Dead Twitter Threads #4 — Digging on CDC’s Covid Deaths BS, Part 1

Post-Mortem

Twitter suspended my account at the beginning of this year; but I have a few threads from 2020 that I want to re-post here under Devolution.
I’ll call them the Dead Twitter Files

This was my Twitter 2-part thread to try and untangle the crap CDC data regarding COVID-19 deaths.  Of course it was garbage, as we all know now beyond a shred of doubt, the CDC could not accurately report it, because the whole pandemic was a scam from start to finish.

In Part 1, I located CDC data and tried to make sense of it all.  It’s confusing and just as I was working my way through it, the CDC revamped the datasets.  So I had to scrap all the work I did to “cleanse” the original data.  I then started over and broke it down in Part 2.

I downloaded the data daily (M-F) for the whole month of May and most of June.  I did a very short Part 3, but didn’t archive it, as I basically wrote that there were no new insights — all garbage, but I’d continue monitoring in case something changed.  Then I scaled it back to just doing a weekly download. 

By the end of August 2020, I saw no point in continuing as it was all bogus.  The proof is that between 2016 and 2019, when one looks at the annual “excess” deaths, there’s a slight upward trend, merely a function of a growing and aging population.  But the total deaths for all of 2020 is actually just a tad over 2019.  And compared to other causes of death, COVID-19 is a nothingburger.

Annual Death Rates in Germany

So without further ado, here is Part 1.

 

Originally posted on 08 May 2020, 8 tweets, 8 min read

1) OK, it took me a couple of days,

but it’ll be a while yet before I get anything useful. When working with data, my pet peeve is garbage… GIGO…

Methinks the CDC database admin in over his/her head…

@Scavino45@realDonaldTrump@DonaldJTrumpJr



2)… First things first,

examining the source data from CDC, as of 02 May 2020.

data.cdc.gov/NCHS/Provision…

14 columns in this dataset…

WTH is “Start Week” & “End Week”? (Strike 1). If up to me, SW would be Sunday’s date and EW would be Saturday’s date – more useful.



3)… Anyhoo, gotta deal with the given data.

I exported the data to Excel and made a table of it. Plan is to structure the workbook, so I can simply import updates and use pivot charts, formulas etc for review.

Attached is table unfiltered; 87,638 COVID-19 deaths???



4)… Table includes all U.S. Deaths

OK, let’s filter that out.

🤔 Only 43,622??? But fake news have been screaming over 55k since last Friday.



5)… There are also 2 sets of entries, New York and New York City…

🤔 Is NYC a subset of NYS? No clarification from DBA (Strike 2). Assuming NYS, includes NYC, let’s filter out NYC…

Only 31,024 COVID-19 deaths?



6)… Let’s look closer at the NYC vs NYS.

Filtering for only those two and filtering only for the past 2 weeks, we see that NYC has more deaths than NYS, ergo “New York” must mean the “Rest of New York”…



7)… I downloaded today’s updates and the table is twice as large,

instead of 14 weeks, I count 28 weeks, apparently all duplicated 🤷‍♂️ (Strike 3). Garbage…



8)… Preliminary review of the other categories of death don’t add up.

As @DrDannielle surmised, perhaps totals are being inflated, or DBA is incompetent.

Either way, I need to analyze the source data to figure out what’s going on. I’ll update when I have more.

/End

Continue reading Part 2 here…

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