The Pareto Principle or the 80/20 rule states that a small number of events are often responsible for a large numbers of consequences or outcomes.
Influenza spreads in a deterministic way, person to person .There is epidemiological evidence that points towards covid19 spreading in a more random fashion or stochastic mechanism in which randomness plays a major role in the form of super spreader events. It is believed that many, perhaps most persons infected by the Sars Cov 2 virus do not spread the disease to a large number of people but rather to one or two at the most . Others , however, if the personal and situational circumstances are right can spread the disease to hundreds of others .
The circumstances were right when "Patient 31" in Daegu South Korea was shown to be the index case in the covid19 infection of 5,000 cases in a megachurch super spreader incident; Covid19 spreader events such as the Biogen Conference in Boston and the choir spreader event in Washington are well known. Of course,person to person spread can then occurs in households or through close contacts in work settings after the big event seeding.
The role of super spreader events opens the door to "backwards" testing in which a positive case 's previous attendance at event ripe for spread could be investigated .
See the excellent article by Zeynap Tufekci in the Atlantic that discusses these concepts.
(See https://www.theatlantic.com/health/archive/2020/09/k-overlooked-variable-driving-pandemic/616548/)
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Our goal in this blog is to explain what a Pareto chart is, why it is useful to manufacturers, and how it is used in manufacturing analysis to help identify and fix problems at the factory. We will also use a specific example of a SensrTrx dashboard to show how you can use data collected from a bottling line to find bottlenecks.
https://ppcexpo.com/blog/how-to-read-pareto-chart
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