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Understanding R0
Since it seems that most members visit General Discussion, probably this is a good place to post a topic like this, since everyone uses the term. And it would be nice to understand it.
A guide from Nature.com meant for more general reading public:
https://www.nature.com/articles/d41586-020-02009-w
PDE models of disease spreading through human populations (an epidemic) have been around for 100 years. One of the concepts that is important is "R". It is how many members of the population does one infected individual infect in a so-called secondary attack.
If the R value is less than one the disease will eventually die out. Greater than one means the number of infections will spread exponentially.
Sounds simple. It is not simple.
R0 is defined to mean that there are no immune individuals in the population, the disease is considered brand new. Several other assumptions about the population also apply:
homogeneity, fixed density (e.g., not urban + rural), non-seasonal.
So, you will find Rs as a seasonal R value for example, to make it clear what assumption is at play. It also is affected by environmental conditions, such as influenza infection rates change with humidity.
So, R is probably not what you thought.
And it is something that results in data like this, look at the ensemble graph as a final result on the upper right:
https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html
Datasets also undergo "dissociation" in an attempt to remove the varying effects that alter transmission rates.
Here is an example paper that tries to explain R values and the resulting model interpretation for three Covid-19 variants:
https://www.medrxiv.org/content/10.1101/2021.05.19.21257476v1
Since it seems that most members visit General Discussion, probably this is a good place to post a topic like this, since everyone uses the term. And it would be nice to understand it.
A guide from Nature.com meant for more general reading public:
https://www.nature.com/articles/d41586-020-02009-w
PDE models of disease spreading through human populations (an epidemic) have been around for 100 years. One of the concepts that is important is "R". It is how many members of the population does one infected individual infect in a so-called secondary attack.
If the R value is less than one the disease will eventually die out. Greater than one means the number of infections will spread exponentially.
Sounds simple. It is not simple.
R0 is defined to mean that there are no immune individuals in the population, the disease is considered brand new. Several other assumptions about the population also apply:
homogeneity, fixed density (e.g., not urban + rural), non-seasonal.
So, you will find Rs as a seasonal R value for example, to make it clear what assumption is at play. It also is affected by environmental conditions, such as influenza infection rates change with humidity.
So, R is probably not what you thought.
And it is something that results in data like this, look at the ensemble graph as a final result on the upper right:
https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html
Datasets also undergo "dissociation" in an attempt to remove the varying effects that alter transmission rates.
Here is an example paper that tries to explain R values and the resulting model interpretation for three Covid-19 variants:
https://www.medrxiv.org/content/10.1101/2021.05.19.21257476v1
