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state_wise_R0_estimation.R
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state_wise_R0_estimation.R
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##################################################################################
# An R script to state-wise R0 estimation for Covid-19 disease
# progression in India
#
# https://github.com/sourish-cmi/Covid19
#
# Author: Sourish Das
#
# Created: April 04, 2020
# Copyright Sourish Das, 2020
#
# Published on: 07 Apr, 2020 (Version: 1.0)
#
# The script uses the Covid-19 Databases from John-Hopkins University
# Available from: https://github.com/CSSEGISandData/COVID-19
#
# This script is not guaranteed to be free of bugs and/or errors.
#
# This script can be freely used and shared as long as the author and
# copyright information in this header remain intact.
#
# In case John-Hopkins University changes the dataprotocol
# you may have to adopt the data procesing part according to JHU
#
# The code implement the Sherry Tower's SIR implementation
#
# The code uses population_india_census2011.csv and covid_19_india.csv downloaded from Kaggle Covid-19 India database from
# https://www.kaggle.com/sudalairajkumar/covid19-in-india
#
##################################################################################
rm(list=ls())
######## R0 estimation
options(warn=-1)
library(R0)
source("Covid19/sir_func.R")
source("Covid19/R0_and_mean_shape_estimation.R")
data<-read.csv(file='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',head=TRUE)
dts1<-dts<-colnames(data)[-c(1:4)]
for(j in 1:length(dts))dts1[j]<-strsplit(dts[j],"X")[[1]][2]
dats<-as.Date(dts1,format = "%m.%d.%y")
### Population data
nms<-c("China","India","US","Iran","SouthKorea","Japan","Italy","France","Germany","Spain")
population<-c(1401754280,1359772087,329448153,83279228,51780579,126010000,60243406,67064000,83149300,47100396)
names(population)<-nms
china_data<-data[data$Country.Region=='China',]
china_rate<-apply(china_data[,-c(1:4)],2,sum)/population['China']
china_rate<-china_rate*100000
Hubei<-china_data[china_data$Province.State=='Hubei',-c(1:4)]
Hubei_incid<-diff(t(Hubei))
n<-length(Hubei_incid)
Hubei_data1<-cbind.data.frame(Date=dats[2:length(dats)],Hubei_incid=Hubei_incid)
colnames(Hubei_data1)<-c("Dates","Incidence")
Hubei_data1<-Hubei_data1[1:23,]
Hubei_population<-57237740
Hubei_fit<-R0_and_mean_shape_estimation(incidence=Hubei_data1$Incidence
,dates=Hubei_data1$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = Hubei_population
,I_0 = 444
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
Hubei_fit["mean"]
mean_hat<-Hubei_fit["mean"]
shape_hat<-Hubei_fit["shape"]
R0 <- Hubei_fit["R0"]
gamma<-1/14 ## This is gamma parameter of the SIR model
beta = R0*gamma ## This is beta parameter of SIR model
npop = Hubei_population
I_0 = 444
R_0 = 0
S_0 = npop-I_0-R_0
tbegin = 0
tend = length(Hubei_data1$Incidence)
vt = seq(tbegin,tend,1)
vparameters = c(gamma=gamma,beta=beta)
inits = c(S=S_0,I=I_0,R=R_0)
solved_model = as.data.frame(lsoda(inits, vt, derivative_calc_func, vparameters))
vI = solved_model$I
plot(Hubei_data1$Dates,Hubei_data1$Incidence,pch=20,xlab="Hubei : R0 = 2.53",ylab="Incidence")
points(Hubei_data1$Dates,vI[2:length(vI)],type="l",lwd=2,col="red")
plot(Hubei_data1$Dates,cumsum(Hubei_data1$Incidence),pch=20,xlab="Hubei : R0 = 2.53"
,ylab="Cumulative Cases")
points(Hubei_data1$Dates,cumsum(vI[2:length(vI)]),type="l",lwd=2,col="red")
#### China R0 estimation
china_data1<-cbind.data.frame(Date=dats,Cases=apply(china_data[,-c(1:4)],2,sum))
china_incid<-diff(china_data1$Cases)
n<-length(china_incid)
china_data1<-cbind.data.frame(Date=dats[2:length(dats)],China_incid=china_incid)
colnames(china_data1)<-c("Dates","Incidence")
china_data1<-china_data1[1:23,]
China_population<-1427647786
China_fit<-R0_and_mean_shape_estimation(incidence=china_data1$Incidence
,dates=china_data1$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = China_population
,I_0 = 548
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
mean_hat<-China_fit["mean"]
shape_hat<-China_fit["shape"]
R0 <- China_fit["R0"]
gamma<-1/14 ## This is gamma parameter of the SIR model
beta = R0*gamma ## This is beta parameter of SIR model
npop = China_population
I_0 = 548
R_0 = 0
S_0 = npop-I_0-R_0
tbegin = 0
tend = length(china_data1$Incidence)
vt = seq(tbegin,tend,1)
vparameters = c(gamma=gamma,beta=beta)
inits = c(S=S_0,I=I_0,R=R_0)
solved_model = as.data.frame(lsoda(inits, vt, derivative_calc_func, vparameters))
vI = solved_model$I
plot(china_data1$Dates,china_data1$Incidence,pch=20,xlab="China : R0 = 2.46",ylab="Incidence")
points(china_data1$Dates,vI[2:length(vI)],type="l",lwd=2,col="red")
plot(china_data1$Dates,cumsum(china_data1$Incidence),pch=20,xlab="China : R0 = 2.53"
,ylab="Cumulative Cases")
points(china_data1$Dates,cumsum(vI[2:length(vI)]),type="l",lwd=2,col="red")
#### India R0 estimation
India_data<-data[data$Country.Region=='India',]
India_data1<-cbind.data.frame(Date=dats,Cases=t(India_data[-c(1:4)]))
India_incid<-diff(India_data1$`132`)
n<-length(India_incid)
India_data1<-cbind.data.frame(Date=dats[2:length(dats)],Hubei_incid=India_incid)
colnames(India_data1)<-c("Dates","Incidence")
India_population<-1359772087
India_data_full<-subset(India_data1,Dates>="2020-03-02")
#India_data1<-subset(India_data1,Dates<="2020-03-24")
India_fit_full<-R0_and_mean_shape_estimation(incidence=India_data_full$Incidence
,dates=India_data_full$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = India_population
,I_0 = 5
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
India_data2<-subset(India_data1,Dates>="2020-03-02")
India_data2<-subset(India_data2,Dates<="2020-03-24")
India_fit_b4_lokdwn<-R0_and_mean_shape_estimation(incidence=India_data2$Incidence
,dates=India_data2$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = India_population
,I_0 = 5
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
#India_data2<-subset(India_data1,Dates>="2020-03-02")
#India_data2<-subset(India_data2,Dates<="2020-03-24")
India_fit_with_jan_feb<-R0_and_mean_shape_estimation(incidence=India_data1$Incidence
,dates=India_data1$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = India_population
,I_0 = 1
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
India_data2<-subset(India_data1,Dates<="2020-03-24")
India_fit_with_jan_feb_b4_lokdown<-R0_and_mean_shape_estimation(incidence=India_data2$Incidence
,dates=India_data2$Dates
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = India_population
,I_0 = 1
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
Fit_summary<-rbind(Hubei_fit
,China_fit
,India_fit_with_jan_feb_b4_lokdown
,India_fit_full
,India_fit_with_jan_feb
,India_fit_b4_lokdwn)
library(xtable)
xtable(round(Fit_summary[,c('R0','R0.lower','R0.upper')],2))
mean_hat<-India_fit["mean"]
shape_hat<-India_fit["shape"]
R0 <- India_fit["R0"]
gamma<-1/14 ## This is gamma parameter of the SIR model
beta = R0*gamma ## This is beta parameter of SIR model
npop = India_population
I_0 = 5
R_0 = 0
S_0 = npop-I_0-R_0
tbegin = 0
tend = length(India_data1$Incidence)
vt = seq(tbegin,tend,1)
vparameters = c(gamma=gamma,beta=beta)
inits = c(S=S_0,I=I_0,R=R_0)
solved_model = as.data.frame(lsoda(inits, vt, derivative_calc_func, vparameters))
vI = solved_model$I
plot(India_data1$Dates,India_data1$Incidence,pch=20,xlab="India : R0 = 2.52",ylab="Incidence")
points(India_data1$Dates,vI[2:length(vI)],type="l",lwd=2,col="red")
plot(India_data1$Dates,cumsum(India_data1$Incidence),pch=20,xlab="India : R0 = 2.52"
,ylab="Cumulative Cases")
points(India_data1$Dates,cumsum(vI[2:length(vI)]),type="l",lwd=2,col="red")
#### statewise R0 estimation
population_india<-read.csv(file="Covid19/covid19-in-india/population_india_census2011.csv",header = T,stringsAsFactors = F)
data_state<-read.csv(file="Covid19/covid19-in-india/covid_19_india.csv",header = T,stringsAsFactors = F)
data_state$State.UnionTerritory[data_state$State.UnionTerritory=='Chhattisgarh']<-'Chattisgarh'
data_state$State.UnionTerritory[data_state$State.UnionTerritory=='Pondicherry']<-'Puducherry'
data_state$Date<-as.Date(data_state$Date,format = "%d/%m/%y")
#data_state<-subset(data_state,Date>="2020-03-07")
#data_state_before<-subset(data_state,Date<="2020-03-24")
data_state<-subset(data_state,State.UnionTerritory!="Unassigned")
st_ut<-sort(unique(data_state$State.UnionTerritory))
R0_statewise<-data.frame(matrix(NA,nrow=length(st_ut),ncol=4))
colnames(R0_statewise)<-c("State/UT","R0","Lower","Upper")
for(i in 1:length(st_ut)){
#i<-24
tryCatch(
{
data_sub<-subset(data_state,State.UnionTerritory==st_ut[i])
R0_statewise[i,"State/UT"]<-st_ut[i]
### R0 Estimation
incid<-diff(data_sub$Confirmed)
incid[incid<0]<-0
n<-length(incid)
cat(st_ut[i],n,"\n")
t<-na.omit(as.character(data_sub$Date[2:nrow(data_sub)]))
state_popln<- population_india[population_india$State...Union.Territory==st_ut[i],"Population"]
rm(list = 'state_fit')
state_fit<-R0_and_mean_shape_estimation(incidence=incid
,dates=t
,N=70
,mean_min=0.1
,mean_max=10
,shape_min=0.1
,shape_max=10
,npop = state_popln
,I_0 = 1
,R_0 = 0
,gamma = 1/14 ## 14 days to recover
)
R0_statewise[i,"R0"]<-state_fit['R0']
R0_statewise[i,"Lower"]<-state_fit['R0.lower']
R0_statewise[i,"Upper"]<-state_fit['R0.upper']
}, error=function(e){}
)
Sys.sleep(10)
}
apply(R0_statewise[,2:4],2,median,na.rm=T)
final_R0_results<-list(Fit_summary=Fit_summary,R0_statewise=R0_statewise)
final_R0_results[['Fit_summary']]
final_R0_results[['R0_statewise']]
save(final_R0_results,file='Covid19/R0_results.RData')
xtable(na.omit(R0_statewise))
### Analysis for Punjab
data_sub<-subset(data_state,State.UnionTerritory=="Punjab")
plot(data_sub$Date, data_sub$Confirmed,pch=20,xlab="Punjab : R0 = 15.89",ylab = "Cases")
plot(data_sub$Date[2:nrow(data_sub)],diff(data_sub$Confirmed),pch=20,type = "b")