Here's a listing of the major paragraph titles in the article:
Our World in Data relies on data from the World Health Organization
Deaths from COVID-19
The growth rate of COVID-19 deaths
Cases of COVID-19
Growth of cases: How long did it take for the number of confirmed cases to double?
Confirmed COVID-19 cases by country
Trajectories since the 100th confirmed case
Testing for COVID-19
The COVID-19 pandemic
Strategies to respond to COVID-19
The intention of early containment
‘Flattening the curve’
COVID-19: What are the symptoms?
How does the disease progress?
What do we know about the risk of dying from COVID-19?
The definition of the case fatality rate (CFR)
Measuring and interpreting the case fatality rate
Global case fatality rate of COVID-19
Case fatality rate of COVID-19 by age
Case fatality rate of COVID-19 by preexisting health conditions
Case fatality rate of COVID-19 compared to other diseases
Data and dashboards from other sources
IMHO, the most important paragraphs in the article are:
Case fatality rate of COVID-19 by age
Case fatality rate of COVID-19 by preexisting health conditions
Using the data from these two paragraphs, I have conducted risk analyses for myself and my wife. We are both high risk.
Based on the risk analyses, we are self-isolating.
Comments are always welcome.
Look at the daily new deaths US. So far it’s all noise and no signal.
Yet population genetics is critical in understanding the spread and susceptibility of individuals and regions to this COVID-19 pathogen. The CDC and WHO studiously avoid listing the race of those who have become infected, seriously ill, have died or have recovered. It is politically incorrect but important data to understand this disease and just how to allocate resources and target patients who are likely to become seriously ill. So far it appears that while many are susceptible to infection with COVID-19, Asians, elderly Caucasians, people with diabetes and chronic lung conditions are those who are most likely not to recover from infection but become seriously ill or die. Thus far the Indian sub continent and sub Sahara Africa have been largely spared from serious disease. Probably has more to do with genetics than the heat.
Cov-19 study
3 authors. Leftist drivel. Search for my previous posts on PhDs and their beloved “data”.
For decades, every last whatever has had to be measured and analyzed by race. Suddenly, there can be no mention thereof.
A study by some very very very very very very very smart people who have very big degrees I’m sure. And they have lots of real world experience I’m sure.
Uh huh, right. The problem with data is the PhDs who flaunt it make the ignorant assumption that the data is good. Usually, it is not. Data crunchers, scientists, analysts, usually have no street smarts, no understanding of how a paycheck is derived, where money actually comes from, how customer behaviors are studied, normal life stuff. No, instead, they take their data prima facie, and move on...multivariate analysis (as an e.g.), etc. That’s not the real world. Taking into account count the Chinese migrant workers in the northern Italy leather industry would have been a wee bit important. Single payor healthcare system in Italy. Deaths in Italy, avg age 81. I’ll take my 21 yr old budding chemical engineer son’s street smart understanding of the world over 99.9% of PhDs’ anytime. My 18 year old even challenges him in that category. My to also don’t have a hidden agenda.
An excellent repository of research articles, updated daily
https://flutrackers.com/forum/forum/-2019-ncov-new-coronavirus/-2019-ncov-studies-research-academia
COVID-19 is different
Isn’t every strain of flu.
So that is why the death rate started at about 2% and is now nearly 4% (using Johns Hopkins numbers).