I think it's pretty easy to reach reasonable conclusions from what the doctor presented.
1. Lots of cases
2. Not many deaths (a fraction of a percent in all measurements)
Fauci and all presented lots of cases, lots of deaths.
So the question becomes how did Fauci and all get to their predictions? That question is probably easily answered too.
It is. The daily briefings allow us to infer their bias. They are data driven, and what they don't have data to support, they ignore, which amounts to a specific assumption.
They assumed that the only people infected are the ones that test positive. If you are symptomatic but not tested, you are not infected. There is no data that you were infected.
Sometime in about the past ten days they reported that now that the peak seems to be manageable, they can move on to get data relating to this assumption, with that data being from antibody survey. Birx made sure to tell us that ramping this up will take a few weeks because the tests are not known trustworthy.
Fauci's best case assumption was the disease is ten times more deadly than flu. Take a not unusual flu season, 50,000 dead, and multiply by ten.
At any rate, that initial "no data, therefore assumed negative" assumption is being exposed in current news. Golly, more people were infected than we thought.
Here is Fauci in the March 24 briefing:
The second thing is, I just want to reiterate what Dr. Birx said about New York. It's a very serious situation. They've suffered terribly through no fault of their own. But what we're seeing now is that, understandably, people want to get out of New York. They're going to Florida. They're going to Long Island. They're going to different places.The idea, if you look at the statistics, it's disturbing. About one per thousand of these individuals are infected. That's about 8 to 10 times more than in other areas ...
About 1 per 1,000 in NYC is infected. That was Fauci's assumption a month ago. It was the working assumption of the entire group. Nobody disputed it. Policy was made and that assumption is a critical factor in the epidemic calculus.
Note too, the assumption that the rate of exposure or infection outside of NYC was between 1 in 8,000 and 1 in 10,000. And then that this germ, this one in 10,000 germ, that would multiply as people passed it between each other at a clip of each of those infected persons infrects a couple more, and before you know it, 1 in 50 has it, and then all hell breaks loose.
The model was not just a little bit off. It was wildly off, and now we know it.
And these clowns want to do global warming policy based on computer model predictions.
Aside from the basic understanding of how our immune system works and the pitfalls of staying locked up in our homes, the biggest thing to understand here is the inaccuracies of the models we were given. With data over time, we can draw conclusions. Just as the models presented by Fauci and Birx, we now have real world testing and numbers. Yes, we extrapolate data to draw a wider conclusion.
This virus is contagious. Like the flu it can be deadly. The percentage of people who require hospitalization and those who ultimately die are fairly low. The underlying conditions of those who died may have led to a poor outcome even with the flu.
We need to get out and get back to work. Some people will get sick and a smaller percentage will require treatment. The longer we stay inside and hide, our own immune systems will weaken and cause us to be more vulnerable to common ailments that we were previously protected from.