/
ExecutiveSummaryPlots.R
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ExecutiveSummaryPlots.R
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#R scripts to make figures for executive summary report
#load dependencies
library(sf)
library(tmap)
options(stringsAsFactors = FALSE)
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
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-2)]
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)]
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$County,length(newnames))
Dat = c()
NUMS = c()
NUMS2 = c()
NUMS3 = c()
NUMS4 = c()
NUMS5 = c()
NUMS6 =c()
NUMS7 =c()
NUMS8 =c()
NUMS9 = c()
for (aa in 1:length(newnames)){
Dat = c(Dat,rep(newnames[aa],159))
NUMS = c(NUMS,DD[,aa+2])
NUMS2 = c(NUMS2,EE[,aa+2])
NUMS3 = c(NUMS3,FF[,aa+2])
NUMS4 = c(NUMS4,GG[,aa+2])
NUMS5 = c(NUMS5,HH[,aa+2])
NUMS6 = c(NUMS6,RAT[,aa])
NUMS7 = c(NUMS7,II[,aa+2])
NUMS8 = c(NUMS8,RAT2[,aa])
NUMS9 = c(NUMS9,CC[,aa+2])
}
gad = list(FIPS=JJ, County = KK, date=Dat, deaths = NUMS, ICUreq = NUMS2, dpd = NUMS3,cpd = NUMS4, totHosp = NUMS5,ICUrat = NUMS6, Suscep = NUMS7,ICUrat2 = NUMS8,cumH = NUMS9)
GAtmap_sim <- merge(GA, gad, by = "FIPS")
return(GAtmap_sim)
}
#######load shapefile########
counties <- st_read("./data/counties.shp")
GA <- counties[counties$STATE_NAME == "Georgia", ]
#data taken from GDPH daily status reports https://dph.georgia.gov/covid-19-daily-status-report
recordedCases= c(1097,1387,1643,2198,2446,2683,3032,4117,4748,5444,5967,6383,6742,7558,9156,9901,10885,11859,12261,12550,13621,14578,15409,16369,17432,18489,19398,20166,21102,21883,22491,23216,23481,23913,24861)
recordedDeaths = c(38,47,56,65,79,83,102,125,154,176,198,208,219,294,348,362,412,425,432,442,480,524,579,617,668,689,774,818,846,881,899,907,916,971,1036)
recordedHospitalised = c(361,438,509,607,660,678,773,885,1013,1129,1222,1266,1296,1393,1899,1993,2298,2454,2491,2518,2702,2858,3024,3260,3395,3489,3702,3885,4018,4154,4322,4353,4377,4681,4898)
LREC = length(recordedCases)
#calculate cases per day and deaths per day
recordedCPD = recordedCases[2:LREC] - recordedCases[1:(LREC-1)]
recordedDPD = recordedDeaths[2:LREC] - recordedDeaths[1:(LREC-1)]
#simulation names to load in.
INPUTSIMNAME = c("March28_eig_RunApr23","March28_sim_RunApr23","28thApr_Run_int_1stMay")
#Note the dates of interest for plotting
DATESRECORD = c("2020-03-24","2020-03-25","2020-03-26","2020-03-27","2020-03-28","2020-03-29","2020-03-30","2020-03-31","2020-04-01","2020-04-02","2020-04-03","2020-04-04","2020-04-05","2020-04-06","2020-04-07","2020-04-08","2020-04-09","2020-04-10","2020-04-11","2020-04-12","2020-04-13","2020-04-14","2020-04-15","2020-04-16","2020-04-17","2020-04-19","2020-04-20","2020-04-21","2020-04-22","2020-04-23","2020-04-24","2020-04-25","2020-04-26","2020-04-27","2020-04-28")
modDatesRec = as.Date(DATESRECORD)
DATESINTEREST = seq(as.Date("2020-03-28"), as.Date("2020-07-04"), by="days")
modDates = as.Date(DATESINTEREST)
DATESINTEREST2 = seq(as.Date("2020-03-28"), as.Date("2020-04-28"), by="days")
modDates2 = as.Date(DATESINTEREST2)
DATESINTEREST3 = seq(as.Date("2020-04-28"), as.Date("2020-07-04"), by="days")
modDates3 = as.Date(DATESINTEREST3)
SDname <- c("Social_1","Social_2","Social_3","Social_4") #social distancing file extensions
## Read in the simulation data
#1. plot against data using social distancing of 50% [evaluation model]
E_3 = READ_SIM_DATA(INPUTSIMNAME[1],SDname[3],GA,DATESINTEREST)
#2. plot out from April 28 at 50% SD, then from May 1for 3 SD with 0%, 50% and 75% reductions in transmission rates.
EE1= READ_SIM_DATA(INPUTSIMNAME[3],SDname[1],GA,DATESINTEREST3)
EE3= READ_SIM_DATA(INPUTSIMNAME[3],SDname[3],GA,DATESINTEREST3)
EE4= READ_SIM_DATA(INPUTSIMNAME[3],SDname[4],GA,DATESINTEREST3)
###MAKE FIGURES
# FIG 1: Show state wide deaths, hospitalised and cases
LLL = length(DATESRECORD)
LWD=2
PSZ=1.2
CL="black"
dev.new(width=7*0.81, height=9*0.81, unit="in")
PCH1 =19
PCH2 =21
TIMES= as.Date(c("2020-03-24","2020-03-28","2020-04-01","2020-04-28","2020-05-01","2020-06-01","2020-07-01","2020-07-04"))
par(mfrow=c(3,1),mar= c(3.8,4.5,1,2))
X1 = 18345 #March 24th
X2 = X1 + 102 #Jul 4th
#deaths
plot(modDatesRec[5:LLL],recordedDeaths[5:LLL], xlim =c(X1 ,X2),ylim=c(0,5000),xlab="",ylab="Cumulative deaths",cex.lab=CLS,pch=PCH2,bg=CL,col=CL, xaxs='i',xaxt="n")
axis.Date(1,at=TIMES,format="%e %b",las=2) # https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html
points(modDatesRec[5:LLL],recordedDeaths[5:LLL],col=CL,pch=PCH2,bg=CL,cex=PSZ)
points(modDatesRec[1:4],recordedDeaths[1:4],bg=NA,col=CL,pch=PCH2,cex=PSZ)
lines(modDates2,ExtractSumThisDate(E_3$deaths,E_3$date,DATESINTEREST2),col="blue",lty=1,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE1$deaths,EE1$date,DATESINTEREST3),col="green",lty=1,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE3$deaths,EE3$date,DATESINTEREST3),col="olivedrab",lty=2,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE4$deaths,EE4$date,DATESINTEREST3),col="darkgreen",lty=4,lwd=LWD)
legend("topleft",bty="n",legend=c("Prior data","Data","Evaluation model","Model with back to business as usual","Model with 50% transmission reduction","Model with 75% transmission reduction"), col=c(CL,CL,"blue","green","olivedrab","darkgreen"),pt.bg=c(NA,CL,rep(NA,4)),lty=c(NA,NA,1,1,2,4),pch=c(1,PCH2,rep(NA,4)),ncol=1,cex=1.25,pt.cex=PSZ,lwd=2)
MminD = ExtractSumThisDate(EE1$cumH,EE1$date,DATESINTEREST3)[1] - recordedHospitalised[LLL]
#hospitalised
plot(modDatesRec[5:LLL],recordedHospitalised[5:LLL], xlim =c(X1 ,X2),ylim=c(0,25000),xlab="",ylab="Cumulative hospitalised",cex.lab=CLS,pch=PCH2,bg=CL,col=CL, xaxs='i',xaxt="n")
axis.Date(1,at=TIMES,format="%e %b",las=2) # https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html)
points(modDatesRec[5:LLL],recordedHospitalised[5:LLL],col=CL,pch=PCH2,bg=CL,cex=PSZ)
points(modDatesRec[1:4],recordedHospitalised[1:4],bg=NA,col=CL,pch=PCH2,cex=PSZ)
lines(modDates2,ExtractSumThisDate(E_3$cumH,E_3$date,DATESINTEREST2),col="blue",lty=1,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE1$cumH,EE1$date,DATESINTEREST3)-MminD,col="green",lty=1,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE3$cumH,EE3$date,DATESINTEREST3)-MminD,col="olivedrab",lty=2,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE4$cumH,EE4$date,DATESINTEREST3)-MminD,col="darkgreen",lty=4,lwd=LWD)
legend("topleft",bty="n",legend=c("Prior data","Data","Evaluation model","Model with back to business as usual","Model with 50% transmission reduction","Model with 75% transmission reduction"), col=c(CL,CL,"blue","green","olivedrab","darkgreen"),pt.bg=c(NA,CL,rep(NA,4)),lty=c(NA,NA,1,1,2,4),pch=c(1,PCH2,rep(NA,4)),ncol=1,cex=1.25,pt.cex=PSZ,lwd=2)
DRABOLIVE = rgb(107/255,142/255,35/255,0.5)
DARKGREEN = rgb(0,100/255,0,0.7)
GREEN = rgb(0,255/255,0,0.5)
LOW=5
HIGH=10
#cases
#Note: here, we down project cases based on there being 5-10 extra cases per recorded case.
plot(modDatesRec[5:LLL],recordedCases[5:LLL], xlim =c(X1 ,X2),ylim=c(1000,120000),xlab="",ylab="Cumulative recorded cases",cex.lab=CLS,pch=PCH2,bg=CL,col=CL, xaxs='i',xaxt="n")
axis.Date(1,at=TIMES,format="%e %b",las=2) # https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html)
XXX = c(modDates2,rev(modDates2))
THI = POPSIZE- ExtractSumThisDate(E_3$Suscep,E_3$date,DATESINTEREST2)
YYY = c(THI/HIGH,rev(THI/LOW))
#rgb(1, 0, 0,0.5)
polygon(XXX,YYY,col=rgb(0, 0, 1, 0.5),border=NA)
MminDH = (POPSIZE-ExtractSumThisDate(EE3$Suscep,EE4$date,DATESINTEREST3)[1])/HIGH - recordedCases[LLL]
MminDL = (POPSIZE-ExtractSumThisDate(EE3$Suscep,EE4$date,DATESINTEREST3)[1])/LOW - recordedCases[LLL]
MminML = (POPSIZE-ExtractSumThisDate(EE3$Suscep,EE4$date,DATESINTEREST3)[1])/7.5 - recordedCases[LLL]
XXX = c(modDates3,rev(modDates3))
THI = POPSIZE- ExtractSumThisDate(EE4$Suscep,EE4$date,DATESINTEREST3)
YYY = c(THI/HIGH-MminML,rev(THI/LOW -MminML))
polygon(XXX,YYY,col=DARKGREEN,border=NA)
lines(modDates3,THI/7.5-MminML,col='darkgreen',lwd=2)
THI = POPSIZE- ExtractSumThisDate(EE3$Suscep,EE3$date,DATESINTEREST3)
YYY = c(THI/HIGH-MminML,rev(THI/LOW -MminML))
polygon(XXX,YYY,col=DRABOLIVE,border=NA)
lines(modDates3,THI/7.5-MminML,col=rgb(107/255,152/255,35/255,1),lwd=2)
THI = POPSIZE- ExtractSumThisDate(EE1$Suscep,EE1$date,DATESINTEREST3)
YYY = c(THI/HIGH-MminML,rev(THI/LOW -MminML))
polygon(XXX,YYY,col=GREEN,border=NA)
lines(modDates3,THI/7.5 -MminML,col='green',lwd=2)
points(modDatesRec[5:LLL],recordedCases[5:LLL],col=CL,pch=PCH2,bg=CL,cex=PSZ)
points(modDatesRec[1:4],recordedCases[1:4],bg=NA,col=CL,pch=PCH2,cex=PSZ)
legend("topleft",bty="n",legend=c("Prior data","Data","Ascertainment uncertainty","Model with back to business as usual","Model with 50% transmission reduction","Model with 75% transmission reduction"), col=c(CL,CL,rgb(0, 0, 1, 0.5),"green","olivedrab","darkgreen"),pt.bg=c(NA,CL,rep(NA,4)),lty=c(NA,NA,1,1,1,1),pch=c(1,PCH2,rep(NA,4)),ncol=1,cex=1.25,pt.cex=PSZ,lwd=2)
dev.copy2pdf(file="figures/Summary1a.pdf")
dev.copy(png,file="figures/Summary1a.png",width=7*0.81, height=9*0.81, unit="in",res=300)
dev.off()
# FIG 2: recorded cases and deaths per day
dev.new(width=7*0.81, height=7*0.81, unit="in")
par(mfrow=c(2,1),mar= c(3.8,4.5,1,2))
#recorded cases per day
#Note: here, we down project cases based on there being 5-10 extra cases per recorded case.
plot(modDates2,ExtractSumThisDate(E_3$cpd,E_3$date,DATESINTEREST2),col="white",log="",ylim=c(1,5200),xlim=c(X1,X2),xlab="",ylab="Recorded cases per day", xaxs='i',xaxt="n")
axis.Date(1,at=TIMES,format="%e %b",las=2,cex.axis=0.8) # https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html)
#evaluation phase
XXX = c(modDates2,rev(modDates2))
THI = ExtractSumThisDate(E_3$cpd,E_3$date,DATESINTEREST2)
YYY = c(THI/HIGH,rev(THI/LOW))
polygon(XXX,YYY,col=rgb(0, 0, 1, 0.5),border=NA)
#forcast phase
XXX = c(modDates3,rev(modDates3))
THI = ExtractSumThisDate(EE4$cpd,EE4$date,DATESINTEREST3)
YYY = c(THI/HIGH,rev(THI/LOW))
polygon(XXX,YYY,col=DARKGREEN,border=NA)
THI = ExtractSumThisDate(EE3$cpd,EE3$date,DATESINTEREST3)
YYY = c(THI/HIGH,rev(THI/LOW))
polygon(XXX,YYY,col=DRABOLIVE,border=NA)
THI = ExtractSumThisDate(EE1$cpd,EE1$date,DATESINTEREST3)
YYY = c(THI/HIGH,rev(THI/LOW))
polygon(XXX,YYY,col=GREEN,border=NA)
points(modDatesRec[1:4],c(0,recordedCPD[1:3]),col=CL,pch=PCH2,bg=NA,cex=PSZ)
points(modDatesRec[5:LLL],recordedCPD[4:(LLL-1)],col=CL,pch=PCH2,bg=CL,cex=PSZ)
legend("topleft",bty="n",legend=c("Prior data","Data","Ascertainment uncertainty","Model with back to business as usual","Model with 50% transmission reduction","Model with 75% transmission reduction"), col=c(CL,CL,rgb(0, 0, 1, 0.5),"green","olivedrab","darkgreen"),pt.bg=c(NA,CL,rep(NA,4)),lty=c(NA,NA,1,1,1,1),pch=c(1,PCH2,rep(NA,4)),ncol=1,cex=0.8,pt.cex=PSZ,lwd=2)
#deaths per day
plot(modDates2,ExtractSumThisDate(E_3$dpd,E_3$date,DATESINTEREST2),col="white",log="",ylim=c(1,250),xlim=c(X1,X2),xlab="",ylab="Deaths per day", xaxs='i',xaxt="n")
axis.Date(1,at=TIMES,format="%e %b",las=2,cex.axis=0.8) # https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html)
lines(modDates2,ExtractSumThisDate(E_3$dpd,E_3$date,DATESINTEREST2),col="blue",lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE1$dpd,EE1$date,DATESINTEREST3),col="green",lty=1,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE3$dpd,EE3$date,DATESINTEREST3),col="olivedrab",lty=2,lwd=LWD)
lines(modDates3,ExtractSumThisDate(EE4$dpd,EE4$date,DATESINTEREST3),col="darkgreen",lwd=LWD,lty=4)
points(modDatesRec[1:4],c(0,recordedDPD[1:3]),col=CL,pch=PCH2,bg=NA,cex=PSZ)
points(modDatesRec[5:LLL],recordedDPD[4:(LLL-1)],col=CL,pch=PCH2,bg=CL,cex=PSZ)
legend("topleft",bty="n",legend=c("Prior data","Data","Evaluation model","Model with back to business as usual","Model with 50% transmission reduction","Model with 75% transmission reduction"), col=c(CL,CL,"blue","green","olivedrab","darkgreen"),pt.bg=c(NA,CL,rep(NA,4)),lty=c(NA,NA,1,1,2,4),pch=c(1,PCH2,rep(NA,4)),ncol=1,cex=0.8,pt.cex=PSZ,lwd=2)
dev.copy2pdf(file="figures/Summary2.pdf")
dev.copy(png,file="figures/Summary2.png",width=7*0.81, height=6*0.81, unit="in",res=300)
dev.off()
#FIG 3: Maps of cumulative deaths on 4 July 2020.
#read in data for maps
EM_1 = READ_SIM_DATA(INPUTSIMNAME[3],SDname[1],GA,c("2020-07-04","2020-07-03"))
EM_3 = READ_SIM_DATA(INPUTSIMNAME[3],SDname[3],GA,c("2020-07-04","2020-07-03"))
EM_4 = READ_SIM_DATA(INPUTSIMNAME[3],SDname[4],GA,c("2020-07-04","2020-07-03"))
MXD =max(EM_1$deaths,EM_4$deaths,na.rm=TRUE)
deathbreaks2 = c(-1:log10(floor(max(MXD))), log10(MXD))
TIT = ">30,000 deaths with back to BAU model projections by 2020-07-04"
TIT2 = "~2,000 deaths with 75% reduction in transmission rates by 2020-07-04"
d = sort(unique(EM_1$date))[1]
ThisGAtmap <- EM_1[EM_1$date == d,]
m1 = tm_shape(ThisGAtmap ) +
tm_borders(alpha=0.6,lwd=0.5)+
tm_fill(col = "deaths", style = "cont",breaks = 10^deathbreaks2, textNA = "No data",
colorNA = "lightgrey",
palette = PAL,
title ="projected\ncumulative\ndeaths") +
tm_layout(title=TIT, title.position = c("LEFT","BOTTOM"),attr.outside=TRUE,attr.position=c("right","top"),panel.label.size = 1,legend.title.size = 1,
legend.text.size = 0.5, legend.outside.position = "right" , legend.outside.size = .6)
ThisGAtmap <- EM_4[EM_4$date == d,]
m2 = tm_shape(ThisGAtmap ) +
tm_borders(alpha=0.6,lwd=0.5)+
tm_fill(col = "deaths", style = "cont", breaks = 10^deathbreaks2 , textNA = "No data",
colorNA = "lightgrey",
palette = PAL,
title ="projected\ncumulative\ndeaths") +
tm_layout(title=TIT2, title.position = c("LEFT","BOTTOM"),attr.outside=TRUE,attr.position=c("RIGHT","top"),attr.just="right",panel.label.size = 1,legend.title.size = 1,
legend.text.size = 0.5, legend.outside.position = "RIGHT" , legend.outside.size = .6)
M3 = tmap_arrange(m1,m2,nrow=2)
tmap_save(M3, width = 4*0.81, height = 9*0.81, units="in", filename = paste("figures/Summary1b.png", sep=""))
tmap_save(M3, width = 4*0.81, height = 9*0.81, units="in", filename = paste("figures/Summary1b.pdf", sep=""))