Model Exploitation for Political Purposes

 In Science, Virus

For about 20 years I was a scientist in the field of underwater acoustics, developing new sonar systems for finding Soviet submarines.

During the course of that, I participated in several sea trials involving numerous ships, submarines, aircraft and so on, to help measure and understand how sound traveled underwater, what it did when it encountered an object like a seamount or, of course, a submarine.

In addition to all that, we used and built models to help plan the exercises, and to extrapolate from our data to more general situations.

Models are science, too, but only if they take into account the real world, and that is what we did.

In short, how sound travels from a source in the water to another point is a function of depth, salinity, temperature and frequency of the sound.  Other factors may come into play but those are the big ones.

Sound doesn’t just travel.

Nor does the freaking Wuhan Virus just kill whoever is exposed to it.

Models that predict infection rates clearly aren’t taking a whole lot of things into account that we have learned are fundamental.

And the most important thing we’ve learned is that a) it’s not random, and b) the death rates are also far from random.

To say that the lethality rate is 3% or .008% or any of those numbers is utterly ludicrous, with or without models.

Just as sound doesn’t just sort of go “anywhere,” this idea that the disease and its lethality are somehow homogeneously dispersed is mind-blowingly false, and deceitful on its face.

The question any scientist should ask is: what are the variables that contribute to the lethality rate? THAT is the scientific way to look at it, and only that.

And clearly what we are seeing is that the lethality rate is a function of

  • Age – the mean age of deaths due to the virus in Italy was 81, for example
  • Co-morbidity – in Los Angeles, 93% of deaths had at least one other condition involved
  • Overall viral load – tragically, those in the medical profession are swimming in it
  • Living conditions – urban density, close proximity to others are crucial factors


There may be others but those seem the big ones.

For any model to not take this into account is unscientific, and to base policy on such flawed approaches is criminally irresponsible.  And not only is it absurd to imagine everyone is equally at risk, but it robs those MOST at risk of the special attention they need.

Modeling is a terrific tool, but can clearly be exploited by agenda-driven ideologues, as we have learned regarding the “climate change” hysteria.  Climate “scientists,” when faced with an inconsistency between the models and actual data, chose to reject the data in favor of the model in a manner that is an insult to all scientists everywhere.

We are seeing the identical anti-science, agenda-driven propagandists tell similar lies for similar purposes of control in the case of the virus and we are all suffering for it.

The idea that the infection rate is x, or the mortality rate is y, presupposes a homogeneity throughout whatever geographic area one chooses, which is simplistic to the point of uselessness at best.  Even assuming one knows the denominator (number tested, number infected) or numerator (number positive, or number dead) it’s ridiculous to ignore the fundamental variables of which these rates are a function.  To attach a lethality value, even as a per-million metric, to the United States as a whole, or to New York State, or Los Angeles, is beyond preposterous, and yet, those are the data we’re given and they’re insulting to our intelligence and are only a means to continue government’s control over us under the guise of “Public Health.” Despicable, deceitful, anti-scientific, and stupid.

For every death, we should have those 4 characteristics: age, other diseases, job if applicable, nature of residence.  Then we can really use science to best address those most at risk instead of wasting precious resources and destroying the economy with the sweeping generalizations arising from the assumption we’re all equally at risk.

We’re not.

And decision-makers need to stop treating us as if we were, and start focusing on high risk groups and let the rest of us get back to work.



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