worth keeping in mind is that the mRNA vaccines were tested mostly in the younger and the healthy and during as time of low seasonal disease prevalence. all this will tend to overstate efficacy vs what happens when you vaccinate the high risk and immunocompromised populations and expose them to seasonal surge.
dropping overall efficacy does not prove it will work less well on you as a young, healthy person. it means the risk strata underlying the aggregates changes.
the data is just not good enough to get too deeply into that without GIGO issues.
there are all kinds of non-linear issues and massive heterogeneity in social graphs, number of connections, risk levels, etc and this interact in complex ways that we really cannot model.
SIR adjusts for this poorly or fails to account for it entirely. if you flip the right few superconncected nodes from spreader to blocker, you get wildly disproportionate effects etc.
we don't even have a good map of naturally generated immunity or any idea how many people have really been exposed. with an R this high, it's gotta be extremely high. this also makes assessing vaccine efficacy really difficult. there is so much acquired resistance and vax crossover with it.
the data her has been an utter mess and is hard to compare across regions and temporally.
this has rendered a lot of the sophisticated tools difficult to apply in any meaningful way.
worth keeping in mind is that the mRNA vaccines were tested mostly in the younger and the healthy and during as time of low seasonal disease prevalence. all this will tend to overstate efficacy vs what happens when you vaccinate the high risk and immunocompromised populations and expose them to seasonal surge.
dropping overall efficacy does not prove it will work less well on you as a young, healthy person. it means the risk strata underlying the aggregates changes.
the data is just not good enough to get too deeply into that without GIGO issues.
there are all kinds of non-linear issues and massive heterogeneity in social graphs, number of connections, risk levels, etc and this interact in complex ways that we really cannot model.
SIR adjusts for this poorly or fails to account for it entirely. if you flip the right few superconncected nodes from spreader to blocker, you get wildly disproportionate effects etc.
we don't even have a good map of naturally generated immunity or any idea how many people have really been exposed. with an R this high, it's gotta be extremely high. this also makes assessing vaccine efficacy really difficult. there is so much acquired resistance and vax crossover with it.
the data her has been an utter mess and is hard to compare across regions and temporally.
this has rendered a lot of the sophisticated tools difficult to apply in any meaningful way.