My research on the Spanish flu was a modified SEIRD model, my job was to estimate the parameters in the equations. The S represents anyone who can get sick. The math did not work. When I changed my model to include smoking as a factor, I was able to get a model that agreed with the empirical data that I got from the CDC. Of course I could be wrong.
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