What does that even mean ? It's slowly going down everywhere with serious lockdowns instead of going exponentially up. Anything less than exponential is a _dramatic_ decrease.
Exponential growth for a short period is seen in every epidemic, even small ones, because they all follow the same bell-curveish sort of shapes. Having observed that by itself doesn't mean anything - all epidemics grow slowly, then quickly, then slowly again as they approach their natural saturation point, then go into decline.
The idea that COVID-19 would grow exponentially starting from zero until a saturation of 100% was reached was only ever the outcome assumed by simulations, which have - as far as I can tell - a perfect track record of total failure. At least the sort of models created by universities in the modern era do.
To make this clear, the UW model driving much US policy response was updated several times in real time yet despite that managed to make wrong predictions for the exact same day it was updated.
And not just wrong, but reality fell far outside this models own uncertainty bounds too, so according to the modellers observed reality was not just unlikely but almost completely impossible.
The Imperial model that drove the UK ignored the possibility of hospital capacity increases entirely, and comes from a team that has (as far as I could find out) never successfully predicted an epidemic.
Real diseases don't play out like unending exponential growth until the whole world is infected - they go into decline far before that.
Quite why this is so is unknown. The dark secret of epidemiology is nobody really understands viral spread. Every model is wrong and nobody can explain why. Germ theory must have a major gap, so what is it? My guess is a combination of much higher asymptomatic spread than currently understood (asymptomatic infection not being a phenomenon that's really been investigated much as only academic modellers care about it), and perhaps some forms of natural immunity to viruses that aren't based on antibodies.
Whatever the cause, it'd be wrong to compare the observed outcomes vs the predictions from models that are known to be completely useless and say, there's been a decrease.
The idea that COVID-19 would grow exponentially starting from zero until a saturation of 100% was reached was only ever the outcome assumed by simulations, which have - as far as I can tell - a perfect track record of total failure. At least the sort of models created by universities in the modern era do.
To make this clear, the UW model driving much US policy response was updated several times in real time yet despite that managed to make wrong predictions for the exact same day it was updated.
https://twitter.com/alexberenson/status/1247010068148731904?...
And not just wrong, but reality fell far outside this models own uncertainty bounds too, so according to the modellers observed reality was not just unlikely but almost completely impossible.
https://twitter.com/alexberenson/status/1246465515704463360?...
(note how wide the uncertainty bounds are, yet that it's still wrong)
More commentary:
https://twitter.com/alexberenson/status/1245416486879051778?...
https://twitter.com/AlexBerenson/status/1248640413835108355
The Imperial model that drove the UK ignored the possibility of hospital capacity increases entirely, and comes from a team that has (as far as I could find out) never successfully predicted an epidemic.
Real diseases don't play out like unending exponential growth until the whole world is infected - they go into decline far before that.
Quite why this is so is unknown. The dark secret of epidemiology is nobody really understands viral spread. Every model is wrong and nobody can explain why. Germ theory must have a major gap, so what is it? My guess is a combination of much higher asymptomatic spread than currently understood (asymptomatic infection not being a phenomenon that's really been investigated much as only academic modellers care about it), and perhaps some forms of natural immunity to viruses that aren't based on antibodies.
Whatever the cause, it'd be wrong to compare the observed outcomes vs the predictions from models that are known to be completely useless and say, there's been a decrease.