Newsgroups: alt.ufo.reports
From: kymhorsell@gmail.com
Subject: the mystery of havana syndrome

[uploaded 56 times; last 31/10/2024]

Don't we all love a good mystery? Especially one that has been around
for decades and lots of people have looked into but don't seem to find
much about it makes sense.

I noticed in the past week the "final" intel report on Havana Syndrome
was finally released. Not to mere mortals but to someone, somewhere.
The executive summary seems to indicate the problem is not likely the
result of foreign interference with US diplomatic staff.

--- sidebar ---
  US intelligence community finds no foreign adversary linked to
  'Havana syndrome'
  Fox News, 03 Mar 2023
  Fox News chief national security correspondent Jennifer Griffin
  speaks with 'Adam,' a former government worker who is reportedly
  Havana syndrome's 'patient...

  Cuba blasts U.S. for years of disregarding evidence on 'Havana Syndrome'
  CTV News, 04 Mar 2023 03:29Z
  Cuba on Thursday blasted the United States for taking too long to
  accept evidence that the ailment "Havana Syndrome" was not likely
  caused by a foreign...

  We should keep looking into the 'Havana syndrome' mystery
  The Washington Post/OpEd, 04 Mar 2023 01:29Z
  I was surprised that the March 2 front-page article "Report: 'Havana
  syndrome' is not an attack" reported that seven intelligence
  agencies found it "very...

  U.S. to Continue Paying Benefits to Havana Syndrome Sufferers
  The Wall Street Journal, 03 Mar 2023 18:31Z
  Washington. The U.S. government will continue paying benefits to
  victims of the unexplained illness known as Havana Syndrome,
  following an intelligence...
------

The syndrome first came to light around 2016 when some diplomatic
staff posted to the US's then-recently-revived embassy in Havana
complained of a bunch of strange symptoms including nausea, hearing
unusual noises, various aches and pains, and some other perceptual
problems. It was filed on the back-burner at first but then the case
numbers started to mount. Could it be some kind of mass hysteria among
the US embassy staff in Cuba? Then cases started cropping up in
Germany and elsewhere. Then Canadian diplomats and staff also started
reporting cases. Up till now around 1000 cases have been reported.
I know, because the just-released report at least admits to looking
into around 960 of them.

But the actual data are very hush-hush. Even newspapers obtaining
reports from medical authorities that looked into the problem were
stymied for hard facts for a long time. The US National Archives
eventually stumbled on one report, but scanning through it very little
in the way of usable info is apparent. About the only thing a numbers
droid like me noticed was a spread of cases by how long after being
assigned to an embassy a member of staff complained they felt ill.

While there can be problems inferring anything from the date a case
came to the attention of medical authorities even that data was not
readily available. The medical studies that are available now in any
case seem to look at a tiny fraction of the 1000+ that seem to have
been reported. Even the Canadian report in cases among Canadian
diplomats is not much firmer on too many details, apart from that
something may have happened.

So of course you have to wonder why all the secrecy? Everyone has
accused everyone else in causing the illness somehow. At first it was
said to be sonic weapons. Then it may have been EM weapons -- either
microwave or something similar. It also may have been mass hysteria.

After reading about the Pentagon's AI program that is "aware of the
past", "aware of the present", "can point telescopes in interesting
directions", and has a subroutine "to spot UFOs" -- what does that
thing think is the cause? Or at least the correlates? Did anyone ask
or are we relying on opinions of human analysts again?

So. OK. I have a little AI or 2 of my own. They are starting to get
good at their various jobs. So why don't we throw the data over to
them and ask what they think?

First problem. What data? There is very very very little data to go
on.  So the first task we have is getting an AI program to FIND some
data.  Or make it up.

Now this is not as hokey as it might sound. Data out there in the world
interact with each other. In fact it's almost totally impossible to
prevent one kind of data affecting another kind of data if they are in
around the same place around the same time. It's a generalised law of
forensic science -- if a criminal goes to a crime scene they will
leave evidence behind no matter how hard they try not to, and the crime
scene will leave evidence on them no matter how hard they try to wash
it off. Whether or not there is ENOUGH of this "brushed off" evidence
to trigger a light on the relevant lab instrument is another matter.

So we have a hope of "making up" some Havana Syndrome data that kind
of mimics the real data which we haven't got. And an excellent place to
start "making stuff up" is Wikipedia. The Wiki page on Havana
Syndrome has a lot of dates. We might assume the dates it mentions
about when cases were reported in newspapers and dates when
investigations were done and dates when reports were written or
published in journals -- all these dates mean something. We might
assume if there is a date that is mentioned more often than all the
others it might be "more significant" than all the other dates.  So if
we tabulate up all the dates in the Wiki entry we might have a very
very rough outline of how cases were reported over time.

And given cases being reported are probably some number of months or
maybe years after the relevant patient noticed red spots on their
chest (or whatever) we can just try to back-date all the dates to get
a more exact idea of what the case numbers over time look like.

So what we are going to do is take this made up data and see what
might be the reason it looks like it does. As in other cases we'll use
very robust data science type methods. We'll put (say) 2/3 of the data
to one side. We'll take 1/3 of it and see what can explain that very
very closely. Then we will take that explanation to see how well it
predicts the occurrence of the other 2/3 of "made up cases". If we get
a beyond chance match then we might be onto something! :)

So first up. Here are the "made up dates" the AI culls from the Wiki
page:

2015.54 1
2016.62 1
2017.21 5
2017.29 2
2017.62 7
2017.71 7
2017.79 12
2017.88 5
2017.96 7
2018.04 9
2018.12 2
2018.21 13
2018.29 11
2018.38 3
2018.46 14
2018.54 1
2018.62 4
2018.71 5
2018.88 10
2018.96 6
2019.04 5
2019.12 7
2019.21 11
2019.29 1
2019.38 4
2019.46 1
2019.54 5
2019.62 1
2019.71 5
2019.88 2
2019.96 6
2020.04 1
2020.21 1
2020.29 1
2020.38 1
2020.46 2
2020.54 3
2020.62 2
2020.71 1
2020.79 24
2020.88 1
2020.96 15
2021.04 2
2021.12 4
2021.21 5
2021.29 4
2021.38 12
2021.46 2
2021.54 12
2021.62 5
2021.71 16
2021.79 18
2021.88 8
2021.96 9
2022.04 9
2022.12 11
2022.21 3
2022.29 7
2022.38 5
2022.46 2
2022.54 5
2022.71 2
2022.79 2
2022.88 1
2022.96 9
2023.21 1

This is a month-by-month count of how many times each date in that
month was mentioned on the wiki page in connection with the
problem. The date could be in the body of the text or in the footnotes
or even in the list of publications.  How many times it gets mentions
we assume is related to its importance and maybe the number of cases
that had some to light in that month. Roughly.

So now we throw this to the AI program that finds the best matches
from a very large database of weather and climate data, planetary and
satellite positions, economic data and military spending by various
countries, numbers of different newspaper articles published from
time-to-time that contain certain keywords. And a lot of other junk.

So what do you think it came up with as the best (say) 8 data series
that when joined together explain most of the "counts" in the data
series above? No surprise. The position of a lot of different
asteroids and a planet or 2.

And, remember, we did the match on 1/3 of the list, above, *THEN* we
checked how well the other 2/3 matched up with the patterns we found
in the first 1/3. The 1/3's in this case were assigned
randomly. Sometimes I use the first 1/3 only or the last 1/3 only, but
this time I just specified a random 1/3 drawn from inside the limits
of the table.

So the "best explanation" was found to be:

Variable	lag
		(m)
DEC378090	1
DEC90004264	1
DEC567827	0
DEC50347602	0
saturn-Dec	1
DEC50224365	0
DEC90000544	0
DEC1993SC	0

Not unreasonably the program found a bunch of similar types of things
recorded the best match-up against the 2/3 "not seen" data.  The model
it estimated from the 1/3 "seen" data predicted the number of "pretend
cases" (the above list) within +-1.2 75% of the time.  When plotted
out it looks pretty impressive. The plot shows the counts going up and
down a couple of times between ~0 and 5 or 6 in some months. The curve
of the model is always close by and most of the time within +-1 of the
pretend case number we gave it.

So when the CIA (or whomever) say Havana Syndrome has likely "nothing
to do with" China or Russia or any other known country then maybe they
are right. The fact the US govt intends to keep paying the 1000
affected staff some kind of support payments also seems to fit with
the idea that *something* happened. Some of the medical reports say
some kind of common brain injury was found in at least some patients.
Of course *other* reports say that report is all wrong and it's just hysteria.

But it's kinda funny a bunch of dates from a wiki page talking about
the Syndrome seem to line up pretty exactly with a bunch of data that
also seems to line up with another bunch of mystery things that have
been going on at least the past 10-20-30 maybe more years.

Another mystery! And we all love a good mystery, right?

--
Section 8. Unidentified Flying Object (UFO) Reports
Persons wanting to report UFO/unexplained phenomena activity
should contact a ... data collection center, such as the National UFO
Reporting Center, etc.
-- www.faa.gov, as at 30 Nov 2022

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