This is a problem of insufficient data. We don't have a denominator, so there's no way to tell if it's more widespread than we thought. In fact there's no good way to tell how widespread it is at all. We have numerators: number of people sick enough to show to up to an ER, or number dead, or whatever, take your pick. But in every fraction we might want, we lack a denominator: total number of people infected.<p>There's a stuningly simple way to get one: sample randomly from a population of interest in large enough numbers (say e.g. Cook County, home of Chicago, or hell, the entire US) and test everyone for the virus. Voila! You have a representative chunk of your population, an estimate of virus prevalence, and a quantifiable degree of precision about that estimate (SE's for a confidence interval, say). With enough people in your sample, you can have extremely accurate estimates of the spread of Covid in your population of interest. You repeat this process over and over, through time, to track the numbers.<p>I'm not trivializing this kind of effort: it takes rigorous sampling designs and dozens or hundreds of field workers, among other things. It's intense but very straightforward. Political pollsters (and thousands of researchers in different fields) do it every day.<p>No one in the US has done this, and no one with any visibility from Fauci on down has even suggested it (that I know of; please correct me). I work in public health, and this is first year, first semester of grad school stuff.<p>This matters because the public health responses to coronavirus when there's 1% or 10% or 25% or 75% of a population infected all look very different. In short, we may be over- or underreacting to the situation with the measures we currently have in place.<p>I've been going half crazy wondering what's happening about this for a while now. After the initial fuckup, we now have tests. We have money. We can do this. Why don't we?<p>(Iceland has done this!)
% of visits to primary care provider is a _terrible_ metric. People have been actively advised to stay away from the Doctor’s office and hospitals for routine reasons (and reschedule for later). Obviously the % visiting because they think they have Covid is going up, even if they don’t have it!
Here’s a preprint of the actual paper: <a href="https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v1" rel="nofollow">https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v...</a>
I dont understand how would such highly contagious virus suppress and contain itself if it started in CA months ago. As if Californians have no outbound travel. Impossible scenario.
I was pretty sick in late February, and ended up in the urgent care at my local Kaiser. It was packed at the time too. Seemed like everyone was sick with the flu.
Was this data not publicly available ? Were there not army of analysts? I thought with Machine Learning being all the rage and hype, we’d have more people and machine keeping tabs on all kinds of trends, this included. Good that we have these retroactive inspections, but the whole appeal of ML is its predictive power is it not? Or did the hedge funds just keep this knowledge to themselves?
> "I think the 3.4 percent is really a false number — and this is just my hunch — but based on a lot of conversations with a lot of people that do this, because a lot of people will have this and it's very mild, they'll get better very rapidly. They don't even see a doctor. They don't even call a doctor. You never hear about those people,..."<p>At the time, the person who said that was called an idiot.