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To: Do the math
The strange thing demographically about the Spanish flu is that it killed a lot of middle aged men.

No. Not according to the mortality rates not only here in the US but worldwide during the Spanish Flu pandemic. The age group most likely to die from the Spanish Flu was in the 20 to 40 age range which is the exact opposite of most influenza outbreaks where the mortality is typically in the very young and the very old or in people with already compromised immune systems. One theory was that this novel strain caused a cytokine storm immune response. In other words, if you were relatively young and healthy adult and having a robust immune system that would normally protect you against something like influenza, this particular strain caused those people with healthy immune systems to go into a type of immune over hyper drive – their own immune systems in trying to fight off the virus, actually ended up killing the host.

Why Did the 1918 Virus Kill So Many Healthy Young Adults?

The curve of influenza deaths by age at death has historically, for at least 150 years, been U-shaped (Figure 2), exhibiting mortality peaks in the very young and the very old, with a comparatively low frequency of deaths at all ages in between. In contrast, age-specific death rates in the 1918 pandemic exhibited a distinct pattern that has not been documented before or since: a "W-shaped" curve, similar to the familiar U-shaped curve but with the addition of a third (middle) distinct peak of deaths in young adults ≈20–40 years of age. Influenza and pneumonia death rates for those 15–34 years of age in 1918–1919, for example, were >20 times higher than in previous years (35). Overall, nearly half of the influenza-related deaths in the 1918 pandemic were in young adults 20–40 years of age, a phenomenon unique to that pandemic year. The 1918 pandemic is also unique among influenza pandemics in that absolute risk of influenza death was higher in those <65 years of age than in those >65; persons <65 years of age accounted for >99% of all excess influenza-related deaths in 1918–1919. In comparison, the <65-year age group accounted for 36% of all excess influenza-related deaths in the 1957 H2N2 pandemic and 48% in the 1968 H3N2 pandemic (33).

https://wwwnc.cdc.gov/eid/article/12/1/05-0979_article

Again, I would point out that it seems you were limiting your mathematical modeling on a subset of the population – men serving in the army at the time. So among those men, what were their aveveage ages compared to the rest of the population who also died from that influenza pandemic? Did this subset have a higher rate of smoking vs. the general population? What about women, while I understand the infection and death rate of women was lower, women did die from it, what was there rate of smoking? Any of those data points could skew your results.

I recall reading a news article some years ago about a supposed statistical correlation of the use of underarm deodorant and breast cancer because of the number of women who had trace (very minute) amounts of alum in their breast tissues as found in biopsies.

But what that article left out was how many women who use underarm deodorant never get breast cancer (and of course they are not likely to get biopsies) or how many women who have never used underarm deodorant do. Then again in that same sampling how many of these same women diagnosed with breast cancer also, drove a car, owned a cell phone, has ever eaten at Chick Fil A, watched American Idol. Does ever having watched American Idol, driving a car, eating Chick Fil A or using a cell phone correlate to an increased risk of breast cancer? No of course it doesn’t

Mathematical modeling and statistic is something I am very interested in and that I’ve done in my job, creating graphs and charts and dash boards on statistical HR and Financial data. But I’ve also seen how statistics can be all to easily manipulated to result in a desired outcome.

Again, correlation does not necessarily equate to causation.

How Ice Cream Kills! Correlation vs. Causation

The danger of mixing up causality and correlation: Ionica Smeets at TEDxDelft

Junk Science Episode 10: Correlation / Causation

119 posted on 12/06/2017 4:19:53 AM PST by MD Expat in PA
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To: MD Expat in PA
Unfortunately the only reliable data was the Army data. I'm not claiming causation. My job was to model the data I was given. Traditional models failed miserably. We had to guess at what rates women and men smoked. My work was not a statistical model, but a deterministic model. The data did include deaths outside of the military, but with little forensic information. The civilian data was not reliable. I was not limited to just the army data, but I don't have much faith in the civilian data. My model was a modified SEIRD model, no statistics in that model. This is also not an age structured model. Some one else was working on that problem, and he needed my model to work to solve some of his problems. By adding smoking to my model I was able to get the model to work. Of course a model is only as good as the data available. I did not manipulate any data, I just used what I was given.

The life expectancy in 1918 was estimated too be about 53, so when I said middle age men I meant men in the about 20-40 range. Which is approximately the age range for the army men who died.

I have a new SE1E2,...,EnIRD model that works great for measles, but it sucks for Scarlet fever. So I'm missing something , there is a hidden factor or factors that I need in my model. If someone has some data that hints at a factor, then I will add it to my model. If it helps fix my model great. This new variable or equation does not imply correlation or causation sense I'm not making any statistical inference. Again I could be wrong, that adding this factor helps in only a cosmetic way. When I get better data I improve my model. I have an idea how to fix the model, but the data available is not consistent. So I'll wag it until I get better data.

120 posted on 12/06/2017 7:25:44 PM PST by Do the math (weo)
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To: MD Expat in PA
Did you receive training or take related courses at any university to qualify your interest in mathematical modeling and statistics or did your employer send an x number of HR/Financial employees to an out of town seminar to familiarize the staff with the latest required government software and the videos you watched included those terms more than three times?

Your ability to twist information that goes against the lies of the criminal controllers of the medical mafia is at the least an indicator that, whatever station in life you achieved through your interest in mathematical modeling and statistics, your lazy thinking got you there.

Do you also support the hockey sticks model of global warming?

Expand your knowledge and look up this chlamydial organism, C trachomatis and try to keep it in the context of what I wrote … there is a long list of pneumonia causes including STD's, specifically chlamydia. There have been documented reports of immunocompromised adults and laboratory workers catching pneumonia due to C trachomatis.

As for your open borders attitude of accepting the unlikely scenario of the dead Mexican woman being in Arizona legally, did you ever hear of Occam’s Razor?

In fact, your willingness to conjoin honorable and legally entered immigrants into this great United States with the low life criminal invaders sounds suspiciously like the Pennsylvania criminal democrats Kenny and Rendell whose accepted and approved level of eligibility for the third world’s entry into our Republic is the villager’s ability to embrace mayhem while Democrat and RINO remnants of the Obama machine work to convince the mush for brains Americans it’s ok because in the third world mayhem is a family value.

I don’t mind people arguing with me but those who are ignorant and argue from a high horse are decidedly annoying. Those who suggest some kind of adherence to scientific integrity in their learned ‘knowledge’ and are unable to recognize inconsistencies and outright lies are not just fools they are enemies of freedom and a free People.

2009, if that year is correct, marks the pharma industry's push to inoculate and inoculation does provide a pathway of many laboratory life forms capable of squeezing through the opening at the end of that needle.

No. No. And No. You are not going to get pneumonia from getting a flu or any other type of shot, and not from “laboratory life forms” whatever that is supposed to mean.

An intelligent person, one allegedly familiar with research and interested in the subject beyond recognizing it as an opportunity to read an edict from a high horse might ask for a clarification.

2009

Have no idea where yer eminence got the ‘You are not going to get pneumonia from getting a flu or any other type of shot' idea from.

Where did I say a person would?

Don’t tell me an intrepid researcher such as your self is not familiar with the existence of engineered organisms and the nanotechnology that makes it possible to deliver a surprise package to anyone without their knowledge?

You find that difficult to accept? You don’t believe our leaders would do that to US?

Well then you must not think that those who attack President Trump are in fact making war on the people of the United States.

You must not think the open borders, encouraged by presidents republican and democrat alike was an act of war on the people of the United States.

You must not think the Obama administration’s effort to fly in jihad on a daily basis was an act of war on the people of the United States.

What makes you so secure in your government you can compartmentalize evil in an environment described as a swamp?

https://nursesagainstmandatoryvaccines.wordpress.com/2014/11/17/employee-vaccination-rates-are-tied-to-government-funding-and-reimbursement/

121 posted on 12/06/2017 9:11:19 PM PST by MurrietaMadman (Drain swamp drain.)
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