The last three years have shown the world the willingness of governments to push fear for political power. Many have told me they know they’re being lied to, they just don’t know how they’re being lied to.

Unfortunately, the lies continue: As of yesterday, President Biden extended the “COVID emergency” through January 11, 2023. With no data to support the extension (as if pandemics follow political decrees anyway), President Biden has extended the “COVID emergency” conveniently through the midterms.

President Biden and his cronies are lying. You know they’re lying, and I will show you how you’re being lied to.

I authored the United States Government’s H1N1 after-action report in 2012, which was reviewed and approved by every federal health authority, including Fauci’s den of thieves at NIH. It was to be used as the “roadmap” to guide the response for the next pandemic, which would be COVID-19. It’s safe to say I know how pandemics actually work.

Pandemics rarely last for years. Most only last months. The exceptions are reserved for diseases that spread very slowly either by how they’ve evolved or by the impact of their environment. Additionally, people often unwittingly affect the spread of a virus by how much or little they mix into their larger populations.

But in general, pandemics begin, they peak, and – unless the pathogen can find a non-human reservoir – they disappear into extinction in a path governed by Farr’s Law of Epidemics. As immutable as the law of gravity, Farr’s Law states that when measured against time, the number of an epidemic’s cases or deaths will form a bell-shaped curve. A curve, as in one curve.

Think about it: Farr’s Law says we only had one actual COVID wave. That’s a major problem for governments seeking to prolong and create future “pandemic threats” for political reasons. Once you understand Farr’s Law, you begin to see the lies. How could “flu disappear” suddenly? How can one country have seven COVID waves of a “pan”demic (Japan), but another only has four (the US)? Why did masks suddenly become mandated in the early summer of 2020? Farr’s Law also disproves the notion that a virus’ variants can create separate surges on their own – variants are easily recognized by the immune system, and any variant that causes disease would be part of the original curve.

Unfortunately for the snakes in government, pandemics move mathematically, making them predictable. With estimates of a population’s size, date of first infection, the virus’ infectious period, and the average number of people one person can infect (known as the virus’ R0), it can be predicted with fair accuracy when a pandemic will peak, when the theoretical last case will occur, and how many days the pandemic will last.

Not only that, but with accurate case numbers, death counts, or hospital occupancy rates, you can work backwards and find the virus’ infectious period and R0. This is important because for common viruses, R0 and the infectious period are unique enough as to serve as a “signature” for the type of virus moving through the population. For example:

– Coronaviruses have an R0 of between 2.4 to 3.4, and an infectious period of 14 days.

– Influenza is a very mutable virus with a much wider R0 of 0.9 to 1.9, and an infectious period that varies from 3 to 7 days. This mutability is why flu returns every year – when it returns, influenza is not just a “variant” of the strains from the year before, it’s essentially an entirely different group of species. Its mutability is also why there’s only a 40% chance the flu vaccine will work in any given year, and why the true pandemic threat comes from a novel influenza strain.

– Ebola hemorrhagic fever has an R0 estimated at 1.4 to 1.8, and a very wide infectious period varying from 2 to 21 days.

Each of those viruses will produce a curve in hospital occupancy, cases, and/or deaths that aligns to the virus’ R0 and infectious period, allowing the virus to be identified. This will be demonstrated below.

Because the scientific method demands predictions align to observations, I used Maryland’s hospital occupancy rates as my observation points against the mathematical predictions. I use these rates because unless you can convince people to flood a hospital when they’re not sick, hospital occupancy is a difficult number for the government to fake. Maryland is also between two international cities (DC and New York), allowing the state’s occupancy rates to be used as a barometer for how a virus is moving the country in general. Other states’ hospital occupancy rates can be used because roads and airlines were never shut down, and this will shift the observation points by only a matter of days or weeks.

First, let’s look at the original COVID-19 wave lined up against the mathematical process. As a coronavirus, COVID-19 has an R0 range of 2.4 to 3.4 and an infectious period of 14 days. Dates of first infection can be tricky to find, as “patient zero” is rarely also the first person sick enough to show up to the hospital. The earliest confirmed date I could find was December 19, 2019, courtesy of a New York Times article. Plug in the numbers, and this is what we get:

The predicted date of the peak hospital occupancy (herd immunity) should be about May 10, 2020, and the lowest point of the curve should be about June 30, 2020. Do these dates align to what was observed in hospital occupancy rate, as required by the scientific method?

Yes, the observed dates for the peak and end points of the COVID-19 pandemic almost exactly match what is predicted for a coronavirus moving through the population. (The other bumps in the graphic are part of the “2nd COVID wave,” which we will see actually matches the surges profile for influenza.)

So what about the other three “COVID waves?” Applying the above process, we can work backwards from the hospital occupancy rates and find both the R0 and infectious periods of the pathogens. When the numbers are crunched and compared to the observations, we can see that these waves were actually seasonal influenza… exactly as Farr’s Law demanded, and flu never “disappeared.” For good measure, I added in the Liberian Ebola epidemic of 2014-15 to further demonstrate the accuracy of this method (R0 for that epidemic was estimated at 2.0).

Here’s a summary of the images that follow:

The “2nd COVID wave” is a match for an average seasonal influenza strain with an R0 of 1.4 and an infectious period of 6 days. A second bump indicates unexposed people mixing back into the general population in the weeks following an unusually warm week in March. (There’s also a tiny peak on the larger curve occurring two weeks after Christmas and New Year’s.) If the population were 100% mixed instead of self-segregated, that second bump would have been part of the larger curve, and the surge extinction points would have aligned more closely.

“2nd COVID wave” matches the profile of a seasonal influenza strain with R0 of 1.4 and an infectious period of 6 days. Date of first infection is estimated off of the graph.

The hospital occupancy curve from the “delta wave” of 2021 matches another average seasonal influenza strain with R0 of 1.5 and an infectious period of 6 days. Note also the new inclusion of pediatric COVID cases into the data, as governments tried to panic parents into having their children injected with an experimental substance for a disease for which children were known to be not at-risk. The date of first infection was identified from online sources, but now appears to have been pushed earlier. Additionally, its emergence in the US during the warmer spring weather would have affected its spread. Even with these uncertainties in the estimates, the observed peak is within two weeks of the predicted peak, and the date of surge extinction appears to be accurate within a day.

“Delta wave” matches an average seasonal influenza strain with R0 of 1.5 and an infectious period of 6 days.

The hospital occupancy curve during omicron, the fourth and (so far) final “COVID wave,” is yet another match for seasonal influenza. Omicron has a pronounced and clearly defined peak, owing to a slightly more infectious influenza strain with R0 of 1.7 and an infectious period of 3.3 days.

Once again, there’s an inflection upwards in hospital occupancy just after Christmas and New Year’s, as the population mixed more thoroughly during the holidays. The speed of omicron was fast enough that it occurred entirely during colder weather, so there’s no second bump from self-segregated individuals who otherwise may have mixed more fully back into the general population during a period of warmer weather.

The “omicron wave” matches a slightly more infectious seasonal influenza, with R0 of 1.7 and infectious period of 3.3 days. Estimated date of first infection was identified off of the graph.

Because it kills too fast and is dangerous to study, there’s not much known about Ebola hemorrhagic fever. The infectious period usually ranges from 2 to 21 days, but has been estimated to be as high as 40 days. R0 was thought to be about 2.5, but newer estimates have lowered that down to 1.4 to 1.8; this is perhaps a result of people fleeing epidemic areas. During the Liberia epidemic of 2014 to 2015, R0 was estimated to be 2.0, and the case curve aligns to that R0 and an infectious period of about 18 days. Even though public health surveillance in Liberia is not strong, especially during a true viral apocalypse, the surge peak and extinction point can still be predicted to within two weeks of what is observed. At the end of the curve we see two additional bumps, where lowering case counts twice misled unexposed people to mix back into the epidemic population.

The data suggests that during the week of 12/22/14, lowering case counts made unexposed people return to epidemic areas. They became infected, cases jumped back up, and people fled again during the week of 2/24/15. They returned two weeks later, and the process repeated until the epidemic died out.

Of the five viral waves seen here, only the original COVID wave of early 2020 has the right combination of R0 and infectious period to truly be a coronavirus. The 2nd wave, delta, and omicron have R0s and infectious periods that place them firmly as influenza strains. None of these viruses have R0s and infectious periods capable of having them run for three years; even Ebola maxed out in 13 months. There is simply no mathematical scenario that would allow a coronavirus to run for three years.

The method I show above is accurate enough to predict the total length of a pandemic, including its dates of peak and extinction, to within days. The only true limitation of this method are the unknowns associated with how individuals are motivated to mix into the larger population. As is, it appears that warmer weather and family-oriented holidays are the main factors in how the population mixes, although there are almost certainly others.

When the government chooses next to create a pandemic panic, this tool will assist in shutting down the narrative. When (and if) sanity ever returns to our public health establishment, it also provides a useful way to predict how future epidemics will unfold, and help to determine when and which resources are needed.





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