31–40 of 75 posts
- Number of deaths per capita
- Number of "severe" cases per capita (good indicator of the future number of deaths)
- Number of tests per capita (good indicator for whether or not "number of cases" means anything at all)
Earlier quoted context omitted.
One reason is that daily increases are very volatile/noisy. The standard way to handle this is to use n-day moving averages, but they have a time lag and are harder to interpret. Financial Times has the best graphical trackers I've seen. They used to show cumulative counts, but switched to 7-day moving averages of daily increases" recently. https://www.ft.com/coronavirus-latest I agree that displaying increases/growt…
Not really. Data for Spain can be perfectly used and fit without averages on daily increases => https://media-exp1.licdn.com/dms/image/C5622AQH1JmaVMs3mqQ/f...
Definition of deaths varies widely, testing policy varies widely, no statistical extrapolation is being done in an attempt to avoid undermining public health policies that might be based on total fantasy.
Why all graphs are cummulative instead of new cases for that day? It's harder to notice how it is growing that way (i.e. more or less new cases than the previous days) and harder to see if it is going exponential, lineal or whatever. And, of course, forces to use log scales because the accumulated number is already high. At least for networking graphs is more meaningful to see difference from the actual from the prev…
And that shows pandemic effect rising and falling, because many cases either don't reach a hospital, get misclassified etc
But getting a daily ticker of total deaths (all deaths not just covid) in most parts of the world gets shutdown quick by politicians of all stripes.
Why all graphs are cummulative instead of new cases for that day? It's harder to notice how it is growing that way (i.e. more or less new cases than the previous days) and harder to see if it is going exponential, lineal or whatever. And, of course, forces to use log scales because the accumulated number is already high. At least for networking graphs is more meaningful to see difference from the actual from the prev…
One reason is that daily increases are very volatile/noisy. The standard way to handle this is to use n-day moving averages, but they have a time lag and are harder to interpret. Financial Times has the best graphical trackers I've seen. They used to show cumulative counts, but switched to 7-day moving averages of daily increases" recently. https://www.ft.com/coronavirus-latest I agree that displaying increases/growt…
Same, but interactive and more powerful: https://www.datacat.cc/covid/
These numbers are effectively meaningless. Definition of deaths varies widely, testing policy varies widely, no statistical extrapolation is being done in an attempt to avoid undermining public health policies that might be based on total fantasy.
"But what about the co-morbidity?". People don't die "of" COVID-19. They have heart failure, kidney failure, or other triggers of death because COVID-19 pushes their body to the limit. Pointing to resources that claim that "only" some small percentage actually died of COVID-19 is pure ignorance, because then the declaring doctor was simply being efficient because if they truly looked in there would be another triggered cause of death. Just as no one died of "AIDS", they died of things like Kaposi sarcoma, but if someone said "see, it wasn't AIDS at all" they would be laughed out of the room.
The majority of deaths are people who are health compromised in some other way (not all deaths, and there have been an abundant number of completely healthy people who have perished), but that is known by everyone and is not news, nor does it diminish the tragedy.
"no statistical extrapolation"
This is the most interesting, and ridiculous, claim of all. Enormous statistical measures and extrapolations are being done daily...that's how we are where we are. What is this meaningless claim even trying to say, other than that you, easytiger, know more than every health authority.
All of these need to be normalized per capita. Otherwise you don't see the true extent of the problem. Also from looking at the stats recently, here's what I find more useful than the raw number of "cases": - Number of deaths per capita - Number of "severe" cases per capita (good indicator of the future number of deaths) - Number of tests per capita (good indicator for whether or not "number of cases" means anything…
Normalizing per capita replaces the true extent of the problem with the true relative local impact of the problem; both are significant.