This analysis is pretty damning, and is more credible in context (the topline findings of the original study constituted extraordinary claims, which, if extrapolated, could imply that a majority of all New Yorkers had C19 antibodies).
Some of the underlying ideas here are pretty straightforward. For instance, the fact that even with 90+% specificity, if your rate of false positives exceeds the true positives in the population (as can happen even with good tests when the underlying condition is rare, as it is with C19), you're going to have problems.
Other antibody studies have consistently found similar results to the Stanford one. The Stanford numbers are very high - most have found closer to 20-30x reported cases than 50-85x - but it's virtually guaranteed that a huge fraction New Yorkers have C19 antibodies today. I'd be pretty surprised if it's less than 20%.
We don't know if these antibody tests are specific for covid-19 only or in fact for any coronavirus. We don't know if they randomly test positive either due to something else. There are a lot of unknowns. These antibody tests are not FDA approved.
They validate the antibody tests against blood collected before the outbreak. If the tests detect other corona viruses, they should get positive tests from blood that existed before Sars2.
Yes there are still unknowns, that’s the point of is science. FDA approval is irrelevant.
We lost a month at the beginning of this pandemic waiting for FDA approval of the tests confirming coronavirus was spreading in the US. I am unconvinced that this approval offered any value at all, and we can't afford to lose another month waiting to see how far it's spread.
Your statement amounts to "we have to do something. This is something. Therefore we have to do it."
Doing the wrong thing can be extremely dangerous in this situation. Consciously choosing to wait until you have greater certainty that the action taken is correct isn't the same as inaction, not when the downside risks of getting it wrong are so high.
I disagree that theorizing and gathering data can be extremely dangerous in this situation. If we're worried the Stanford study is flawed, the right response is to urgently do more studies, not sit on our hands waiting for some ponderous FDA approval process.
There's a certain limited amount of qualified research capacity representing a bottleneck. So, urgently doing more studies to refute one that has flaws and bias by the author isn't the right type of action to take if it will take resources from other, more productive endeavors.
Theorizing and gathering data is fine; going on to write a WSJ op-ed pushing it in a way you hope will influence policy goes a far step beyond proper scientific research protocols.
Standard scientific research protocols are heavily biased towards inaction. We can't afford that bias in the middle of a global emergency; it matters a lot if necessary action gets delayed for a month or even a week. Imagine how bad things would have gotten if we had made the same demands a month ago - if we had refused to institute social distancing until randomized controlled trials proved it helped for this disease.
There was no need to delay social distancing on account scientific uncertainty over the practice. We already knew social distancing was an effective method of preventing spread. Heck, we've known it since the 1918 spanish flu, since quarantines themselves begam. (and still, it was delayed, still is delayed in some places)
The point is that there is a resource bottleneck on "action". There are limited resources. You qualified your own statement as necessary action. How do you know what is necessary? Options must be weighed, the most promising chosen, as many, but not all paths can be pursued. I'm not saying "do nothing", I'm saying that we can't do everything. And then, yes, in areas of great uncertainty, where wrong action can cause more harm than choosing to wait, then we should not take action merely for the sake of "we can't do nothing!!!" emotional response to the crisis.
The linked article is the source. The analysis in the linked article placed the specificity within a range of "they could all be false positives" to "they might mostly be legitimate results".
I've seen in mentioned in a few articles criticizing unapproved tests, but it was more about the potential for this than actual testing. I think one was on CNN, but a quick search doesn't turn it up. There's just too much noise in the new-sphere right now to easily find a specific article from a week or two ago if you don't remember something ultra specific about it. You get, um, lots of false positives in your search results ;)
They are! I don't mean to deny that, there's a large discrepancy which Stanford and other researchers in Santa Clara County should work to resolve. But when newspapers and governments report numbers that are off by at least a factor of 20, a factor of 2-4 isn't the most pressing concern.
They report people who tested positive as cases. The testing is quite limited and almost never done for people without symptoms. It is typically done for people with strong symptoms. Existence of asymptomatic infected people is widely known at this point.
So I would expect undereporting in cases. So the additional 2-4 factor from study that is supposed to figure out underreporting is actually big deal.
Because reported cases where never assumed to be everyone sick (or infected) and I never seen anyone pretend they represent all infected people. Meanwhile, this study presents itself as estimate of immune people in population.
I've never seen anyone assert the specific claim "reported cases capture everyone infected". But I've regularly seen people claim that we know it's a very deadly disease because the number of deaths is 1-2% the number of reported cases, or claim that there's some burden of proof which other studies have to give to justify deviating from the reported count.
This is why you don't compare the current situation tothe known fatality rates of another pandemic for which the dust has settled and we already know everything. A better (not perfect) method is the CFR, where you compare the current CFR to the CFR of the other pandemic when that pandemic was ongoing.
If we take something like H1N1, the CFR during that pandemic was significantly lower than what we face right now [0] and was highly dependent on healthcare infrastructure. We're getting CFR rates, e.g. in NYC, far in excess of those seen in even the worst places with H1N1.
1.) Case fatality ratio, alias CFR, alias the number of deaths per the number of reported cases is NOT 1-2%. And pretty much never was. It was much higher.
2.) We knew it is deadly from observing meltdown in Italy and from observing China. Currently from observing New York.
3.) When you complain that reported cases "underreport" what exactly are you claiming? And what exact unfair burden of proof is there on this scientific study?
If the point is that cases are underreported, there isn't much of a difference between 20x, 30x, and 80x. The post was likely emphasizing that other studies have found even crazier numbers so 20-30x isn't unreasonable.
Many of the other studies have also been with populations that had relatively low numbers infected. Fortunately New York is about to do broad antibody testing themselves, so we should get better answers soon.
Almost every claim that's ever been made about the coronavirus hasn't passed peer review and has been criticized heavily. If you only accept non-controversial peer-reviewed information, you're pretty much going to be stuck with "COVID-19 is a new pandemic respiratory virus".
https://news.ycombinator.com/item?id=22899272
This analysis is pretty damning, and is more credible in context (the topline findings of the original study constituted extraordinary claims, which, if extrapolated, could imply that a majority of all New Yorkers had C19 antibodies).
Some of the underlying ideas here are pretty straightforward. For instance, the fact that even with 90+% specificity, if your rate of false positives exceeds the true positives in the population (as can happen even with good tests when the underlying condition is rare, as it is with C19), you're going to have problems.