Yep. Ventilator usage [if available] and deaths are more reliable indicators, but they're also lagging ones [and even deaths aren't entirely consistently measured since many of the most severely affected patients exhibit comorbities, and many nations don't test the dead]

> deaths are more reliable indicators

They are more reliable than the simple number of cases but in the Italian province of Bergamo (and I suppose in neighbouring Brescia, too) lots of people have started dying before the ambulance even arrives at their homes. Those people will not show up on the official statistics of covid victims, at least not now.

A similar thing had started happening in Wuhan once the situation had become really dire, and the closest valid indicator people could find was to look at the number of incinerators and how busy they were (mostly looking at the number, because the incinerators were busy 24/7 anyway).

Others do, and that skews the results as well (at the very least, it makes numbers incomparable).

once people's minds are made up, it's incredibly difficult to walk them back to a different reasoning. Apart from the lack of early tests the problem is therefore exacerbated by the downplaying of the problem during the critical early stages. It wasn't just lost-time that needs to be made up for but also that mitigations will now become harder to swallow!

The death curve is also quite steep. Seven days ago we were at 57 total deaths, now it's 276. Daily deaths has gone from eight people seven days ago to 49.

https://www.worldometers.info/coronavirus/country/us/

Here's a good article on estimating current number of cases from number of deaths:

https://medium.com/@tomaspueyo/coronavirus-act-today-or-peop...

The death curve is also deceptive - there were likely many patients who were recorded as dying of the flu, or of influenza-like illness, that actually died of COVID-19, due to a lack of testing. The bottom line is that it’s very difficult to make a conclusive statement based on the data collected prior to widespread testing.

As for that Medium article, it’s written by a tech exec with zero education or professional experience in anything related to medicine or the life sciences, so I would treat it with a heavy dose of skepticism. Many of the deluge of preprint articles from academic institutions written by epidemiologists or public health experts would likely be a far better source of info.

Fivethirtyeight has been surveying epidemiologists:

https://fivethirtyeight.com/features/infectious-disease-expe...

Responding on March 16 and 17 to the question, "how many cases will the US report by March 29", their median prediction was 19,000. As of this moment, early on the 21st, that figure has ALREADY been exceeded (per Worldometer). Based on current growth rates, it appears virtually certain that the actual figure on the 29th will exceed the "best-guess" prediction of every single expert surveyed, and the "high-end" prediction of all but one or two. They've completely whiffed a prediction just a few days out.

This is a novel (no pun intended) situation, even for epidemiologists. To understand it, critical thinking skills are at least as important as subject matter expertise. My read is that the Medium article represents some of the best critical thinking on the subject so far.

Here is a report from a team of epidemiologists at Imperial College, led by Neil Ferguson. The title speaks for itself: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand. The paper is sobering reading. But of course, all the numbers plugged into the model are saddled with huge uncertainty.

https://www.imperial.ac.uk/media/imperial-college/medicine/s...

We have plenty of flu tests. Covid-19 is also pretty distinctive on chest CT scans, if it's advanced enough to kill you.

The numbers may be wrong in that article but the math itself seems straightforward. It's certainly true that we need more tests to be sure of the numbers.

Here's a pretty interesting simulation from a research group at the University of Basel: https://neherlab.org/covid19/

> Covid-19 is also pretty distinctive on chest CT scans, if it's advanced enough to kill you.

I guess you're suggesting that when people die of Covid-19, we'd know it and count it?

That's not true.

First, CT scans (and other expensive tests) are not generally performed on people who have died of pneumonia, apparently due to the flu (or other causes). Second, the radiologists reading those studies would have to train to make the distinction.

Now, going forward, we have an increasing need to know whether the virus that caused the pneumonia that killed the patient was Covid-19 or not, so maybe such testing will become routine. (I doubt it will be CT scans though, because there will be much cheaper and reliable ways to do it.)

But so far we've been undercounting Covid-19 deaths for the same reason we've been undercounting cases: lack of testing.

I was more thinking that CT scans would happen in advanced cases before the patients die. I did a bit of googling and found CT scans are not a definitive diagnosis but they are distinct from flu at least (just as koheripbal said).

But it's a good point that we might have more deaths than we've counted, and this might just mean we're further along that we realize. The rate of growth is probably not just an artifact of more testing; it's similar to the growth rates in other countries that haven't gotten control of the disease. And we definitely don't have it under control.

I expect that the number of people who die monthly for a certain condition to be relatively stable over time.

We can compare the usual number of people who die of pneumonia with the current one, and any significant difference can be attributed to this novel virus.

It's not distinctive from other lower respiratory pneumonia cases - it's only distinctive from the flu, so it could have been confused with other infections.

The death curve is a lot less deceptive than the number of cases.

Overall death rate is much less deceptive than known overall virus deaths.

not to downplay any death but there are roughly 100 deaths/day from car accidents and similar number is also from gun-related just to have some perspective. Unfortunate/scary thing is that the virus has the potential to eclipse those numbers by 10x easily

I am worried that we are choosing the second route with articles like this one: https://www.latimes.com/california/story/2020-03-20/coronavi...

If I understand article right, it is not about choosing. It is about not having resources and ability to do the other route.

The situation is exactly the same in Norway now, for the same reason: Lack of resources.

> Want to have a less steep curve? Lower the amount of testing.

That works only at the beginning.

A more interesting view would be the number of positive tests over the number of tests preformed.

Often tests are not performed on a random sample of the population but only on people that are symptomatic.

But not all symptomatic people receive tests.

Knowing those two number (number of symptomatic people who would have qualified for a test, were it available; number of test performed) could paint q better picture.

Until recently Ontario had very stringent testing pre-requisites: 2 or more symptoms, history or travel in the last 14 days. Even then, on average, only 2% of tested people tested positively for SARS-Cov-2. In my opinion that makes the notion of much more larger population having it and not noticing due to the lack of testing, at least at this stage, highly unlikely.

They should be doing some tests via random sampling anyways just to make sure.

Ideally, yes. But given the limited testing capability that is a politically hard thing to do. Even if 50-75% of cases are asymptomatic (and that is a very big if), that would mean that on average you have a number of asymptomatic people that is a factor of 1 to 3 of confirmed cases. While it would be great to isolate those people, at this point that is still not a huge number that means anything for the response.

Why should we assume different states have the same starting conditions?

The graph I think would be helpful would show how many positive tests per day out of a random sample of 1,000 (or whatever) people (non-symptomatic).