3 ms·

Ok, makes sense. But why does this make any difference to the crux of my argument? - if that supermarket employee is wearing a mask, they’re going to spread it

Ok, makes sense. But why does this make any difference to the crux of my argument? - if that supermarket employee is wearing a mask, they’re going to spread it to less people.

mike_hearn 5y ago
R=1.1=everything goes to shit is not a valid inference. R is just the derivative of the case graph. It can and does change on a daily basis, says nothing about absolute levels, and says nothing about how it will evolve over time or what the actual peak levels of infections will be. You can find "exponential growth" in nearly any graph if you want, look at a sine wave for example, and viruses in particular are a curious choice to describe this way because epidemics show S-curve logistic growth, not exponential. It's very confusing to people that it's so often described as exponential because that implies unbounded and very rapid doubling until a day/serial interval before saturation half the population is infected at once, whereas we know that epidemics don't do that in reality.
- jonplackett 5y ago
  Ok so what I mean is r being 1.1... over time. I think you’re looking to find holes in a perfectly reasonable argument by adding complexity.
  - mike_hearn 5y ago
    Over how much time and how does that variable relate to the actual length of time an epidemic of an (apparently) seasonal respiratory virus is expected to last? I don't think it's just a nitpicky minor thing. Governments have pretty regularly been making decisions and citing the value of "R" (they mean Rt), or "exponential growth", as a justification for new restrictions, apparently without realizing that by itself these things means little and justify nothing. Exponential growth can only be said to be a problem when taking into account the serial interval, the actual exponent, the starting population sizes, total population sizes, fixed capacity limits (e.g. hospital bed counts) and so on. Yet the scientists advising governments routinely ignore all those things.

Ok, makes sense. But why does this make any difference to the crux of my argument? - if that supermarket employee is wearing a mask, they’re going to spread it to less people.

mike_hearn 5y ago

R=1.1=everything goes to shit is not a valid inference. R is just the derivative of the case graph. It can and does change on a daily basis, says nothing about absolute levels, and says nothing about how it will evolve over time or what the actual peak levels of infections will be. You can find "exponential growth" in nearly any graph if you want, look at a sine wave for example, and viruses in particular are a curious choice to describe this way because epidemics show S-curve logistic growth, not exponential. It's very confusing to people that it's so often described as exponential because that implies unbounded and very rapid doubling until a day/serial interval before saturation half the population is infected at once, whereas we know that epidemics don't do that in reality.

jonplackett 5y ago
Ok so what I mean is r being 1.1... over time. I think you’re looking to find holes in a perfectly reasonable argument by adding complexity.
- mike_hearn 5y ago
  Over how much time and how does that variable relate to the actual length of time an epidemic of an (apparently) seasonal respiratory virus is expected to last? I don't think it's just a nitpicky minor thing. Governments have pretty regularly been making decisions and citing the value of "R" (they mean Rt), or "exponential growth", as a justification for new restrictions, apparently without realizing that by itself these things means little and justify nothing. Exponential growth can only be said to be a problem when taking into account the serial interval, the actual exponent, the starting population sizes, total population sizes, fixed capacity limits (e.g. hospital bed counts) and so on. Yet the scientists advising governments routinely ignore all those things.