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population, the curve is identical to that for COVID-19.

Figure 1
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Epidemic curve of pandemic influenza in Prussia, Germany. from 1918~19. Reported daily

“Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1LOL8—10" -
Theoretical Biolagy and fdedical Modelling volume 4, Articie number: 20 (2007)

At last Tuesday’s meeting, Tom Lopez asserted that because we were seeing fewer infections
and “cases”, the mask ordinance must be working. This is a classic statistical blunder that people
make — correlation does not equal causation (this is known as the Post Hoc fallacy — past hoc,
ergo propter hoc). As is abundantly clear from the above, it is easy to illustrate that mask
wearing, and mask ordinances were not responsible for the absolutely expected decline in

COVID-19 cases and deaths.

Modeling Excess Deaths

It is important to model excess deaths, because the measure of cases or infections depends on
how much testing has been done, and the quality of the test. Typically tests for infections are
“noisy” — in other words, there are a large number of false positives. As a result, death is the best
measurement to use. This is relevant with respect to the impact of mask wearing. The chart
below ~ representing all-cause deaths from EuroMomo countries (so around 360 million citizens,
comparable in size to the USA) — shows two critical notions. First, note that you can see a low
trough of deaths in both the 45-64 and 65+ age groups preceding the onset of COVID-19. What
this means is that there is effectively a “backlog” of deaths waiting to occur — people who would,
in normal years, have passed away. Therefore, the second thing that this chart illustrates is that
when COVID-19 arrived, all of those deaths were essentially front-loading the system, While
this sounds callous, it is a simple reality that no amount of hand-wringing will change.

While 180,000 excess deaths seems like a lot, it’s really only 0.05% of the population, and the
difference between 2018 and 2020 excess deaths is 40,000 or 0.01% of the population. In other
words, there is very little real difference between the excess deaths of the 2018 winter season vs
the 2020 winter season. What has occurred ts that those who would previously have died from
influenza have instead died from COVID-19. The same holds true in the USA.

EUROPE ALL-CAUSE DEATHS

(EuroMomo Countries) 2018 Winter Season 2020 Winter Season
~360 Million Population Excess Deaths Excess Deaths
TOTAL = “140,000 TOTAL 7th Sept ™120,000
45-64 years 2018 Excess Deaths / 2020 Excess Deaths
(integrated from curve} {integrated from curve}
15-64 y.o’s 15-64 y.0.'s

~20,000

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ATE yihy bOSY DEAT RAT. POLCE IAG COWL

65+ years 2020 Excess Deaths

2018 Excess Deaths fintegrated fram curve)
(integrated from curve} 65+ yo. |
85+ ¥.0,3 ~167,000 “*
~120,000

NOE VERY LOM BLAH RATE PEESESING COVO

This is another view that explains the expected decline in the number of deaths and cases from
COVID-19. Again, a reason that is quite independent from the notions of social distancing and
mask wearing by the general public.

A Note Regarding Incorrect Statistical Models

Much has been made about how models have been used to predict a catastrophic surge in deaths
from COVID-19. In particular, many have used the Imperial College model to justify why they
needed to lock down cities, states or countries. Let’s compare the models to the reality. We can
focus on Sweden here, because once again they provide a great closed loop system pertaining to
how a country that did not follow the herd and did not lock down completely debunks these
models.

The chart below shows the Imperial College predictions for Sweden under two scenarios — the
first scenario (the orange line in the chart) shows the number of predicted deaths if Sweden did
nothing, the gray line shows the number of predicted deaths if Sweden implemented “moderate”
no-lockdown measures (which is actually similar to what Sweden did, in fact, do), while the blue
line is the actual observed COVID-19 positive deaths. As should be clear to anybody, the reality
does not come even close to the predictions. It is worth mentioning that Sweden had no

lockdown, had no mask mandate, did not close restaurants, did not close hair salons, did not
impose absurd conditions on hairdressers for wearing laboratory-level protective clothing, and
did not come within an order of magnitude of the number of predicted deaths. And yet we
continue to use these inherently flawed and grossly inaccurate models, and continue to perpetrate
draconian measures regarding personal space, PPE requirements, and mask ordinances when it is
quite clear that these measures are not, in fact, effective.

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Coronavirus Deaths in Sweden -
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httos://swprs.ore/covid19-lethality-how-not-to-do-it/

A Historical Perspective On The Actual Severity Of COVID-19

Taking a look at the monthly death rates — deaths per million adjusted for population size - for
Sweden from 1851, there are three things that stand out. The first obvious trend is the slow, but
steady decline in overall population death rate as the years go by. Next, in 1918 there is clearly a
huge event. This is the Spanish Flu —a once in a century event. When we look on the extreme

right of the chart, however, we see the COVID-19 spike. It is singularly notable for the fact that
it barely registers above the noise level.

SWEDEN
Monthly deaths per million (165 /01-2020/05}

Spanish Flu Spike

deaths per million

| Corona Spike
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Influence of Prior “Soft” Influenza season

SEEDE A Ranrralty © aah Travite Avavare
SWIFDEN Bortabty io Week Trading Avarer
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This is about the time in this document where you’re probably wondering “gee, Sweden had a
lot of deaths. This document keeps using Sweden as an example, but the media has criticized
them”. Well, let’s take a closer look at things.

The chart above shows the trailing average of mortality for Great Britain, Spain, Netherlands
and Sweden. When we look at all of them for the season prior to the current season —in other
words, the right-most part of each chart, we can see that all four of these countries had a very
“soft” prior season — in other words, fewer deaths than expected occurred. What is also
common amongst these countries is that when SARS-CoV-2 arrived in situ, there was a marked
spike in deaths. This is essentially another view of the concept of “dry tinder’ — because of the
prior “softer” season, those countries had “built up” a group of citizens — old, frail citizens —
who were spared from death by influenza, and instead died from COVID-19, causing a large
initial spike in mortality,

Next, we need to dissect the notion that Sweden had more deaths than other Nordic countries.
The following chart illustrates this. Neither Finland nor Norway had a large “trough” with
respect to deaths in the prior season, and as can be seen, neither of them had a massive spike
of deaths in the following season. Hungary is included for interest; they had the opposite occur,
where they saw excess mortality the prior season, and in fact then moved into a period of a
trough even though SARS-CoV-2 was present in the country.

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This clearly shows that there is a confounding factor, one of many, upon death statistics due to
COVID-19. While not directly pertinent to the mask debacle we are discussing, it is nonetheless
interesting. There are a number of other factors that easily explain the difference in deaths

between Sweden and other Nordic countries, but | will leave that as a research exercise for
readers.

On The Topic of Restaurants And Bars

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og Just asking: there are 47,600 pubs in the UK {let's say
1000 week through the door} that's nearly 300M people
in 6 weeks - How many outbreaks in that time and

AO :

oO what's the risk?

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Maris. 20202 AnriO Aorao May? fun’ Jun

Nashua has been one of the cities that has wanted to implement further restrictions on the
dining experience, and are deeply concerned about opening bars due to the “potential to
spread coronavirus”. In the United Kingdom, bars reopened at the end of June. There have
been no mask requirements in those bars, and only token social distancing requirements. There
are almost 48,000 pubs in the UK, and it is reasonable to assume a minimum of 1,000
patrons/week in those pubs. In a six-week period, that means that there would be nearly 300
million people passing through a pub, not wearing a mask, and not social distancing. They have
now been open for almost two months, and during that period there have been precisely zero
outbreaks in that time, once again proving the fallacy that a mask is required to “prevent the
spread of coronavirus”, and that in fact what you are simply seeing is the “baked in” GOMPERTZ
chart that | have previously explained.

Demonstrating The Lack Of Efficacy Of Mask Ordinances

Starting with our own CDC references, Xiao J, Shiu E, Gao H, et al. Nonpharmaceutical
Measures for Pandemic Influenza in Nonhealthcare Settings—Personal Protective and
Environmental Measures. Emerging Infectious Diseases. 2020;26(5):967-975.
doi:10.3201/eid2605.190994, we can see the following:

There were 3 influenza pandemics in the 20th century, and there has been I so far in the
21st century. Local, national, and international health authorities regularly update their
Plans for mitigating the next influenza pandemic in light of the latest available evidence
on the effectiveness of various control measures in reducing transmission. Here, we
review the evidence base on the effectiveness of nonpharmaceutical personal protective
measures and environmental hygiene measures in nonhealthcare settings and discuss
their potential inclusion in pandemic plans. Although mechanistic studies support the
potential effect of hand hygiene or face masks, evidence from 14 randomized
controlled trials of these measures did not support a substantial effect on transmission
of laboratory-confirmed influenza. We similarly found limited evidence on the
effectiveness of improved hygiene and environmental cleaning.

Not even the CDC actually supports — from a scientific perspective — the notion that face masks
have a significant effect on the transmission of a virus.

However, let’s look at the actual in-the-field evidence, starting with Austria and Poland

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death
f Estimated Using Cases - Case: dnvidedi by cases S days ado
. Apa! Lith Everyone requiced POLAND
| towsar late manky
: “7 April 6th Everyone required AUSTRIA
to wear face masks

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The above two charts are just two exemplars of the same trend throughout Europe. First, we see
that Austria enacted a mask ordinance on April 9. There is absolutely no effect on their case
curve; similarly for Poland after it enacted a mask law on April 10. In fact, it can be argued that
Poland had an increase in cases subsequent to the enactment of their mask ordinance. Germany
had a similar curve. Once again, these charts — all data obtained from European CDC sources,
publicly available and verifiable — illustrate the fact that wearing a mask did not materially
impact the curve.

Seasonality

https://emedicine. medscape.com/article/227820-overview

The above chart illustrates seasonal variations in frequency of selected upper respiratory tract
infection pathogens. PIV = parainfluenza virus; RSV = respiratory syncytial virus; MPV =
metapneumovirus; Group A Strept = group A streptococcus. Note that both influenza and
Coronavirus are seasonal viruses, with the incidence highest in the winter and spring. Once
again, this illustrates why there has been a decline in incidence of COVID-19 in the Northeast in
general, and further illustrates how a naive interpretation of “oh, we started wearing masks
and things got better” is wildly inaccurate.

Next, let’s examine the cases in Italy, Brazil and Peru with a slant towards seasonal variation.

BRAZIL

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When you examine the case of Italy, we see that it has a sharp increase in March, and then an
equally precipitous drop in April and May, very much in line with the seasonal variation in
frequency chart shown previously. However, something “interesting” seems to be happening
with Brazil and Peru. As an aside, note that Brazil did not implement a lockdown, while Peru did
~and as you can see, Brazil had a lower death count than Peru did.

What we are seeing is that Italy is exhibiting the Northern Temperate Type Pattern, while Brazil
and Peru are exhibiting the Northern Tropical Type Pattern with respect to seasonal variability
as described by Dr. R. Edgar Hope-Simpson, F.R.C.G.P. at Cirencester Epidemiological Research
Unit, UK. For those who have intellectual curiosity, you can read about The Transmission Of
Epidemic influenza where these concepts are explained.

Now, let’s turn our attention to the USA and the much-hyped “second coming” of COVID-19.

Seasonality/Regionality - Europe Vs USA?

Daily new confirmed CON 1D-10 dealhs per milion peaple

2 Laue i Gem means That The mummar of confines

SOUTHERN USA

4 GaM dar 24 fg 1G Am 3% May 120 dun & dar 29 Jud? auc & Sep 8. 7096

We can see that there is a marked similarity, up until the end of June, between the USA and
Europe when we do not control for different seasonal patterns due to regionality. However,
when we control for regionality, you can see the yellow line representing the Northeast USA,
and therefore easily infer what influence the Southern USA has upon things. What we are seeing
here, is a classic “double hump” due to seasonality and regionality.

New Deaths from COVID-19 per Day by US States/Territories, normalized by population

g
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few Daily COVID-I9 Deathafim pecpie

“ Deaths per Million on Deaths per Million
“ USA NORTH-EAST * USA SOUTH
By (note: similar to USA West) ee {nate similar 1o USA West}

:
New Dally COVIO-13 Oraths/im poopie

Therefore, when we isolate the new deaths from COVID-19 per day and regionalize it, we in fact
see exactly what is predicted — a Northern Temperate Type Pattern for the Northeast USA, anda
Northern Tropical Type Pattern for the Southern states.

You may be asking why this is relevant. It is relevant for two reasons. Firstly, it once again
reinforces the notion that it is absolutely not simply “we wore masks, things got better, therefore
masks made them better” — that is the post hoc, ergo propter hoc fallacy that is endemic at the
moment ({1.¢., it occurred after ¢his, therefore ¢his caused it), but also that it illustrates that while
there may well be a “second coming”, the reason for that is quite simply seasonality and
regionality, and not some mysterious unknown that requires us to wear a totem ~ in this case a
mask — to ward off evil spirits. We are no longer living in the 17" century; we have science, we
have the ability to analyze data on an unprecedented scale, and as elected officials you all have a
responsibility to educate yourselves and not make decisions based upon fear, uncertainty, doubt
and the certain lack of knowledge.

“Cases” vs “Positive Test Result”

Historically — and by historically, I mean right up until a few months ago when the media went
completely bananas — a “case” meant a symptomatic, hospitalized case. It did not mean
“somebody tested positive”. Furthermore, historically PCR tests used a Ct amplification
threshold of less than 30. For COVID-19, they are instead using a Ct threshold of less than 40.
This has the effect that trace amounts of no-longer-active SARS-CoV-2 virus are detected,
effectively amplifying by a significant percentage the number of positive tests, giving rise to the
phenomenon of false positives that we are seeing.