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“When the facts change, I change my mind …” This single visualization of pandemic data says more than a 1000 blog posts do:
Nice visualization which illustrates two oft-neglected points/fallacies:
1. Just because something is extremely small, it can still be quite worrisome if it grows exponentially–used here in the actual sense with an implied substantial base, not in the journalistic sense meaning “fast”.
2. Conversely, just because something grows fast by some measure, it is not necessarily significant. Hence “fastest growing” is almost invariably a strong pointer that the following is panic mongering. Usually applied to something bad happening to a small, underrepresented demographic not stereotypically associated with that bad.
For example, during the height of the AIDS epidemic/panic you couldn’t turn on your TV without hearing a news or chat show producing drivel like: “The fastest growing group among AIDS sufferers? Nonagenerian widows! Last month there was 1. This month, this has doubled! Unless drastic action is taken, within a few years we’ll all be nonagenerian widows. With AIDS!”
RE: Sub Specie point #2 –
Several counties in rural/mountainous Colorado were identified as the next “up-and-coming hotspots” last week. One county that was mentioned had literally 1 case, but the county population is under 1,000 people (over 1,100 square miles I might add!). Checking again today that county is still at…hmmm…exactly 1 case! Outside of Eagle County – home of Vail, CO – the other mountain regions are sitting almost exactly where they were last week.
Preventing them from popping into the popular towns has certainly helped, but it seemed a bit melodramatic at the time to act like these rural areas were viral ticking time bombs.
Very good! Wonderful to see in-context numbers.
Does your data source for this have the daily all-causes mortality? I’d love to see a “live” version of that, along with the average daily values.
My wonder is whether the *actual* (not average) deaths from other causes (cancer, heart disease) declined. (Cancer patient got COVID-19, then didn’t die of cancer).
I’d also expect accidental deaths to decline because of the lockdowns.
Cheers,
Robert
In Europe, Euro Momo tracks all-cause mortality: https://www.euromomo.eu/outputs/zscore_pooled.html
I haven’t found a similar all-cause tracking source for the USA, but the CDC does track influenza and influenza-like illness: https://www.cdc.gov/flu/weekly/overview.htm
Someone at https://github.com/jsilve24/ili_surge/blob/master/Silverman_and_Washburne.pdf uses the CDC data to estimate symptomatic case detection rates in the United States.
P.S. average mortality data for the visualization matches CDC data from https://www.cdc.gov/nchs/nvss/mortality/lcwk1_hr.htm, but the visualization omits “all other causes,” which would come in a little bit below cancer if included.
Wow. Thanks, Joe.
I don’t need Google-fu. I have People for that! :-)
Cheers,
–Bob
No problem! We seem to be getting new data sources daily – CDC now has provisional death counts that I swear weren’t there yesterday :) https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm
The link to the Silverman & Washburne paper changed as well, having apparently gained an author. Try https://github.com/jsilve24/ili_surge and you’ll see the relevant document.
I surmise that we are looking at United States data. Average daily deaths per mortality type (for what year?) are in blue. Actual COVID-19 daily deaths are in yellow.
Globally, we’re still going to end up with a situation where more people die (primarily in developing nations) because of economic privation and because they missed their measles vaccination etc. than would have died in a completely unmitigated Covid-19 scenario.