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5Norovirus.R
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organism<-"Norovirus"
source("Parameters and distributions.R")
#1.A single fomite contact & a single orifice contact
##1.0 Matrix -------------------------------------------------
numevents<-2
eventsname<-c("pre", "post")
Conc<-matrix(nrow=numevents,ncol=iterations)
rownames(Conc)<-eventsname
Dose<-matrix(nrow=numevents, ncol=iterations)
rownames(Dose)<-eventsname
Risk<-matrix(nrow=numevents, ncol=iterations)
rownames(Risk)<-eventsname
##1.1 Baseline scenario ----------------------------------------
Conc[1,] <- TE.all*Frac.HS*Conc.i.surface
Dose[1,]<- TE.face*T.handarea*Frac.HF*Conc [1, ]
Risk[1,]<- 0.722*(1-exp(-Dose[1,]/1106))
##1.2 Intervention scenario ----------------------------------------
Conc[2,] <- TE.all*Frac.HS*(Conc.i.surface/10^(Reduc.intv))
Dose[2,]<- TE.face*T.handarea*Frac.HF*Conc[2, ]
Risk[2,]<- 0.722*(1-exp(-Dose[2,]/1106))
##1.3 plotting---------------------------------------------------------------
library(ggplot2)
library(ggpubr)
Conc.df<-as.data.frame(t(Conc))
Dose.df<-as.data.frame(t(Dose))
Risk.df<-as.data.frame(t(Risk))
event<-rep(c(rep("pre",iterations),rep("post",iterations)),3)
type<-c(rep("Conc",2*iterations),rep("Dose",2*iterations),rep("Risk",2*iterations))
value<-c(Conc.df$"pre", Conc.df$"post", Dose.df$"pre", Dose.df$"post",Risk.df$"pre", Risk.df$"post")
data<-data.frame(event,type,value)
windows()
ggplot(data)+geom_violin(aes(x=event,y=value,fill=type, group=event),alpha=0.3,draw_quantiles = c(0.25,0.5,0.75))+
facet_wrap(~type,scales="free") +
scale_y_continuous(trans="log10") +
scale_x_discrete(limits=c("pre","post"))+
ggtitle("Comparison between Pre- and Post-intervention (Noro)")
ggsave("noro_intevention.tiff", dpi=600, dev='tiff', height=4, width=6, units="in")
##1.4 Data pulling-----------------------------------------------------------
#Conc
matrix.Conc<-matrix(nrow=2, ncol=4)
colnames(matrix.Conc)<-c('mean', 'sd', 'min', 'max')
rownames(matrix.Conc)<-c("pre","post")
for (f in 1:2){
matrix.Conc[f,1]<-mean(Conc[f,])
matrix.Conc[f,2]<-sd(Conc[f,])
matrix.Conc[f,3]<-min(Conc[f,])
matrix.Conc[f,4]<-max(Conc[f,])
}
#Dose
matrix.Dose<-matrix(nrow=2, ncol=4)
colnames(matrix.Dose)<-c('mean', 'sd', 'min', 'max')
rownames(matrix.Dose)<-c("pre","post")
for (f in 1:2){
matrix.Dose[f,1]<-mean(Dose[f,])
matrix.Dose[f,2]<-sd(Dose[f,])
matrix.Dose[f,3]<-min(Dose[f,])
matrix.Dose[f,4]<-max(Dose[f,])
}
#Risk
matrix.Risk<-matrix(nrow=2, ncol=4)
colnames(matrix.Risk)<-c('mean', 'sd', 'min', 'max')
rownames(matrix.Risk)<-c('pre','post')
for (f in 1:2){
matrix.Risk[f,1]<-mean(Risk[f,])
matrix.Risk[f,2]<-sd(Risk[f,])
matrix.Risk[f,3]<-min(Risk[f,])
matrix.Risk[f,4]<-max(Risk[f,])
}
#Check the data
View(matrix.Conc)
View(matrix.Dose)
View(matrix.Risk)
#Pull out the data
library(openxlsx)
write.csv(matrix.Conc, file="Conc.noro.csv")
write.csv(matrix.Dose, file="Dose.noro.csv")
write.csv(matrix.Risk, file="Risk.noro.csv")
#5. Sensitivity Analysis---------------------------------------------------
spear.Noro<-data.frame(T.handarea, Frac.HS, Frac.HF, Reduc.intv,TE.all, TE.face,
Conc.i.face, Conc.i.hand, Conc.i.surface, Risk[2,])
spear.anal<-cor(spear.Noro,method="spearman")
View(spear.anal)
library(openxlsx)
write.csv (spear.anal, file="Sensitivity.noro.csv")