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The Viral Load Hypothesis

Viral load, advises Wikipedia, is “a numerical expression of the quantity of virus in a given volume of body fluid a higher viral load often correlates with the severity of an active viral infection.”

So, the viral load hypothesis is that the more of a virus you’re exposed to, the sicker you’ll be. For obvious reasons I’m applying this to COVID-19, producing this hypothesis: Inhale a lot of COVID-19 and you’ll get a lot sicker than if you inhaled just a little.

So, this is a big question for us now, and should have been to the “experts” and “authorities” a few months ago. (Perhaps it was, but I didn’t notice it.)

This isn’t necessarily true for COVID-19, of course. As Wikipedia notes, this is “often” true, not “always” true. We have to test to really know.

Whether or not this is true for COVID-19, however, is a very serious question, with very serious implications. For that reason I’d like to give it some brief but serious attention.

So…

The best method of testing this hypothesis would involve a double-blind study that purposely exposed a large number of people; some at low levels of exposure to the virus, some middle, some high, and some none. We won’t do that for obvious reasons, and so we’ll have to look for statistical indications. Some of the things we’d look for are these:

  • Of all COVID-19 cases, what percentage had long-term exposure? Doctors and nurses treating COVID patients would fit this model, as would people stuck in the same house as someone sick with COVID-19.

  • Of all COVID-19 cases, what percentage had very brief exposure? These people would be those who have the antibodies (showing that they were exposed), but had very brief exposure, such as a one-time pass close to someone who was coughing.

  • Of all COVID-19 cases, what percentage had a middling exposure, such as sharing a large office with one other person who was sick, over a fairly brief period.

To support this properly, we’d have to do a lot of random antibody testing (some was done in New York recently), so we know how many people were exposed but had minimal symptoms. Without them, all our percentages would be skewed, and perhaps wildly.

With enough of this information, the statistical experts could sort through everything, also breaking things down according to other risk factors. But within a few days (a week or two at most) we would have a clear picture.

Indications And Implications

I have my personal suspicions on this, of course, but that’s neither here nor there as regards statistics. What matters are actual tests (assuming they’re reliable tests), good information, and enough of both. After that it’s only math… that and the courage to face the results.

My guess is that viral load matters a great deal in this case, and that the 1919 model of bringing the sick out of closed spaces and into the out-of-doors was a better model. And, I might add, the human instinct seems to be to flee concentrations of people in time of plague, seeking open space. Might that be in recognition of viral load? There’s no easy way to tell, and instinct isn’t infallible, but it should at least be entertained.

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Paul Rosenberg

freemansperspective.com

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