Thanks for the post! This may not be helpful, but one thing I would be curious to see would be how the dispersion coefficient k (Discussed here; I’m sure there’s a better reference source) affected the importance of having many sites. With COVID, a lot of transmission came from superspreader events, which intuitively would increase the variance of how quickly it spread in different sites. On the other hand, the flu has a low proportion of superspreader events, so testing in a well connected site might explain more of the variance?
I haven’t done or seen any modeling on this, but intuitively I would expect the variance due to superspreading to have most of its impact in the very early days, when single superspreading events can meaningfully accelerate the progress of the pandemic in a specific location, and to be minimal by the time you get to ~1% cumulative incidence?
Thanks for the post! This may not be helpful, but one thing I would be curious to see would be how the dispersion coefficient k (Discussed here; I’m sure there’s a better reference source) affected the importance of having many sites. With COVID, a lot of transmission came from superspreader events, which intuitively would increase the variance of how quickly it spread in different sites. On the other hand, the flu has a low proportion of superspreader events, so testing in a well connected site might explain more of the variance?
I haven’t done or seen any modeling on this, but intuitively I would expect the variance due to superspreading to have most of its impact in the very early days, when single superspreading events can meaningfully accelerate the progress of the pandemic in a specific location, and to be minimal by the time you get to ~1% cumulative incidence?