[w1ntermute]

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.

[snewman]

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.

[hanche]

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...

[DennisP]

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/

[jmull]

> 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.

[koheripbal]

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.

[jacquesm]

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

[seanmcdirmid]

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