragging their feet on recommending the use of masks. Still, I think that it's been a pretty settled question since April (CDC recommended masks from April 3 https://www.livescience.com/cdc-recommends-face-masks-corona...).

I would agree with you that it does look as if the overall IFR is in the range of 0.8-1.6%, and that the Spanish study was well conducted. But it is also horrifying that a pandemic that has killed more than 1M people worldwide still has so few similar studies to help answer these questions.

2. I don't want to make strong claims about this, but I do think it has moved quite a bit.

3. Yes, mask utility seems really well established now and has been for "many" months.

I still wish we would hear more public speakers using the numbers from 1. above combined with "flattening the curve" to make it clear what we're trying to do. 1% of the US is a million people. That could still be the outcome, but at the very least, we'd like that not happen all in the same month!

We now know that antibody seroprevalence studies missed many of the mild cases. This is naturally difficult to quantify.

https://www.bmj.com/content/370/bmj.m3364

So the actual IFR is probably lower than the numbers you cited.

If you look at the responses to that editorial, they note that the 11% positivity of igA tests, cited as suggestive of a high level of undetected infection, is actually the false positive rate of the test, among other embarrassing errors.

The first citation on your linked editorial is Ioannidis, which is the same Stanford researcher in the OP article here, and the same group responsible for the utterly flawed Santa Clara study. It seems like it's literally this one group, and a few other weirdos and contrarians, singlehandedly raising spurious doubts about the science, and then being amplified beyond all reason.

The cited Ioannidis study also sucks, he takes a number of seroprevalence studies, including some very flawed or underpowered ones, and then takes the unweighted median for some reason? More details here, including an illustration of how the high-quality, randomly-selected samples do not vary so much https://twitter.com/GidMK/status/1283232023402868737

You're missing a big part of the picture. It is well established that some patients infected with SARS-CoV-2 either never produce detectible levels of antibodies, or have them fade away quickly. In order to measure actual infection numbers we need to look beyond antibody seroprevalence, and factor in CD4+ and CD8+ T cell assays as well.

https://doi.org/10.1016/j.cell.2020.08.017

90% of PCR-positive subjects in the ENE-COVID test, for example, did also have a positive antibody test. So actual infection numbers might be ~10% higher than the raw numbers from the antibody tests (and I believe that this is factored into the results reported by such studies). It doesn't change our big-picture understanding of the disease.

T cell immunity is hardly vague. There have been multiple peer-reviewed journal articles published on that topic.

https://scholar.google.com/scholar?hl=en&as_sdt=0%2C24&q=mem...

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