Re:How the coronavirus is driving new surveillance programs ...
by Tainnor on Friday April 17, 2020 @10:36AM
(#22899308)
you do understand what it means to be at the beginning of an exponential curve, right? this is not going to grow linearly, if this is not contained somehow, you could be looking at millions of deaths in the US alone.
again, all these numbers have been debated so many times before.
Re:How the coronavirus is driving new surveillance programs ...
by hartator on Friday April 17, 2020 @03:23PM
(#22902096)
> you could be looking at millions of deaths in the US alone
That's not true. Very worst case scenario will be 160,000 deaths based on our last data. But around 60,000 is more probable even we go out of lockdown right now.
Ref: https://covid19.healthdata.org/united-states-of-america
Re:How the coronavirus is driving new surveillance programs ...
by Tainnor on Saturday April 18, 2020 @08:04AM
(#22907126)
notice the preceding clause in the sentence you quoted, "if not contained somehow".
The numbers you cite all assume social distancing measures, it says so at the very top (and also the forecast only goes to the end of August, there is no guarantee that the virus doesn't come back in winter or so, we saw this with the Spanish flu).
Re:How the coronavirus is driving new surveillance programs ...
by samoa42 on Friday April 17, 2020 @03:11PM
(#22901985)
do you understand that exponential curves stem from unbound models?
sigmoids tend to reflect nature more realistically.
Re:How the coronavirus is driving new surveillance programs ...
by Tainnor on Saturday April 18, 2020 @07:52AM
(#22907071)
of course, no growth is truly exponential forever, but a sigmoid model is still basically exponential in its initial stage and as long as there is no big saturation factor (i.e. lots of herd immunity), the limiting factor is negligible.
so, as long as herd immunity is not high, the curve is approximately exponential.
Well actual death tolls for the US would be around 60,000 which is comparable of number of new suicides due to lockdowns.