covid and ufos

[MrPosti...@kymhorsell.com]

EXECUTIVE SUMMARY:
- We re-visit the pandemic data and link it with daily UFO sightings
across the US from 14 days prior -- the approx "incubation period" of
some versions of the virus.
- The AI s/w finds a very good predictive model for daily covid cases
for each US state. The model explains more than 90% of daily cases
seen in the period 2020 to 2022Q1, allowing for about 30% of data
falling into a period between a growing number of cases and one
where cases have seemingly been brought under control.
- The model implicates some UFO types in possibly "promoting" the virus
across various US states. But at the same time other UFO types seem
to be associated with a decline in infections in the same regions.
- UFO types not presented may be connected in one or other way,
or not connected at all.
- The "list of suspects" is very roughly consistent with personal
observations, although back-comparing model results is not a very
reliable measure of anything. Maybe you have seen planes or helicopters
or flying wings chasing meteors or flashes in the sky during the
pandemic period. That would be interesting.


We've looked at a basic regr study comparing ufo activity (i.e.
sightings reported to the NUFORC) and covid cases and deaths. But
that was 1/2-way through the pandemic. While surges in cases are
expected to continue in some parts of world into the future, the
pandemic seems to have mostly subsided with many vaccines and, more
importantly, viral treatments now rendering a family of viruses that
have killed a mn people in the US and 210k in the UK to a
relatively low level of lethality.

But the possible connection remains. Maybe the pandemic was not an
entirely natural event. While early models predicts the mark 1 virus
would produce 100 day cycles in an unvaccinated population it was not
predicted the cycle would seemingly sig correlate with the approach of
Mercury.

With now 3 years of daily data for both UFO sightings and covid cases
we can do some additional studies, aided with some AI-driven stats
s/w. The AI part of the system determines what -- in light of its past
experience with many 1000s of datasets -- is worth trying in model
building and then interpreting the results from lower-level -- usually
standard packages that have been used many places for many decades --
stats s/w to pick up problems caused by a bevy of niggles that can
sometimes produce erroneously "significant" results.

The AI part is still under development -- code for bug fixing -- and
is still learning what to learn -- so the results are maybe comparable
with an average medical researcher. But its reports do make
interesting reading.

The latest run tries to build the "simplest" model that is
statistically justified and does predict covid cases as closely as
possible. The data it's using at the cases and UFO sightings data day
by day for each US state between 2020 and 2022. It tries to assemble a
model that uses a history of different types of UFO sightings to match
the daily covid cases reported in the JHU database 14 days after the
UFO sighting. IOW it tries to link UFO activity with events related to
starting new covid infections with an incubation time of around 2 wks.

While this simple idea has many possible problems up to and including
the changing mix of viruses seen in different states and different
times, it does robustly appear to explain more than 90% of reported
covid infections across the US in the period.

The model seemingly predicts the sudden drop in daily cases in the
middle of the period and puts it down to a "gear change" in the types
of UFO activity reported. Well. The program can't do anything else.
That's the data it was given. It doesn't know anything about vaccines
being developed and deployed, or whether a state had mandatory masking
and isolation measures at various times. The unsettling conclusion
may be -- most of that did not matter all that much in the run of the
pandemic.

As we've seen before the summary results suggest the appearance of some
UFO types is associated with a sig increase in covid cases 2 wks later
and some seem to be associated with a significant decrease in cases 2
wks later. Since we're using a 2-d grid of input data this pattern is
the "average" result for every US state. Individual states might have
seen a much different pattern. But we assume that UFO activity is not
limited to a single US state. They are in the air and presumably can
move around the country at will and "do the same thing" anywhere they
might choose.

The s/w chooses to build an exponential model whereby the number of
UFO's of different types on day T is used to predict the number of
covid cases on day T+14. It culls each basic type of UFO sighting -- I
use the NUFORC-suggested "shape" of the object plus color keywords that
might be found in the comment section of each report summary to get 45
"basic" UFO types. Within this selection there are the 50 US
states. Many states report nothing for many different types. Some
states report "lots" of certain types. We put all the data into the
mix to predict what happens in each state 14 days after the relevant
reports.

The s/w then selects from these 45 basic types of report the small
subset that explain the most day-to-day covid case reporting 2 wks
after. The underlying s/w supports regression with up to 9 indep
variates. So it's a whittle 45 downto the best 9 process using a
statistical hill-climbing algorithm with some AI assist to throw away
results that don't look good.

After some mulling on my antique little cluster the s/w comes up with
the model:

REWEIGHTED LEAST SQUARES BASED ON THE LMS
*****************************************

VARIABLE COEFFICIENT STAND. ERROR T - VALUE P - VALUE
----------------------------------------------------------------------
purple -1308.83362 187.81165 -6.96886 0.00000
brown -1759.46033 639.48364 -2.75138 0.00691
pink 689.97601 57.37311 12.02612 0.00000
gold -1.43485 0.05960 -24.07362 0.00000
Cross -0.71695 0.02274 -31.53115 0.00000
Egg 0.13225 0.00725 18.24668 0.00000
Flash 0.08574 0.00716 11.97349 0.00000
Cylinder -0.04930 0.00618 -7.97347 0.00000
CONSTANT 1375.94739 589.82257 2.33282 0.02143

WEIGHTED SUM OF SQUARES = 1.49096
DEGREES OF FREEDOM = 113
SCALE ESTIMATE = 0.11487
COEFFICIENT OF DETERMINATION (R SQUARED) = 0.99131
THE F-VALUE = 1611.597 (WITH 8 AND 113 DF) P - VALUE = 0.00000
THERE ARE 122 POINTS WITH NON-ZERO WEIGHT.
AVERAGE WEIGHT = 0.71765

(NOTE: log case counts mn state capita is the dependent variate aka Y
in the regr).


The first thing to note is the F-VALUE. It's HUGE. A value of around 1
means the model produces no statistically robust results. Given the
data has boiled down to more than 113 days when all input values were
available at the same time, 1000 means the result is way way beyond a
fluke in the data. The P-VALUE assoc with the F-VALUE is essentially
0. IOW the stats is 99.99%+ sure there is a link between these input
variables and reported covid cases.

The R SQUARED shows this model 99.1% of day-to-day cases across each
US state in the period 2020 to 2022. At least, on the 113+ days that
were left in the data after various niggling cases were left out
beforehand. (I.e. we did not go back and leave out certain cases that
made the results better).

It needs to be pointed out that this underlying stats program runs 3
different models of regr at the same time. The model, here, is the 3rd
and most sophisticated of those regressions. The other 2 use a basic
linear regression (i.e. minimize a sum-of-square-errors), then a
simple least-median-of-squares, and this final version that uses
weighting of datapoints to push down cases that appear to be
"something else" and are outliers in the present model.

The AVERAGE WEIGHT says 30% of cases (days across all states) that
remained in the dataset at this point were "outliers". This is a
little bit higher than I would normally allow -- I prefer a model to
cover 90% or more of the cases I feed into the program -- but the AI
s/w and the underlying stats program accept 72% as a valid
representation of the original data.

The SCALE ESTIMATE shows that the median error in the model's estimate
if covid cases is. Since we have fed in the LOG of the number of cases
this is essentially a% of error -- in this case 11%. SO the
model can predict the number of covid cases in any state on 70% of
days of the pandemic period and 1/2 the time be better than +-10%
away. This may start to sound like the model is really not much
good. But we will get to a plot of the results below.

Finally, we can look at the coefficients of the model. These show for
changes in the NUFORC sighting reports on day T what change is linked
with the covid cases that would be reported 14 days after the relevant
sightings. E.g. sightings of "purple UFOs" seem to predict SHARP
REDUCTIONS in covid cases in 2 wks time. Since we are using LOGS of
the number of cases the -1000 number means essentially cases numbers in
14 days will be near 0.

On the other hand the appearance of "pink" UFOS seems to be associated
with SHARP increases in covid cases after an incubation period. For
each pink UFO reported in any state, covid cases 14 days later tend to
rise by huge factor.

And we see that the reporting of certain other types over the average
US state sees a linked moderate increase or decrease in covid cases
in the state 14 days later.

The model selected is the "best" robust model the AI s/w can find.
But searching for the "best" of anything is a fraught problem. There
may be better models that show "opposite" results. But the stats
assures us it is very very unlikely they will show "no connection".

Other UFO types may also be associated with an increase of decrease in
covid cases later but the evidence for them is too weak or too small
(not the same things) in the data presented.

It would be rational e.g. if you see a pink, egg-shaped or flash in
the sky to maybe put on a mask if your vaccinations are not totally
up-to-date. And just for safety sake. Maybe any other type of
unrecognizable object you see in the sky as well. (The "flash" result
is interesting in the light of personal observations of small aircraft
appearing to "patrol" the nighttime skies during the pandemic that
seemed to significantly react to the appearance of a meteor in the
sky; and another case where a blacked-out helicopter and another
"dark unconventional aircraft" seemed to high-tail it across my rural
property toward a part of the sky that just 1-2 minutes before saw 2 bright
meteors streak down to the horizon).

And, finally, a little plot of the results. We compare the predictions
of the model against actual state by state, day by day covid reports
from the JHU database.

<kym.massbus.org/covidmodel2022.gif>.

The plot is "time" across the X axis. For each day all the states
reporting covid cases are listed one after another in alphabetical
order by 2-letter code. The green region is therefore a mess of points
that looks like a solid green area. But we can see the top of the
region which is the state on that day reporting the highest covid
rates per mn state capita. And, remember, the UFO reports are
taken from 14 days prior to the covid reporting day.

The fit seems pretty good. Even the sudden "gear change" in the middle
when health measures were thought to be responsible for suddenly
bringing down case numbers may be due to the activity of one of the
UFO types, above. There is a YUGE glitch around then where daily
cases go up and down over a period of several weeks. This is the
section where the regr program decided to remove ~30% of the data cases.

But we can see the build-up of the pandemic in the first year or 2 --
at least the "maximum state" -- is predicted by the model reasonably
closely. The the "maximum state" after the pandemic was broken in the
US is predicted fairly well.

It is certainly not conclusive proof, but seems to be evidence the
development of the covid pandemic, at least in the US, seems to have
followed a pattern that shadowed reported UFO activity 2 wks earlier.

--
"Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear less."
- Marie Curie

[One of our UFO reports has gone missing!]
The Strange Case of the ODNI's Missing UAP Report
The Debrief, 24 Nov 2022 15:26Z
In this Thanksgiving installment of The Intelligence Brief, we look
at the latest on a "missing" UAP report set to be delivered last month.

On Halloween, NASA and intelligence agencies looking at UFOs seem to be
gearing up to play them down
NBC News, 04 Nov 2022
On Halloween, NASA and intelligence agencies looking at UFOs, or UAPs, seem
to be gearing up to play them down.

ON THIS DAY: 1953 UFO Sighting Makes Jet and Its Crew Disappear
1077 WRKR, 23 Nov 2022
One of the most mysterious Michigan UFO encounters took place over Lake
Superior On Nov 23, 1953.

US Feds Raided a UFO Blogger's Houses
Gizmodo Australia, 18 Nov 2022
Earlier this month, agents from both the FBI and the US Air Force raided
multiple homes belonging to a man who runs a little-known blog about UFO.

Nation's Largest Center for Historical Records on UFOs to be Established in
New Mexico
The Debrief, 18 Nov 2022
The largest historical archive of records on unidentified aerial
phenomena in the nation will be unveiled in New Mexico in the coming
years, according to a group of leading UFO historians and archivists.
The National UFO Historical Records Center (NUFOHRC) will be "dedicated
to the preservation and centralization of UFO/UAP information in the
United States," according to a press release announcing the new
non-profit organization.
[Researcher and historian David Marler] expressed appreciation for the
renewed public interest in the UFO subject seen in recent years.
"What I find though is that, despite the ever-increasing amount of
people that are looking at the UFO or UAP subject, not a lot of
people--a very small percentage--are interested in the history," Marler
told The Debrief.
"Everyone wants to know what's new," Marler says, "and part of that is
due, I think, to the framework by which the intelligence community
[and] the media in recent years has framed the discussion; this `new'
discussion."
"Everyone seems to be looking at 2004 moving forward," Marler told The
Debrief, noting that "we know that this history is diverse and rich and
stretches many decades back, if not even further."

Unidentified aerial phenomena I. Observations of events
B.E. Zhilyaev, V. N. Petukhov, V. M. Reshetnyk
Main Astronomical Observatory, NAS of Ukraine,
Zabalotnoho 27, 03680, Kyiv, Ukraine
[...] We present a broad range of UAPs. We see them everywhere. We observe a
significant number of objects whose nature is not clear. Flights of single,
group and squadrons of the ships were detected, moving at speeds from 3 to
15 degrees per second. Some bright objects exhibit regular brightness
variability in the range of 10 - 20 Hz. Two-site observations of UAPs at a
base of 120 km with 2 synchronised cameras allowed the detection of
a variable object, at an altitude of 1170 km. It flashes for one hundredth
of a second at an average of 20 Hz. [...]
An object contrast makes it possible to estimate the distance using
colourimetric methods. [Objects with 0 albedo] are observed in the
troposphere at distances up to 10-12 km. We estimate their size from 3 to 12
meters and speeds up to 15 km/s. [...]
[Astronomers in Ukraine have undertaken their own independent survey
of objects they see flying over the Kyiv region at speeds around 15
km/sec. They are watching the daytime sky at the zenith and in front
of the moon. They see many objects -- some bright and some dark,
different sizes. They travel often singly but sometimes in large
groups. They report brightness is linked with speed. The spectrum
of bright objects is reportedly not reflected sunlight. Objects
have been spotted inside the atm upto ~10 km but also out to ~1000 km
above the earth, travelling up to ~1000 km/sec. They are not likely
anything sent by Russia or any other country].