As a result of recent events, the internet is simply full-to-bursting with words like "karma" and phrases like "everything Trump touches dies." But to be honest, it's impossible to get any kind of an accurate read on the situation.
We're told the president tested positive for COVID, as did his fluffer Hope Hicks, truth scarecrow Kellyanne Conway, administration eye candy Kayleigh McEnany, Senators Mike Lee, Thom Tillis, and Ron Johnson, RNC person Ronna McDaniel, professional hanger-on Chris Christie, three WH reporters, and a WH staffer. Also the president of Notre Dame, this significant as he introduced the guest of honor at Mitch McConnell's SCOTUS coming out party for jurist and Octomom wannabe Amy Coney Barrett.
Which sounds kinda bad, but who can say. It's possible all of these people have tested positive, or some of them have, or none of them have. Because nothing claimed by this administration can be trusted. Nothing. Trump has been nothing but a perjury firehose since the day he took office. Even Charlie Brown would have stopped kicking at this particular football by now. As far as we know, every single elected Republican is dead and the country is being run by Bill Barr in a Trump mask. Or perhaps they all spent a week lounging 'round the pool at Mar-a-Lago sipping Mai Tais from an infant's skull and having a larf. Either scenario is equally likely. This is what four years of chaos has wrought.
But a tale need not be true in order for it to be a cautionary tale. There is an epidemiology lesson hiding in this story, and that lesson is titled The Superspreader.
The mass action epidemiology models we have previously explored assume all members of the population under study are indistinguishable. In the SIR model, this means the force of infection and recovery rate has the same numerical value for everyone -- be they LabKitty or Donald Trump.
However, equal infectivity and infective periods do not necessarily translate into equal infection success. To explain, let us view those we infect as our "children," those our children infect as our "grandchildren," and so on. Individuals can vary greatly in the number of "offspring" they ultimately produce, just like the number of literal offspring a person leaves behind varies a great deal even though we all possess the same basic reproductive machinery. Selfish baby machines like Michelle Duggar or Amy Coney Barrett would rapidly bury the planet neck deep in humans, whereas a world populated solely by barren antisocial wolverines publishing Javascript pandemic simulators on the Internet would quickly become a lonely place indeed.
In an epidemiology framework, this leads to the concept of the superspreader, a pestulant overachiever who is responsible for a great deal many more infections than the average punter. Although this can occur due to pathogen mutation, it is more often the result of bad behavior, such as refusing to wear a mask, violating social distancing guidelines, or gathering in large numbers -- be it Sturgis or a campaign rally.
To give you a feel for how superspreading plays out, we turn to Javascript.
Here's the sim; explanation follows:
The population (n=500) is divided into a susceptible class (black) and two infected classes (red or blue). This allows you to explore two facets of superspreader dynamics. The first is to examine the effect of individual transmission bias. Moving the bias slider farther to the right increases the transmission probability for the blue team and decreases it for the red team, not only for patient zero but for all subsequent infecteds of that type.
The other slider controls clustering of the population. As the slider is moved to the left, the susceptible population becomes more concentrated at the center of the display. Infection spreads more rapidly through a dense population because of an increase in contact frequency. Now the determining factor is primarily which infection (red or blue) reaches the cluster first.
You should discover the source of most infections reflects your selected transmission bias in a dispersed population (a running score is tallied as the sim runs). However, a sufficiently high cluster value will overcome modest differences in transmission bias. This is the superspreader event -- the congregation of a large number of susceptibles in a confined space such that any introduction of the disease spreads rapidly, placing all susceptibles present at the event at a high risk of infection.
These parameters have a synergistic effect, with high-risk behavior in a dense gathering -- such as that in evidence at recent GOP functions -- providing a worst case scenario. The events we've read about in the news may not be true, but they are inevitable.
We're told the president tested positive for COVID, as did his fluffer Hope Hicks, truth scarecrow Kellyanne Conway, administration eye candy Kayleigh McEnany, Senators Mike Lee, Thom Tillis, and Ron Johnson, RNC person Ronna McDaniel, professional hanger-on Chris Christie, three WH reporters, and a WH staffer. Also the president of Notre Dame, this significant as he introduced the guest of honor at Mitch McConnell's SCOTUS coming out party for jurist and Octomom wannabe Amy Coney Barrett.
Which sounds kinda bad, but who can say. It's possible all of these people have tested positive, or some of them have, or none of them have. Because nothing claimed by this administration can be trusted. Nothing. Trump has been nothing but a perjury firehose since the day he took office. Even Charlie Brown would have stopped kicking at this particular football by now. As far as we know, every single elected Republican is dead and the country is being run by Bill Barr in a Trump mask. Or perhaps they all spent a week lounging 'round the pool at Mar-a-Lago sipping Mai Tais from an infant's skull and having a larf. Either scenario is equally likely. This is what four years of chaos has wrought.
But a tale need not be true in order for it to be a cautionary tale. There is an epidemiology lesson hiding in this story, and that lesson is titled The Superspreader.
The mass action epidemiology models we have previously explored assume all members of the population under study are indistinguishable. In the SIR model, this means the force of infection and recovery rate has the same numerical value for everyone -- be they LabKitty or Donald Trump.
However, equal infectivity and infective periods do not necessarily translate into equal infection success. To explain, let us view those we infect as our "children," those our children infect as our "grandchildren," and so on. Individuals can vary greatly in the number of "offspring" they ultimately produce, just like the number of literal offspring a person leaves behind varies a great deal even though we all possess the same basic reproductive machinery. Selfish baby machines like Michelle Duggar or Amy Coney Barrett would rapidly bury the planet neck deep in humans, whereas a world populated solely by barren antisocial wolverines publishing Javascript pandemic simulators on the Internet would quickly become a lonely place indeed.
In an epidemiology framework, this leads to the concept of the superspreader, a pestulant overachiever who is responsible for a great deal many more infections than the average punter. Although this can occur due to pathogen mutation, it is more often the result of bad behavior, such as refusing to wear a mask, violating social distancing guidelines, or gathering in large numbers -- be it Sturgis or a campaign rally.
To give you a feel for how superspreading plays out, we turn to Javascript.
Here's the sim; explanation follows:
susceptible (k) / infected (r or b)
(red) --- bias --- (blue)
(dense) --- clustering --- (sparse)
---
(red) --- bias --- (blue)
(dense) --- clustering --- (sparse)
The population (n=500) is divided into a susceptible class (black) and two infected classes (red or blue). This allows you to explore two facets of superspreader dynamics. The first is to examine the effect of individual transmission bias. Moving the bias slider farther to the right increases the transmission probability for the blue team and decreases it for the red team, not only for patient zero but for all subsequent infecteds of that type.
The other slider controls clustering of the population. As the slider is moved to the left, the susceptible population becomes more concentrated at the center of the display. Infection spreads more rapidly through a dense population because of an increase in contact frequency. Now the determining factor is primarily which infection (red or blue) reaches the cluster first.
You should discover the source of most infections reflects your selected transmission bias in a dispersed population (a running score is tallied as the sim runs). However, a sufficiently high cluster value will overcome modest differences in transmission bias. This is the superspreader event -- the congregation of a large number of susceptibles in a confined space such that any introduction of the disease spreads rapidly, placing all susceptibles present at the event at a high risk of infection.
These parameters have a synergistic effect, with high-risk behavior in a dense gathering -- such as that in evidence at recent GOP functions -- providing a worst case scenario. The events we've read about in the news may not be true, but they are inevitable.
DISCLAIMER: LabKitty is not an epidemiologist. For reliable COVID information, seek out a reputable source, preferably an independent institution immune to Trump's manipulation of the data, such as the World Health Organization or the COVID dashboard at Johns Hopkins University.
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