A world-renowned data expert has dropped explosive new evidence confirming the link between Covid mRNA “vaccines” and global mass deaths.
Massachusetts Institute of Technology (MIT) computer scientist Dr. Steve Kirsch, the founder of the Vaccine Safety Research Foundation (VSRF), used advanced artificial intelligence (AI) algorithms to identify the cause of surging global excess deaths.
Kirsch, the inventor of the optical computer mouse, used the algorithms to produce an all-cause mortality analysis method.
This novel, simple method proves that Covid mRNA “vaccines” killed far more people than they saved, Kirsch revealed.
During an interview on the Alex Jones Show, Kirsch explained that his analysis shows that the “safe and effective vaccine” narrative is false.
Instead, he said Covid mRNA injections have killed millions of people around the world.
Kirsch noted that pharmaceutical companies are “killing people” with their “vaccines.”
He argued that Big Pharma executives should not “get a pass” when it comes to criminal charges.
“You can’t just look away like that when people are telling you that your product is murdering people,” he noted.
Kirsch also noted that Blumenthal is “not acknowledging the vaccine harms, or that it’s caused any deaths at all.”
Jones added that the senator is a “certified fraud.”
Later in the interview, Jones explained:
“We’ve got government numbers out of Europe, government numbers out of New Zealand,” showing surging all-cause deaths and injury among young people.
“Then we see overall mortality worldwide exploding – This isn’t even like a signal, this is the Ten Commandments written in stone,” he added.
Kirsch noted that his method proves these death surges are caused by Covid injections.
He notes that his method can also be used to prove that “vaccines cause autism.”
FULL INTERVIEW:
GLOBAL BOMBSHELL: Using Advanced AI Algorithms, Renowned Tech Inventor & COVID Expert Steve Kirsch Reveals Incontrovertible Evidence That The COVID-19 mRNA Vaccines Have Caused Mass Death & Illness
Kirsch Also Responds To The New Senate Report & Hearings That Document How The… pic.twitter.com/i1VtaGhygI
Kirsch published his “super simple, amazingly powerful method” on his Substack:
You pick a start of study date (e.g., when 70% of your population (which you divide up into same age groups, e.g., born in 1950-54) has been vaxxed) which defines who is in the intervention vs. control groups. Next, your look for a time period after the start date when no external stress relative to the intervention is present, e.g., the 3 months just after June 1 in the Czech Republic when there was no COVID). You start cumulating event counts at that point in each cohort to establish a relative baseline rate in the two groups while under normal external stresses.
This start date for cumulation will ideally be as close to the start date as possible, e.g., the same as the study start date.
Now all you do is plot the Ratio of (cum intervention events) / (cum control events) on the y-axis vs. time and look at the slope. The observation time period should include times when the external stress is applied so our two counters reflect the differential outcome response in the two cohorts. The slope of a line drawn between the cumulative ratio at the start of observation time till the end of observation time (typically 1 year later but could be any time period of interest where the external common mode stress is “supposed to” produce a differential outcome count, i.e., ACM deaths per week lowered in the vaxxed v. unvaxxed) tells the story of benefit or harm:
Slope up—> vaccine is clearly killing people.
Flat slope —> no change.
Slope down —> net mortality benefit.
There are two caveats to be aware of if you are dealing with a vaccine intervention *AND* your outcome is DEATH:
If there is a healthy vaccinee effect (HVE) observed in the two raw event count lines vs. time (it appears like merging traffic lanes that are later parallel and is very obvious), you must start count accumulation when the lanes have been merged. Below is what it would look like if present (it isn’t present in our dataset since most people were vaxxed way before the start point). Typically, you’d only see this in real life if you are looking at time series data where the deaths are relative to the time of the shot. See these Medicare time series death plots for the COVID vaccine showing the effect is gone 30 days post shot and is exponentially declining in impact (deaths rise quickly at t=0, slower as t increases and you’re at actual mortality by t=30 days or earlier). The slopes post that time are due to seasonality impacts (look at ALL the graphs and you’ll see that), not “long term HVE” which there is no such thing. See also the Pneumococcal vaccine curve (Medicare 2021 all ages) showing the HVE effect is gone in ~14 days.
If there is a non-negligible absolute negative slope in the control group, you need to subtract that slope from the answer which will alway reduce the harm or increase the benefit. This adjusts for the depletion effect in the unvaccinate cohort which behaves (mortality wise) like a group of people 10 years older than the age group you are looking at. So it always applies to cohorts of age 75 or older due to the depletion effect (older people’s absolute weekly mortality becomes comparable or greater than their annual increase in baseline mortality).
“That’s it!!! Simple and objective,” Kirsch said of his method.
“You can calculate confidence bounds from the 4 numbers in the ratio using the normal methods.
“You can shift the observation start time and window size to show you that the result is robust.”
