I must be bored so I found the latest data and made a spreadsheet to calculate mortality by state. Nothing scientific about my findings at all but perhaps a glimpse at things to come. For mortality I just divided total deaths by total cases. Without doing any weighting by state population, my guess is that the current mortality as I define it is around 1.6%. Big states - CA:2.0%, FL:1.4%, TX:1.3%, NY:1.3%. Of course it only includes people who have tested positive so we would have to estimate the number of people who get it and never get tested. I have know idea how to estimate that number. Also a time delay factor could make this 1.6% number total garbage.
From what I can glean from the CDC website, it looks like the regular old influenza has a death rate of around 0.13% for those that actually contract the virus. For CV, the ratio of untested:tested contractions is the big unknown - if it's 10:1 then this thing is slightly more deadly than the flu. If it's 2:1 then it's going to kill ~200,000 people worst case - this of course assumes no social distancing, etc. The biggest problem seems to be the high rate of contagion; this overwhelms hospitals and worsens the overall outcome.
What amazes me is the cases versus deaths from the NYC area:
How can Westchester have just one death? It can't be due to timing factors as it had cases right at the start of the outbreak. Is it due to wealth and better lifestyle choices? Seems that even that wouldn't explain away the absurd variance.
Curious to hear your thoughts. If anyone wants the spreadsheet I used to come up with this then PM me.
[Full disclosure - I'm an engineer and not a statistician or pandemic specialist so go easy on the floggings.]
You’re doing about as good a job as my brother who is in finance. The problem is that you have no idea if the total number of infected is even close to accurate. There aren’t enough tests yet and the parameters for getting one seem to vary based on jurisdiction, time and individual care givers understanding of the rules. This is what people mean by “not knowing the denominator”. Bad data = bad analysis
I keep going through this with my bother, the nurse. He’s convinced, and probably right, that the lack of testing is causing the current fatality rate to be overstated. Once widespread testing is done I don’t doubt that the fatality rate for COVID-19 will be similar to Influenza. The concern I see is with the transmission rate (Ro), which appears to be much higher than for Influenza. The exponential growth on this thing has been frightening, with cases (and deaths) doubling every 3 to 4 days. Unchecked that would lead to massive numbers, hence the need for social distancing.
. The concern I see is with the transmission rate (Ro), which appears to be much higher than for Influenza. .
As Brownski says, it's very difficult (even for statistical geniuses) to run numbers with incomplete data.
And if you're talking to a "it's the same as the flu" person and they don't mention the difference in transmission rates or the fact that it's a "novel" virus (that we don't appear to be prepared for) rather than a seasonal virus (which we have systems in place to deal with), it's likely that that person either doesn't pay very much attention to the news or has an agenda.
Thanks to all for you responses - all good points. I understand the denominator problem and was trying to do some "back of the napkin" estimates to put some boundaries around the denominator. Without more complete all you can do is estimate and plan accordingly.
I think I just heard that parts of northern Italy are reporting a 9% mortality rate. That’s pretty alarming but it begs the same questions. Is it a testing issue skewing the statistics or an overwhelmed healthcare system that is no longer able to care for patients that otherwise might recover. Or it could be the media jumping on the most dire reports, regardless of credibility, to keep people glued to the tube. Certainly the raw numbers coming out of Italy are just horrible. It’s hard to determine what they mean for us but the people there are really suffering.