Okay, I'm not trying to talk down to you, you seem to be earnestly seeking the truth. From a statistics point of view, there are a lot of flaws with the numbers that will self correct to a point as more numbers come in. To be able to analyze anything you need a clean baseline, which in situations like this would involve a randomized, weighted sample. To do that, we needed a sample of probably 10,000 to 25,000 randomly sampled participants, weighted or proportioned to reflect age, race, socio-economic factors and the list could go on to all kinds of variables, like underlying conditions, etc. We don't have that so we really have no idea, for example, what % of the obese population tests, negative, positive or has antibodies indicating they had it and recovered. We don't know how many people had it and were asymptomatic.
What we do know is strictly about those who had symptoms that indicated a test was indicated. We don't have data on how many tests have come back negative, but we do have data on how many tested positive. That number will increase and not go to zero by the methodology of the charts we've talked about. With limited information, the best indicator is what is happening with the daily new cases and daily new deaths which are generally trending in a favorable direction. The daily new cases can be misleading because we aren't getting relevant data on number of cases requiring hospitalization- it's not 100%. Many small community hospitals in the west are recording 1-200 positive tests with 5-20 hospitalizations.
One way we can put this in perspective is to divide how many people have died by population, the latest mortality at 83,366 population estimated at 327 million which equals .00025. Just to make sure I'm clear.01 is 1% .001 is 1/10th of 1% .0001 is 1/100th of 1%, so we have 25/1000ths of the population has died. If we double the mortality we are still at 50/1000ths of the population. A week ago it was 18/1000ths, so we can see a reasonable view of what is happening and establish a trend. Same methodology we see that 4/10ths of 1% have tested positive, with no idea how many required hospitalization.
This is real rudimentary, it will take months for the numbers to be crunched, weighted and random samples to be developed, so there is some peril in making assessments based on what we have. The key is to determine which numbers are better indicators of trends and use those until better data is compiled. I hope this helps. Too many people (cont)