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Functions_for_msFigures.R
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Functions_for_msFigures.R
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#Functions.R
SumLoggedSquares <- function(x,y){
if(length(x)==length(y)){
return( sum((log10(x/y))^2) /length(x) )
}
else
{
stop("lengths do not match")
}
}
#To read simulation data
READ_SIM_DATA <- function(INPUTSIMNAME,SDNAME,GA,DATES){
SIMNAME = paste("./SimulationOutput/",INPUTSIMNAME,"/",SDNAME,"/",sep="")
DD<-read.csv(paste(SIMNAME,"GA_county_Killed.csv",sep="")) #deaths
EE <-read.csv(paste(SIMNAME,"GA_county_criticalHospitalised.csv",sep="")) #ICU
EE2 <-read.csv(paste(SIMNAME,"GA_county_subacuteHospitalised.csv",sep="")) #subacute
FF <-read.csv(paste(SIMNAME,"GA_county_deathrateperday.csv",sep="")) # deaths per day
GG <-read.csv(paste(SIMNAME,"GA_county_newcaserateperday.csv",sep="")) #new cases per day
HH <-read.csv(paste(SIMNAME,"GA_county_totalHospitalised.csv",sep="")) # total hospitalised
II <- read.csv(paste(SIMNAME,"GA_county_Susceptibles.csv",sep="")) # total susceptibles
CC <- read.csv(paste(SIMNAME,"GA_county_cumulativeHospitalised.csv",sep="")) # cumulative hospitalised
SZ <- dim(DD)[2]
NAMES = colnames(DD)[3:SZ]
newnames=c()
noX = strsplit(NAMES,"X")
for(aa in 1:length(noX)){
this = strsplit(noX[[aa]][2],"[.]")
now = paste(this[[1]][1],"-",this[[1]][2],"-",this[[1]][3],sep="")
newnames[aa] = now
}
INDXdate = c()
for(aa in 1:length(DATES)){
INDXdate[aa] = which(newnames == DATES[aa])
}
DD<-DD[,c(1,2,INDXdate+2)]
EE<-EE[,c(1,2,INDXdate+2)]
EE2<-EE2[,c(1,2,INDXdate+2)]
FF<-FF[,c(1,2,INDXdate+2)]
GG<-GG[,c(1,2,INDXdate+2)]
HH<-HH[,c(1,2,INDXdate+2)]
II<-II[,c(1,2,INDXdate+2)]
CC<-CC[,c(1,2,INDXdate+2)]
newnames = DATES
REGIONS=unique(GA$HospRegion)
RAT = matrix(0,dim(DD)[1],length(INDXdate))
RAT2 = RAT
for(aa in REGIONS){
INDX = which(GA$HospRegion==aa)
if(length(INDXdate)>1){
ICUdemand = colSums(EE[INDX,3:(2+length(INDXdate))])
}else{
ICUdemand = sum(EE[INDX,])
}
Capacity = GA$Reg_ICU[INDX[1]]
for(bb in INDX){
RAT[bb,] = (Capacity - ICUdemand)/(Capacity+ICUdemand)
RAT2[bb,] = ICUdemand/Capacity
}
}
JJ=rep(DD$FIP,length(newnames))
KK = rep(DD$County,length(newnames))
Dat = c()
NUMS = c()
NUMS2 = c()
NUMS3 = c()
NUMS4 = c()
NUMS5 = c()
NUMS6 =c()
NUMS7 =c()
NUMS8 =c()
NUMS9 = c()
NUMS10 =c()
for (aa in 1:length(newnames)){
Dat = c(Dat,rep(newnames[aa],159))
NUMS = c(NUMS,DD[,aa+2])
NUMS2 = c(NUMS2,EE[,aa+2])
NUMS10 = c(NUMS10,EE2[,aa+2])
NUMS3 = c(NUMS3,FF[,aa+2])
NUMS4 = c(NUMS4,GG[,aa+2])
NUMS5 = c(NUMS5,HH[,aa+2])
NUMS6 = c(NUMS6,RAT[,aa])
NUMS7 = c(NUMS7,II[,aa+2])
NUMS8 = c(NUMS8,RAT2[,aa])
NUMS9 = c(NUMS9,CC[,aa+2])
}
gad = list(FIPS=JJ, County = KK, date=Dat, deaths = NUMS, ICUreq = NUMS2, dpd = NUMS3,cpd = NUMS4, totHosp = NUMS5,ICUrat = NUMS6, Suscep = NUMS7,ICUrat2 = NUMS8,cumH = NUMS9,subacute = NUMS10)
GAtmap_sim <- merge(GA, gad, by = "FIPS")
return(GAtmap_sim)
}
#To get the data summed over counties by day
ExtractSumThisDate <- function(NUMS,DATES,DATESWANT){
NUMS_by_date = c()
for(aa in 1:length(DATESWANT)){
INDY = which(DATES == DATESWANT[aa])
NUMS_by_date[aa] = sum(NUMS[INDY])
}
return(NUMS_by_date)
}