Confidence intervals for the models were in far, far, FAR too broad a range to be accurate.

Oh, the models have been plenty accurate in one important respect. We know it’s an insanely infectious and fairly deadly disease that multiplies exponentially if left unchecked.

As for predicting the final death toll:

1. The outbreak isn’t over yet, so we obviously can’t yet assess whether and which death predictions have been accurate.

2. The disease is brand-new, and we don’t yet fully understand it: including the very important question of how many people have already been infected That we don’t know about. Because we don’t yet have antibody tests up and running for everyone. That information is necessary to arrive at an accurate “death rate.”

3. The future progression of the disease depends upon actions governments and individuals take in the future, which are out of the hands of public health officials.

4. We don’t know if and when a real cure/vaccine will be found and scaled into production.

A few reasons for very wide confidence intervals. I know you want science to give us all the answers right away, but science doesn’t work like that.

Nice try. Really cute attempt. LOL. You used numbered points and everything...

For those of us who have built spatial and statistical models, all of this crap with the COVID-19 models bandied about in the press brings to mind George Box’s dictum, “All models are wrong, but some are useful”—or useless, as the case may be. It would really depend on what you wanted to accomplish -- herd immunity or economic destruction, I suppose.

Anyone who has seriously studied statistics (or actuarial accounting) knows that simple confidence intervals can provide decent insight regarding the precision, reliability, and/or usefulness, of the estimates proposed by reductionist models (note: reductionist models are what the epidemiologists and virologists at the CDC are using). With the COVID-19 models, the so-called “news” appears to be using the confidence interval from one model and the actual estimated values (i.e., means) from different models as a way of reporting a range of the “predicted” number of people who may contract or die from the Wuhan Coronavirus (e.g., 60,000 to 2 million).

Either way, the range in estimates is exceptionally large and, quite frankly useless, at least for helping decision-makers make GOOD decisions about our health, the economy, and the destruction of our civil liberties. The range is quite useful in making emotional, ill-advised, or knee-jerk decisions however. Yet, despite their unreliability, these models continue to be touted as the reason governors and mayors refuse to allow basic freedoms guaranteed by the Constitution and paid for with the blood of veterans.

The armchair analysts’ descriptions (CNN, et al) about these estimates show how clueless they are of even the simplest of statistical interpretation. <------------- Please read that again.

The fact is, when a model has a confidence interval as wide as those reported, the primary conclusion is that the model is imprecise and unreliable. It should NOT be used to make far-reaching decisions.

It would be antithetical to the scientific method if such data were used to make decisions.

There are volumes of scientific literature that explain the limitations of reductionist methods, but encouraging the Press or their uneducated echo chamber on this site to investigate this further is a complete waste of time.

Okay

What data would you use at this time to make far-reaching decisions?

That's the point. They should convert a bunch of factories into manufacturing of the virus tests and then test as many people as humanly possible, from the data collected, they could then make accurate models to base decisions off of.

Fact 1: The overwhelming majority of people do not have any significant risk of dying from COVID-19.
Fact 2: Protecting older, at-risk people eliminates hospital overcrowding (the models were wrong here too).
Fact 3: Vital population immunity (aka "herd" immunity) is prevented by total isolation policies, prolonging the problem ... and it appears that prolonging the problem to November is the end goal.
Fact 4: People are dying because other medical care is not getting done due to wildly inaccurate hypothetical projections.
Fact 5: We have a clearly defined population at risk who can be protected with targeted measures without destroying lives and crashing our economy.

HOAX.

Another HOAX.