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I analyzed the Leaked NZ Whistleblower Data and Suggest to Be Wary of It

The data has huge gaping holes in it and may be doctored

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By now, almost everyone has heard of “New Zealand Whistleblower Data.” Many people are discussing it, and I want to weigh in with my skeptical opinion. While I oppose Covid vaccines, I owe my subscribers a duty to report truthfully. My post should not be interpreted as “pro-vaccine advocacy.”

This post is too long for email due to an excessive number of images. Please click on the title to read the story online!

Bad Data and Inconsistent Story Be aware that the “leaked NZ data” is problematic; even the story accompanying it is less than entirely believable.

I spent an entire day analyzing it.

I downloaded it as a CSV file, uploaded it to my MySQL database server, and analyzed it. As I will show,

The “whistleblower data” is missing huge chunks of information that should logically be present.

Liz Gunn of NZ is misinterpreting it by trying to pass normal nursing home deaths as evidence of “super deadly batches” and “mass vaccine casualties”

The data has problems that are incompatible with the story of its origin . It cannot be a full snapshot from a working payment database. Therefore, the story of its origin is suspect.

The actual vaccine casualties may reside in the missing pieces of data that the “database” does not provide.

The Story You may have heard that a brave whistleblower, a database administrator in New Zealand, saved secret Covid vaccine death data and passed it to Liz Gunn .

ht tps://twitter.com/KimDotcom/status/1730230555000574003

The data he extracted includes 4 million records containing:

Individual person ID

Vaccine type

Batch Number

Vaccine dose number

Vaccination date

Date of birth

Age

Date of death, if applicable

I spent half a day yesterday researching this data. In my post below, I will refer to the above video and question the “official” story.

What I did I downloaded the data as a ZIP file. Inside was a large CSV file named “nz-record-level-data-4M-records.csv” with 4,193,438 records. I imported the data to a MySQL database. I chose the combination of individual ID and dose number to be the primary key. Due to 47 records with duplicate primary keys, only 4,193,391 rows were populated.

An example of one of such 47 duplications is shown here: person 152,535 was recorded, in contradiction, as having received their first dose twice, on the same day, 12-07-2021; one AstraZeneca and one Pfizer:

Missing Pieces The total number of DISTINCT INDIVIDUALS in the “leaked” database is only 2,215,729, even though 4.3 million New Zealanders received COVID vaccine s.

So, half of vaccinated New Zealanders are missing.

A lot of doses are also missing, with some people having only dose 5, or doses 3 and 5, etc.:

461,900 people have only doses 4 and 5 recorded:

Only 966,989 records of Dose 1 exist, even though the database contains information on 2.2 million people and 4.3 New Zealanders were vaccinated.

Over half of the people in the database (1,248,740) are missing records of Dose 1

Out of 2,215,729 individuals in the database, a little under half (1,024,375) are missing the record of Dose 1 and 2

Mess With Batches The batch_id is supposed to refer to a particular batch of a particular vaccine, like Pfizer or Novavax. Instead, there are numerous “batches” that contain multiple vaccine types.

Deadly Batches, Mass Murder Sites, or a Total Misunderstanding? The Liz Gunn video poignantly discusses “deadly sites” and “deadly batches.”

This video by Kim Dotcom reports that 21% of people receiving “Batch 1” died. The video’s underlined line shows 21.38% dead.

However, the numbers do not match the leaked database, which has 2,979 people who received Batch 1, and 375 of them died. That’s 12.59%, not 21%.

What kinds of people who received Batch 1 died?

People who received Batch 1 were quite old. The average age of all Batch 1 recipients is 67 years old. The average age of all DEAD Batch 1 recipients is 86 years old. Batch 1 was given 2.5 years ago, so recipients had plenty of time to die naturally.

Another fragment of the video discusses “deadly sites.” According to Liz Gunn, some sites were mass murder vaccine sites . For example, Liz and the whistleblowers refer to one vaccination site, Te Hopai Hospital, where 30% of vaccinated individuals died after vaccination.

But Te Hopai Home and Hospital is a NURSING HOME for old and dying people unable to care for themselves :

Is the death rate of 30% in a nursing home where residents had 2.5 years of post-vaccine exposure excessive? I am not a nursing home expert, and I am not sure – but discussing a 30% death rate without mentioning that this is a nursing home is disingenuous.

Is this “Leak” a Psyop? I do a lot of things. One of them is administering the database for Algebra.Com, a website with millions of monthly visitors and over a million of answered math questions.

So, I understand database administration. The story of a bona fide “leak” does not make sense to me. The data does not have the integrity that a full leaked data set would have.

This is supposed to be a payments database containing information for payments to vaccinators.

How can a payment database have such holes and missing data?

Was data selectively removed from the database before the leak?

How can batch IDs refer to multiple vaccines?

Did both the “whistleblower” and Liz Gunn honestly forget to check that these “deadly vaccine mass murder sites” are nursing homes ?

Do the missing records of first vaccinations (doses 1-2) hide real vaccine deaths, making Liz Gunn go on about “deadly nursing homes” instead of looking at deaths actually caused by the COVID vaccine?

Was the “leak” an intentional attempt to sow confusion, as it occurred with the old, pro-WEF, and vaccine-crazy NZ government still in place during the last days of it?

There are many questions to which I do not have an answer.

I want to invite my readers to discuss this “leak.” I expect vigorous disagreements and hope my mistakes, if any, will be highlighted and corrected.

What do you think?

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