Santa Clara County's Fake COVID Statistics

Responding to Steve Kirsch

Steve Kirsch asked a very good question: how come the COVID statistics of Santa Clara county show the effectiveness of COVID vaccines far exceeding that reported anywhere else in the world?

Did you know that in Santa Clara County, the vaccine works 10X better than the rest of the world?

The answer, of course, is that their charts are fake. Santa Clara County undercounts unvaccinated people, and their Covid case rate dashboard even contradicts their own vaccination dashboard!

What’s going on?

Santa Clara County’s wastewater COVID measurements show another wave of Covid that exceeds last winter and summer.

How come this highly vaccinated county, suffering from the fifth wave of Covid infections that is setting wastewater records, shows graphs illustrating the alleged extreme effectiveness of vaccines? Can we believe them?

Look at the chart of case rates by vaccination status:

The case rate among the unvaccinated is 177, while the case rate among the vaccinated is 23 per 100,000. This implies vaccine effectiveness at preventing infection, as

Vaccine Effectiveness = 1-23/177 = 87%.

Santa Clara County says that this December of 2022, vaccines are 87% effective at preventing infection. Could this be possibly true? Is there a sleight of hand involved?

SCC excludes so-called “partially vaccinated people” from their case rate chart

People who are partially vaccinated, meaning they only received one dose of a two-dose vaccine or who are not yet 14 days after their final dose, are not included in either category.

That’s a common dishonest trick not to count illnesses that happen in two weeks after receiving a booster. The one-dose people were mostly vaccine-injured and, therefore, unable to continue vaccinating. All these people should be considered “vaccinated.” Excluding them is cheating.

It gets worse from here, however.

Are Case Rates Based on Undercounting Unvaccinated People?

We have a basic formula (CR means Case Rate):

Overall CR = “Unvaccinated CR” * “Unvaccinated Population Percentage”

+ “Vaccinated CR” * “Vaccinated Population Percentage”

or

Overall CR = “Unvaccinated CR” * “Unvaccinated Population Percentage”

+ “Vaccinated CR” * (1-“Unvaccinated Population Percentage”)

From the Santa Clara County graph, we know everything from this equation (the bolded pieces above). We do not know the “Unvaccinated Population Percentage,” which we will replace with “X” to look more like an algebra equation.

25.2 = 177*X + 23.0*(1-X)

This is a very simple “linear equation” involving one variable, “X.” Solving it:

25.2=177X + 23 - 23X
25.2-23=154X
154X = 2.2
X = 2.2/154 = 1.4%.

So, the Santa Clara case rate graph implies that the unvaccinated are 1.4% of the combined population of “fully vaccinated” and “unvaccinated” persons.

But does Santa Clara have such a low percentage (only 1.4%) of unvaccinated persons? Of course not. Santa Clara County has 12.5% of its population unvaccinated or once-vaccinated.

Santa Clara lists 114,057 persons (6.0% of its population) as “partially vaccinated.” Those people are not included in the case rate analysis. So, the case rate table suggests that the never-vaccinated people comprise 1.4% of Santa Clara, whereas the vaccination progress page shows that 12.5-6=6.5% of people are unvaccinated.

In other words, Santa Clara County undercounts the unvaccinated, but ONLY FOR THE PURPOSES OF CASE RATES, by 6.5/1.4 = FIVE TIMES, leading to FIVE TIMES OVERSTATEMENT of case rate disparity.

So we have:

• Case rate statistics suggest that there are 1.4% of unvaccinated persons in Santa Clara County

• County’s own vaccination statistics suggest that there are 6.5% of unvaccinated persons

• This five times discrepancy suggests that Santa Clara County is cheating with its statistics.

• This is in addition to NOT counting COVID illnesses of boosted people for two weeks after boosters

• Santa Clara County likely uses other methods of faking vaccine effectiveness, but I have not caught them yet.

Are you surprised by any of this? What other data-faking methods do they use? How can we catch them?