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@EconometricsBySimulation EconometricsBySimulation/ebola.R
Last active Aug 29, 2015

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Analysis of Ebola data
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ebola.R
# First let's load some libraries
require('XML'); require('reshape2'); require('ggplot2')
require('magrittr') # See
# http://goo.gl/Wj5F87
# I have borrowed Andrie's code from stackoverflow
# http://goo.gl/noYVo7
source("http://goo.gl/w64gfp")
# Load in the google spreadsheet
# http://goo.gl/tObo1r
u <- "http://goo.gl/QkXCo0"
ebola <- readGoogleSheet(u) %>% cleanGoogleTable(table=1) %>% as.data.frame
ebola$Date <- as.Date(ebola$Date, format="%m/%d/%Y")
ebola$Day <- ebola$date-min(ebola$date)
# Force columns to be text
ebola[,2:ncol(ebola)] <- sapply(ebola[,2:ncol(ebola)], as.numeric)
# Create totals across countries
# Total Suspected
ebola$totalSus <- with(ebola, GuinSus+LibSus+NigSus+SLSus)
# Total Deaths
ebola$totalDeath <- with(ebola, GuinDeath+LibDeath+NigDeath+SLDeath)
# Total Laboritory Confirmed Cases
ebola$totalLab <- with(ebola, GuinLab+LibLab+NigLab+SLLab)
ebola.long <- melt(ebola, id.vars=c('Date','Day'))
# Create a variable for country
ebola.long$Country <- ''
ebola.long$Country[grepl("Guin",ebola.long$variable)] <- "Guinea"
ebola.long$Country[grepl("Lib",ebola.long$variable)] <- "Liberia"
ebola.long$Country[grepl("Nig",ebola.long$variable)] <- "Nigeria"
ebola.long$Country[grepl("SL",ebola.long$variable)] <- "Sierra Leone"
# Specify the type
ebola.long$type <- "Suspected Infected"
ebola.long$type[grepl("Death",ebola.long$variable)] <- "Death"
ebola.long$type[grepl("Lab",ebola.long$variable) ] <-
"Laboratory Confirmed"
ebola.long$type[grepl("total",ebola.long$variable)] <- "Total"
png(filename='2014-09-06Suspected.png', width=700, height=400)
ggplot(subset(ebola.long, type=="Suspected Infected"),
aes(y=value, x=Date,
group=Country, color=Country, shape=Country)) +
geom_smooth(method=loess)+geom_point(size=3)+
scale_x_date(name="Month") +
scale_y_continuous(name="Number of Cases") +
ggtitle('Suspected Number of Cases by Country')
dev.off()
png(filename='2014-09-06Death.png', width=700, height=400)
ggplot(subset(ebola.long, type=="Death"),
aes(y=value, x=Date,
group=Country, color=Country, shape=Country)) +
geom_smooth(method=loess)+geom_point(size=3)+
scale_x_date(name="Month") +
scale_y_continuous(name="Number of Deaths") +
ggtitle('Deaths by Country')
dev.off()
png(filename='2014-09-06Lab.png', width=700, height=400)
ggplot(subset(ebola.long, type=="Laboratory Confirmed"),
aes(y=value, x=Date,
group=Country, color=Country, shape=Country)) +
geom_smooth(method=loess)+geom_point(size=3)+
scale_x_date(name="Month") +
scale_y_continuous(name="Number of Cases") +
ggtitle('Number of Laboratory Confirmed Cases by Country')
dev.off()
png(filename='2014-09-06Total1.png', width=700, height=400)
ggplot(subset(ebola.long, type=="Total"),
aes(y=value, x=Date,
group=variable, color=variable, shape=variable)) +
geom_smooth(method=loess)+geom_point(size=3)+
scale_x_date(name="Month") +
scale_y_continuous(name="Totals") +
scale_colour_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab' ),
labels=c("Suspected Cases", "Deaths", "Lab Confirm")) +
scale_shape_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab' ),
labels=c("Suspected Cases", "Deaths", "Lab Confirm")) +
ggtitle('Total Number of Cases Across Countries')
dev.off()
# Log transformation
ebola.long$logvalue <- log(ebola.long$value)
png(filename='2014-09-06TotalLog.png', width=700, height=400)
ggplot(subset(ebola.long, type=="Total" & Day>25),
aes(y=logvalue, x=Day, group=variable,
color=variable, shape=variable)) +
geom_smooth(method=loess)+geom_point(size=3) +
scale_x_continuous(name="Days since Discovery") +
scale_y_continuous(name="Totals in Natural Log") +
scale_colour_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab' ),
labels=c("Suspected Cases", "Deaths", "Lab Confirm")) +
scale_shape_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab' ),
labels=c("Suspected Cases", "Deaths", "Lab Confirm")) +
ggtitle('Total Across Countries')
dev.off()
LSratio <- with(ebola, data.frame(
Day=Day,
Guinea=GuinLab/GuinSus,
Liberia=LibLab/LibSus,
Nigeria=NigLab/NigSus,
SL=SLLab/SLSus),
)
LS.long <- melt(LSratio, id.vars='Day')
LS.long <- LS.long[!is.na(LS.long$value),]
ggplot(LS.long,
aes(y=value, x=Day,
group=variable, color=variable, shape=variable)) +
geom_line() + geom_point(size=3) +
scale_x_continuous(name="Days since Discovery") +
scale_y_continuous(name="Number of Cases") +
ggtitle('Number of Laboratory Confirmed Cases by Country')
(Days <- max(ebola$Day))
ebola$Day2 <- ebola$Day^2
ebolasub <- subset(ebola,Day>21)
lm(log(totalSus)~Day, weight=Day, data=ebolasub)$coefficients
lm(log(totalDeath)~Day,weight=Day, data=ebolasub)$coefficients
lm(log(totalLab)~Day, weight=Day, data=ebolasub)$coefficients
ebola$Day2 <- ebola$Day^2
ebolasub <- subset(ebola,Day>21)
shat=lm(log(totalSus)~Day+Day2, weight=Day, data=ebolasub)$coefficients
dhat=lm(log(totalDeath)~Day+Day2,weight=Day, data=ebolasub)$coefficients
lhat=lm(log(totalLab)~Day+Day2, weight=Day, data=ebolasub)$coefficients
projMax <- 28
projDay <- 1:(Days+projMax)
ebolahat <- data.frame(Day=projDay,
Esus = (shat[1]+shat[2]*projDay+shat[3]*projDay^2) %>% exp %>% round,
Edea = (dhat[1]+dhat[2]*projDay+dhat[3]*projDay^2) %>% exp %>% round,
Elab = (lhat[1]+lhat[2]*projDay+lhat[3]*projDay^2) %>% exp %>% round)
ebolahat$totalSus<-ebolahat$totalDeath<-ebolahat$totalLab<-''
ebolahat$totalSus[ebola$Day+1] <- ebola$totalSus
ebolahat$totalDeath[ebola$Day+1] <- ebola$totalDeath
ebolahat$totalLab[ebola$Day+1] <- ebola$totalLab
ebolahat$date <- ebola$Date[1]+projDay-1
ebolahat[c('date','Day','totalDeath', 'Edea', 'totalSus', 'Esus', 'totalLab', 'Elab')]
ebolahat.long <- melt(ebolahat, id.vars=c('date','Day'))
ebolahat.long <- ebolahat.long[!is.na(ebolahat.long$value),]
png(filename='2014-09-06TotalProj.png', width=700, height=400)
ggplot(subset(ebolahat.long,Day>50) ,
aes(y=value, x=date, group=variable,
color=variable, shape=variable)) +
geom_line(data=ebolahat.long[ebolahat.long$variable %in% c('Esus','Edea','Elab'),], size=3, alpha=.4)+
geom_point(data=ebolahat.long[ebolahat.long$variable %in% c("totalSus", "totalDeath", 'totalLab'),], size=3) +
scale_x_date(name="Days since Discovery") +
scale_y_continuous(name="Total Number") +
scale_colour_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab',
'Esus','Edea','Elab'),
labels=c("Suspected Cases", "Deaths", "Lab Confirm",
"E Suspected", "E Deaths", "E Lab")) +
scale_shape_discrete(name ="Value",
breaks=c("totalSus", "totalDeath", 'totalLab',
'Esus','Edea','Elab'),
labels=c("Suspected Cases", "Deaths", "Lab Confirm",
"E Suspected", "E Deaths", "E Lab")) +
ggtitle('Total Cases in West Africa')
dev.off()
# Six months out
projMax <- 360
projDay <- 1:(Days+projMax)
ebolahat <- data.frame(Day=projDay,
Esus = (shat[1]+shat[2]*projDay+shat[3]*projDay^2) %>% exp %>% round,
Edea = (dhat[1]+dhat[2]*projDay+dhat[3]*projDay^2) %>% exp %>% round,
Elab = (lhat[1]+lhat[2]*projDay+lhat[3]*projDay^2) %>% exp %>% round)
ebolahat$totalSus<-ebolahat$totalDeath<-ebolahat$totalLab<-NA
ebolahat$totalSus[ebola$Day+1] <- ebola$totalSus
ebolahat$totalDeath[ebola$Day+1] <- ebola$totalDeath
ebolahat$totalLab[ebola$Day+1] <- ebola$totalLab
ebolahat$date <- ebola$date[1]+1:(Days+projMax)-1
ebolahat[nrow(ebolahat),]
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