Kim Dotcom Discusses My New Zealand Analysis with Dr. Shiva Who Claims It is worthless; I’m Not Allowed to Speak


The new “science.” Blow up my analysis with hand-waving attacks and don’t give me any rebuttal time whatsoever?!?! Is that how we do science now?

Kim Dotcom ran a space today with Dr. Shiva Ayyadurai.

I found out about it yesterday, contacted Kim, but I was not invited to speak in the space. The purpose of the space was for Dr. Shiva to discredit my analysis and the data. So naturally, I wouldn’t be allowed to speak.

Wow. Is that the way we do things now? With a one-sided presentation from a guy who clearly doesn’t understand the subject area?

Dr. Shiva’s claims

Shiva spent 90 minutes to make the following claims:

  1. He is an expert on data analysis. He goes through his credentials and does a mini science class.
  2. Transparency: Health New Zealand should release all the data, not just 4M records.
  3. Need for controls: You need a control group. Without that, you can’t tell anything.
  4. Missing data: The records for a given person are not intact, e.g., the DB lists Dose 3 for a person, but not Dose 1 or 2. So you can’t do a cohort time-series analysis.
  5. Causality: You cannot say that the vaccine killed anyone due to points 3 and 4.
  6. The data supports the government’s claims that lockdowns worked.

In short, Shiva said this is a nothing burger and it supports the government narrative about lockdowns.

My space right after

I hosted a space immediately after the space. I invited Dr. Shiva and Kim Dotcom but both were too busy to attend. I even called Shiva live from the space!

My rebuttal

  1. Lack of expertise. Shiva has an h-index of 13 and 1,644 citations. Harvey Risch has an h-index of 110 and has 51K citations. Risch is a Yale epidemiologist and one of the top epidemiologists in the world. Shiva… I couldn’t find a reference to him as an epidemiologist. The question you have to ask is why did Kim Dotcom seek out Shiva instead of Risch? I called Risch and he said Kim never contacted him. So much for seeking out subject matter experts to opine on the data. Kim Dotcom failed his followers big time on this choice and on not allowing me to speak.
  2. We agree on transparency: Health New Zealand should release all the data, not just 4M records. This is the one point we agree on. However, not a single mainstream doctor thinks this is a problem. That’s the bigger problem!
  3. There is no need for controls: Controls are nice, but not required in this case. If you know what you are doing, you don’t need a control group to spot a signal on this one. In this LARGE whole population dataset, mortality skyrockets for 6 months or more after the shot. It does not do this in any large population which has not been given COVID shots. I don’t care what the comorbidities are, it doesn’t matter what the control group says. This is a large population. Mortality curves post-shot always slope downwards, with minor perturbations caused by big events, like a COVID wave and you’ll only see that if the shot is given over a small time period (otherwise it is smoothed out). Whether you do a time-series cohort analysis, or a simple plot of deaths per day post shot, the slope is well established. For example, for a simple plot of deaths per day post shot, for any large population, regardless of age mix, the slope is always down. We can see this very clearly in the Medicare data (which our “expert” wouldn’t know). So one look at the slope of the deaths per day post dose, and you know there is something SERIOUSLY wrong and sensitivity analyses can rule out it coming from a background event (you time shift the observation window and see the same effect). A positive slope is impossible unless you are injecting people with a drug that is killing them. In the case of these shots, I showed a Medicare slide showing a 26% increase in 1 year. That is insane. Nobody has ever seen that before. It’s a sign of a very deadly vaccine. There is no other possible explanation. No control group needed on that one.
  4. Missing data argument is a red herring: There is missing data, but it’s irrelevant. If I came to you with a dataset “here is a list of people who got shot #2 and died” you can tell a lot from this data, even if you don’t know if they got shot #3 or not. And you know they got shot #1 as well since this is shot #2. This data is more than adequate to establish whether mortality goes up after shot #2. Similarly, if I gave you shot #3 data, the same applies. I’m giving you different samples from large populations and the sample set is large. You can make independent assessments on each shot, even if the populations are not the same. For example, if 25% of the cohort dies on day 12 after dose #2, and 25% of the remaining cohort dies on day 20 after dose #3, do you think that was caused by the vaccine? Of course it was. No doubt about it. Also, in his argument about this, it was pure hand waving with no model to prove it. He didn’t even try.
  5. You can assess causality, there is no other possible explanation: If you have a 50% mortality increase in 6 months after a shot and nothing is going on in the background (which you can verify by sliding your observation window), that’s unusual and must be caused by something correlated to the shot timing. If it wasn’t the shot, what could be causing a mortality effect that large? We don’t know of anything other than the COVID vaccine that has a mortality curve like that. It’s unique. And it’s replicated in all 5 countries we have data on. This kind of a rise has never been seen before. So it must be something novel that is causing this. What is novel and correlated with the shot? Hmmm… got to think hard on that one.
  6. The data in no way supports the government’s claims that lockdowns worked. He presented no evidence whatsoever to back up this claim.

What do you think?


Barry Young, the New Zealand whistleblower, is going to spend 7 years in prison for exposing the data. That data can only be interpreted only one way.

Unqualified experts like Dr. Shiva do Barry a huge disservice; they need to apologize for their errors and sloppy science.

This data is the first time in the history of the world where record-level patient data related to a vaccine has been made publicly available. That is a huge deal. People should be ashamed of trying to discredit it.

Any real scientist would be asking “what can we learn from the data given the limitation?” The correct answer: a great deal if you do the requisite analysis.

Kim Dotcom should have his next session where Dr. Shiva is challenged on his claims by people with credentials in the field like Professor Risch or Fenton.

Kim: It’s time to expose the false prophets who spread misinformation like Dr. Shiva.

And Kim Dotcom should apologize to his followers for giving Dr. Shiva a platform for spreading misinformation and follow through with a subsequent session where Dr. Shiva is held accountable for his false and misleading statements.

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