Some Hard information on #COVID19 as compared to other epidemics in recent history.
==COVID-19==
R0 = 2.2
Global Mortality: 7%
Death Toll = 4,718 (and rising)
== 2009 Swine-flu ==
R0 = 1.5
Global Mortality: 0.04%
Death Toll = 500,000
== 2002 SARS ==
R0 = 3
Global Mortality: 9.6%
Death Toll = 349
== 1920 Spanish Flu ==
R0 = 2
Global Mortality: 2.5%
Death toll = 100 million
For those who don't know R0 is the average number of people who will contract the disease from an infected individual.
As you can see the numbers are very concerning. The only disease that had the same potential for damage as this would have been the SARS epidemic in 2002. Luckily it was contained early on and never spread. The big difference seems to be the 2002 SARS epidemic had very few if any asymptomatic individuals. So it was easy to stop the disease before it spread (artificially lowering the R0 effectively).
However the COVID-19 has a large portion of people with the disease whoa re asymptomatic. This causes the spread to go unhindered. Despite having a lower R0 and lower mortality rate the death toll is already more than 10x what it was for 2002 SARS.
The numbers are scary, it suggests to me, we are in for some really nasty times ahead...
@proxeus So much invalid data framing there I dont know where to start... What scholarly source did you get that from, I would be very shocked if any such source would frame data in that way.
The link you posted doesnt agree with any of the numbers you just posted either.. it clearly states the mortality rate was 7% (the number I stated) not 6% as you just stated, furthermore the number "0.9" does not show up anywhere on that link at all. Nor do any of the other figures you stated.
@proxeus My source is varied and many as I cited a lot of different data for different epidemics (as a data scientist I've been doing a lot of research on this).. For the COVID-19 data specifically I sourced that from the live data provided by John Hopkin's University.
@proxeus Ahh I see how you got such misleading data then..
Your percentage for all people under 50, for example.. aside from being a very arbitrary line to set, it also is invalid because your calling from data on "suspected cases" rather than "confirmed cases" which your dataset has no data for apparently. Obviously false positives by simply visually diagnosing someone is **huge** for a pandemic where peope are in fear. So those numbers are almost entirely invalid.
Though you will find of the people who have died fromt his disease about 80% are people who are older or who are comrpomised in some way. But that really doesn make it any less severe. We see that sort of pattern with most diseases like the flu, yet the spanish flu was still capable of killing 100 million and the 2009 swine flu capable of killing half a million. So not really a counter argument regardless.
@proxeus No the fact that it includes confirmed and suspected cases means the actual number is **higher** not lower.
simple logic should tell you that, If they suspect 100 people have COVID19 and only 10% actually have it and the other 90% just have ordinary flu. then you'd see a mortality rate that is the weighted average of the flu and of COVID19 with the flu being weighted 9x higher than COVID19 in that imaginary scenario. The flu has a mortality rate of about 0.02% - 0.04% comapred to the COVID19 mortality rate currently at 7%.. so you can see how including suspected cases that turn out to be flu would LOWER the mortality rate, not increase it.
@proxeus No your forgetting that if someone **survives** pnumonia but they were suspected of having COVID19 they also wind up int hat chart, which lowers it.
Since very few viruses have the mortality rate of a SARS virus, in this case 7%, any viruses accidentally included in suspected cases will lower the mortality rate rather than increase it. Your forgetting that both those who survive and those who die are factored into the mortality rate.
@proxeus I'm spreading the actual data from actual sources.. There is cause for some level of fear, feat is what keeps you alive and encourages you to be safe, so in that sense the fear is healthy, because the official data (which I shared) is very real.
You can wish and hope and lie to yourtself all you want that this isnt a serious epidemic, but you arent helping anyone by doing that...
But yes, there is a very good chance you will survive, no doubt. The spanish flu killed 100 million people and its mortality rate was even less at 2.5%. If you lived during the spanish flu you had a good chance of surviving too, but that doesnt make it any less of a tragedy nor the fear of the people at the time any less legitimate.
@proxeus We were both pulling fromt he official data. The difference is I was pulling from scholarly sources and using my expertise as a scientist to understand how to frame that data.
As we covered you however pulled from a valid data source, but you then proceeded to do your own math and draw your own conclusions out of it that were ultimately shown to be invalid as I just recently explained why.
The issue isnt the data, its the fact that you dont understand how to actually analyze the data in a way that you can draw useful conclusions from it.
1) are you even listening, yes I did and specifically addressed why your interpritation was faulty.. The data is only partly faulty as we covered the data from china prior to feb 12th has since been adjusted and redacted.. so yes, it is faulty.. second the math and conclusions you drew were also incorrect even if the data happened to be correct, I already explained why
2) no i gave the specific source of the data, the live data provided by John Hopkins University. Though I did indicate it was compiled from multiple sources for completeness. Here are the specific sources the data set is merged from:
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
https://www.cdc.gov/coronavirus/2019-ncov/index.html
https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
@proxeus Also keep in mind the numbers reported by china up to and include Feb 12th were later found to be invalid. The global data sets after that data had to be significantly adjusted to reflect the correct numbers. So any data out of china prior to feb 12th has inaccurate data.
This is all taken from official data.