Just my opinion, with testing increasing we could be up to a million cases in a month.

[shockey80]

We will be up to 100,000 cases in a couple of days. Trump really fucked up and we are going to pay for it. Governors who have not shut down non essential businesses, schools are fucking insane. They are going to get a lot of people killed.

[gibraltar72]

1. Testing criteria is all over the map.

in Mi. we are getting about 1.2 positives for every two tests. I assume that is because we have only tested people with all symptoms because of scarcity. I see Maryland has similar numbers. I think some states are testing very few and some are testing much more randomly.

[Squinch]

2. We double every 3 days. So if we hit 100000 in 2 days,

200000 in 5 days,
400000 in 8 days,
800000 in 11 days,
1,600,000 in 14 days,
3,200,000 in 17 days,
6,400,000 in 20 days,
12,800,000 in 23 days
25,600,000 in 26 days
51,200,000 in 29 days.

So yeah. Safe to say we'll pass a million.

[shockey80]

4. I'm not sure I agree with your numbers.

I think it's better to add up how many cases they are finding per day. That number is rising very quickly.

[Brainfodder]

11. I'm more concerned about the # of deaths.

That can't be swept and should be front and center .

For those not watching that closely, it's been reported jackass was asking states to not report #'s.

DUH!

Transparency is Orange Blowfeld's enemy.

1 illness (or other) or leak away from fixing part of this mess, kind of a gross quandary, right?

[DeminPennswoods]

3. The more people who are tested, the better the data will be

for those predictive models.

For perspective, there are over 330M people in the US.

[shockey80]

6. I agree, It looks like they are finding over 10,000 cases per day now because of increased testing.

Yikes.

[DeminPennswoods]

8. That is still small in relation to the population of the entire US

nt

[Igel]

9. There's not much difference in the #, knowing or not.

Same number.

Try this. Since testing varies by state and by country, the "number of (reported) cases" isn't a reliable yardstick.

I continue to say that deaths are not perfect but more reliable. The problem with them isn't just "that person isn't going to be tested, but we assume it's not COVID" but "that person was a week from dying from diabetes and cancer, and COVID pushed him over the edge" will be counted as 100% COVID. Still, it's harder to avoid a corpse than a person with the sniffles.

The other thing to watch is the percentage of positives, but for that you need to know the total # of tests or the negatives.

You find 100 cases and you've tested 100 cases at random, everybody has it. You find 100 cases but you tested 100,000 sick people (who are more likely to have COVID) and you have a very low incidence in the general population. As the % positives go up, it means one of three things: (1) the trivial "the tester was really unlucky in pulling a biased sample"; (2) there are just more coronavirus-infected people, so the incidence is up; (3) there's a shortage of tests or a flood of people very ill with COVID, and so the tests aren't just for the sick, but the very sick, and the very sick have a higher incidence of SARS-CoV-2. We assume (1) never happens unless it's obvious it did; with increased testing, (3) is less likely, but possible in places like NYC where the positives are at 30% over the last couple of days. In Texas, average is 7%, the virus isn't as common.

[Afromania]

5. I have the feeling if we could deploy blanket testing of any of these cities

we'd find many more people have thing than thing than any of us know and have for some time.

[Siwsan]

7. The data I will find interesting is the # of positive cases hospitalized, vs home care

And, down the road, if there is a way to determine, via routine blood tests, if the antibodies are still to some degree present in those who maybe didn't even realize they were contaminated.

[Igel]

10. There are all kind of models that fit the data.

That's the thing about models with a number of variables, you fit to the results. Then your model predicts things and you can check--but for now, not much predictive ability. I mean, there are predictions but all the data has noise in it making everything a bit shakey, so if your predictions are dead-on with the numbers, you're probably wrong.

We hear the models that suit us. The general mood here is mostly, "This is horrible, we're all doomed" because it feeds the Trump-bad/future-horrible-unless-us model. (And even with-us, it'll take years to recover.)

One model has the assumption that most cases are symptomless or nearly so, so in Britain most people have already contracted it. That has predictions for how bad it'll get there, but they're not in that range yet. Serological studies, looking for antibodies, would fix that problem. But while they exist, I don't know that they're in production or most people much care about them. Yet.

=============

Most models assume things'll get a fair amount worse, but they're all simplistic and don't include feedback. The assumption is an infinite set of people the virus could spread to, and that every infected person has the same probability of running into any other member in that infinite set. We live and work in networks, even where networks are smashed together in a place like NYC. And while from a single virus' perspective a place like NYC appears infinite at the start, and the model works early on, it's really not infinite.

[dweller]

12. other numbers to watch

i look at worldometer, have been all along so am sticking with it for now
but look at the 2 columns total deaths and total recovered
china has the greatest difference between the 2
italy has more deaths than china but recovered is around equal
US totals are near equal, but has the largest # of new cases, and we're
just getting started.. hopefully the recovered grows faster than the deaths

✌🏼