mecej4 (Blackbelt) wrote:
From the point of view of the virus, both dead and no-longer-susceptible humans are effectively dead -- no longer useful in its drive to propagate, multiply and prosper, don't you think?
This is an absolute first time, but I disagree with you -- not about the model but about the statistical analysis. I have no interest in the model per se, I am interested in the data we can calibrate the model against and the quality of the data.
Data quality is a huge issue in all of the analysis I do - whether it is earthquakes -- fatalities, bridge monitoring or brick bond failure.
Let us assume there are 5 levels of data quality -- 1 is poor and 5 is perfect. The death data might be a 4 - because it has to be undercounted -- there are people in the US who live in backwaters that will have died and it is attributed to something else. The number of infected people -- we will never know accurately so maybe consider it a 2.
So taking the best data how do you and I fix the SIR model so it gives me a daily estimated death count -- I do not beleive it is strictly linear?
SUBROUTINE derivs(x,y,dydx)
INTEGER nrhs
REAL x,y(*),dydx(*)
COMMON nrhs
nrhs=nrhs+1
dydx(1)=-0.5*y(1)*y(2)
dydx(2)=(0.5*y(1)-0.2)*y(2)
dydx(3)=0.2*y(2)
write(*,'(1x,f10.4,2x,2f14.6)') dydx(1),dydx(2),dydx(3)
return
END
This is the standard model -- with the factors from COV-19 as presented in the limited literature. I need y(4) that is the old Y(3) - living recovered YN(3).
In this case I would really appreciate your opinion, I respect your opinion deeply.
I have looked at a fair amount of literature thru our library -- the models are good but they are really variants on this simple model or the code is not available - for obvious commercial reasons. The Imperial College Model is probably the best along with the WHO.
But the travel data from Wuhan does not match the results coming out now - that is obvious.
Really appreciate your thoughts on this matter.
John
