Interesting study on super spreaders

2,258 Views | 8 Replies | Last: 4 yr ago by plain_o_llama
missyaggie
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AG
https://quillette.com/2020/03/27/covid-19-science-update-for-march-27-super-spreaders-and-the-need-for-new-prediction-models/
plain_o_llama
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Here is the CDC article referenced

https://wwwnc.cdc.gov/eid/article/26/6/20-0495_article

There is a lot of useful information in Jonathan Kay's Quillette article. It is worth a read. If we are going to settle on strategies to combat the disease we have to understand how the projections of the disease spread are being generated. We have to make projections, but any method has limitations. Kay focuses on how R0, a computed retrospective average, is not uniform across the population and changes with our behavioral changes. In particular, he points to how Super Spreaders account for a disproportionate number of cases. This has implications for how we model disease spread, but also what efforts are necessary to reduce spread.


For some more on the limitations of R0 try

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302597/

Although R0 might appear to be a simple measure that can be used to determine infectious disease transmission dynamics and the threats that new outbreaks pose to the public health, the definition, calculation, and interpretation of R0 are anything but simple. R0 remains a valuable epidemiologic concept, but the expanded use of R0 in both the scientific literature and the popular press appears to have enabled some misunderstandings to propagate. R0 is an estimate of contagiousness that is a function of human behavior and biological characteristics of pathogens. R0 is not a measure of the severity of an infectious disease or the rapidity of a pathogen's spread through a population.

and

R0 can be misrepresented, misinterpreted, and misapplied in a variety of ways that distort the metric's true meaning and value. Because of these various sources of confusion, R0 must be applied and discussed with caution in research and practice. This epidemiologic construct will only remain valuable and relevant when used and interpreted correctly.
Windy City Ag
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AG
Quote:

R0 can be misrepresented, misinterpreted, and misapplied in a variety of ways that distort the metric's true meaning and value. Because of these various sources of confusion, R0 must be applied and discussed with caution in research and practice. This epidemiologic construct will only remain valuable and relevant when used and interpreted correctly.

The good news here at Texags is we have a ton of self-appointed Holiday Inn statistical researchers. I can't imagine the data being misinterpreted or errenous projections being offered up in this forum.
Texaggie7nine
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This is the type of data that vexes me the most. So much seemingly conflicting data. Studies showing that if you are in the same household as someone with CV, the chances of you getting it are like 5%. But then data that shows that 20+ people got it at a ball game or single meeting blows that theory out of the water. Unless there are just some people out there that are super efficient at speading the virus while most everyone else sucks at it.
7nine
JCA1
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AG
I'm picturing Gary Oldman's character from Friends.
TxAG#2011
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I'm imagining most of these are those people you see coughing and sneezing everywhere, never washing hands, touching everything.
96AustinAg
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Texaggie7nine said:

This is the type of data that vexes me the most. So much seemingly conflicting data. Studies showing that if you are in the same household as someone with CV, the chances of you getting it are like 5%. But then data that shows that 20+ people got it at a ball game or single meeting blows that theory out of the water. Unless there are just some people out there that are super efficient at speading the virus while most everyone else sucks at it.
I think that is exactly what happens - the problem is that without extensive testing and tracking, we don't know who those people are, and if you don't socially distance, you risk running into one of them. From that point, you don't know if you yourself might be a super spreader.
KR Training staff instructor - www.krtraining.com
Halconblack
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AG
I think the most important inference from the data is not necessarily the SSE's themselves, but the type of economic and social interactions that make SSE's an important vector in the spread. These are spaces where people are going to forced in close proximity for a decent amount of time. As we approach Easter it might allow the sitting President to prescribe a policy of something like the following:

Businesses and Government Buildings
Indoor business must establish separate entry and exit pathways that maintain 6 ft spacing
Indoor business must maintain a ratio of less than 1 person per 110 sqft
Outdoor business must maintain 6 ft spacing
Customers in Retail and Commercial business should wear a non-surgical mask
Theaters and stadiums to remain closed until further notice
Service providers will adhere to indoor and outdoor business requirements

Hospitals, clinics, nursing homes, long-term care
Continue to operate as prescribed

Public Transportation
Non-surgical masks to be required on all flights, buses and trains
TSA will screen all passengers for elevated temperature during security screenings
v/r
2wealfth Man
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AG
Let the outdoors board deal with super-spreaders!
plain_o_llama
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Yes, the data is vexing. The underlying reality is difficult to pin down and it isn't showing any signs of being Simple.

That is looking to be true about the spread of the virus and resulting disease and the experience of the disease if infected.

You are hitting on something that is going to make policy very difficult. This idea that due to some still hard to pin down set of circumstances one can usually have some level of interaction with people and fairly confidently avoid infection. Yet, there are these rare situations where you can't be confident.

Likewise, it appears everyones experience of the disease if infected is highly individual. We can slice and dice the data various ways and get some feel for averages based on age, sex, co-morbidities, etc. Right now they are really still relative averages. Without a better feel for how many infected people are not tested we may be way off on the magnitude.

But even if we get to the point where we could say something like "a 45 year old male type 1 diabetic with no other issues has a .5% chance of dying if infected" what does that mean for an individual? We like to think that has somehow converted an uncertainty into a risk. Yet, the only notion for hedging the risk takes us back to avoiding infection. Ugh.

However, there is one thing I do feel confident about. A set of 45 year old male type 1 diabetics will not agree on what is the best policy course, even for people like themselves. :-)
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