File:R0 Suomessa kevat 2022 1.svg
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Summary
[edit]DescriptionR0 Suomessa kevat 2022 1.svg |
Suomi: Koronaviruksen tarttuttavuusluku R0 Suomessa syksyllä 2020. Eksponenttikaavalla laskettu perusuusiutumisluku. Oletukset: tauti itää 5 vuorokautta, ei karanteeneja. |
Date | |
Source | Own work |
Author | Merikanto |
- Calculate r0 of Covid-19 in Finland
- "R" script
- 23.7.2022
- v 0000.0013
new_in_skript=0
if (new_in_skript==1)
{
#install.packages("ggplot2", "plotly", repos ="https://ftp.acc.umu.se/mirror/CRAN/")
install.packages("svglite")
install.packages("ggplot2")
install.packages("rvest")
install.packages("readtext")
install.packages("stringi")
install.packages("datamart")
install.packages("XML")
install.packages("tidyr")
install.packages("stringr")
install.packages("stringi")
install.packages("tibble")
install.packages("readr")
install.packages("data.table")
install.packages("caTools")
install.packages("mgcv")
install.packages("repmis")
install.packages("lubridate")
install.packages("tidyverse")
install.packages("R0")
install.packages("EpiEstim")
install.packages("jsonlite")
install.packages("rjstat")
}
library(ggplot2)
library(svglite)
library(rvest)
library(readtext)
library(stringi)
library(stringr)
- library(datamart)
library(XML)
library(jsonlite)
library(tibble)
library(caTools)
library(mgcv)
library(repmis)
library(lubridate)
library(tidyverse)
library(tidyr)
library(readr)
library(data.table)
library(rjstat)
- library (R0)
library(EpiEstim)
- choices
- 1 finnish wiki data !!! NOT UPDATED
- 2 aggregated cases data OK
- 3 solanpaa finnish data OK
- 4 thl cube json data NOK
- 5 thl cube json hospital data OK 15.4.2022
load_data_from=3
- beginday1='28/02/2020'
beginday1='1/1/2022'
- beginday1='1/1/2021'
- today=Sys.Date()-1
- note lag in july data !
today=Sys.Date()-14
spanni=0.1
- spanni=0.3
- yala=0.6
- yyla=2.0
yala=0.9
yyla=1.35
metodi="loess"
widthi=10
heighti=5
plottaa=1 ## must be 1
tulosta_svg=1 # plot to out svg 0, 1 of 2
tulosfilee1="./R0_Suomessa_2.svg"
beginday0=as.Date(beginday1)
beginday2=format(beginday0, "%Y/%m/%d")
today1=format(today, "%d/%m/%Y")
today2=format(today, "%Y/%m/%d")
print(today1)
datelimits1=c(beginday1, today1)
paivat1=seq(as.Date(beginday2), as.Date(today2), "days")
calculate_r0 <- function(time1, time2, val1, val2)
{
td=time2-time1
gr0<-log(val2/val1)
gr=gr0/td
td = log(2)/gr
tau<-5.0
k<-log(2.0)/td
r0<-exp(k*tau)
return(r0)
}
moving_average <- function(x, w, FUN, ...)
{
if (w < 1) {
stop("Window length: mustbe greater than 0")
}
output <- x
for (i in 1:length(x)) {
lower_bound <- i - w + 1
if (lower_bound < 1) {
output[i] <- NA_real_
## !!! assume NA 0
output[i] <- 0
} else {
output[i] <- FUN(x[lower_bound:i, ...])
}
}
return (output)
}
calculate_multiple_r0 <- function(daata1) {
lenu1<-length(daata1)
daata2<-1:lenu1
for (n in 2:lenu1){
valju1=daata1[n-1]
valju2=daata1[n]
timex1=0
timex2=1
r0<-calculate_r0(0, 1, valju1, valju2)
daata2[n]<-r0
#print (r0)
}
return(daata2)
}
load_data_from_finnish_wiki<-function()
{
url1="https://fi.wikipedia.org/wiki/Suomen_koronaviruspandemian_aikajana"
destfile1="./ward0.txt"
download.file(url1, destfile1)
texti000<-readtext(destfile1)
texti0<-texti000$text
etsittava1="1. huhtikuuta 2020 alkaen"
len1=nchar(texti0)
k1=regexpr(pattern=etsittava1, texti0)
k1b=len1-k1
texti1=strtail(texti0,k1b)
sink("out1.txt")
print (texti1)
sink()
etsittava2=""
k2=regexpr(pattern=etsittava2, texti1)
texti2=strhead(texti1,k2)
sample1<-minimal_html(texti2)
tabu1 <- html_table(sample1, fill=TRUE)1
colnames(tabu1) <- c("V1","V2", "V3","V4", "V5","V6", "V7","V8" )
- print(tabu1)
sairaalassa00<-tabu1$V4
sairaalassa=as.integer(sairaalassa00)
teholla00<-tabu1$V5
teholla=as.integer(teholla00)
uusiatapauksia00<-tabu1$V3
uusiatapauksia0<-gsub(" ", "", uusiatapauksia00)
uusia_tapauksia=as.integer(uusiatapauksia0)
uusiakuolleita00<-tabu1$V7
uusiakuolleita1=as.integer(uusiakuolleita00)
uusiakuolleita2<-uusiakuolleita1
uusiakuolleita2[uusiakuolleita2<0]<-0
uusia_kuolleita<-uusiakuolleita2
toipuneita00<-tabu1$V8
toipuneita01<-gsub(" ", "", toipuneita00)
toipuneita0<-gsub("[^0-9.-]", "", toipuneita01)
toipuneita=as.integer(toipuneita0)
tapauksia00<-tabu1$V2
tapauksia01<-gsub(" ", "", tapauksia00)
tapauksia0<-gsub("[^0-9.-]", "", tapauksia01)
tapauksia=as.integer(tapauksia0)
kuolleita00<-tabu1$V6
kuolleita=as.integer(kuolleita00)
pv0<-tabu1$V1
len1=length(pv0)
daates1 <- vector(mode="character", length=len1)
- print(pv0)
n=1
for(n in 1:len1)
{
it1<-pv0[n]
#print(it1)
qq1<-str_split(it1, "\\[")1
qq2<-qq1[1]
qq3<-gsub(" ", "", qq2, fixed = TRUE)
daates1[n]=qq3
}
daates2=as.Date(daates1, format="%d.%m.%Y")
print(daates2)
aktiivisia_tapauksia=tapauksia-kuolleita-toipuneita
- print (paivat1)
- print (teholla)
- print (sairaalassa)
- print (tapauksia)
- print (kuolleita)
- print (toipuneita)
- print (uusia_tapauksia)
- print (uusia_kuolleita)
- plot(paivat1,aktiivisia_tapauksia)
- xy<-data.frame(daates2, sairaalassa)
xy<-data.frame(daates2, uusia_tapauksia)
names(xy)<-c("Dates", "Cases")
xyz<-data.frame(daates2, sairaalassa, teholla)
dfout1<-data.frame(daates2, aktiivisia_tapauksia, uusia_tapauksia, sairaalassa, teholla, uusia_kuolleita )
names(dfout1)<-c("Pvm", "Aktiivisia_tapauksia","Uusia_tapauksia", "Sairaalassa", "Teholla", "Uusia_kuolleita")
write.csv2(dfout1, "./sairaalassa.csv",row.names=FALSE )
return(xy)
}
load_data_from_aggregated<-function()
{
- fetch the data
print("Aggreg")
srkurl='https://raw.githubusercontent.com/datasets/covid-19/main/data/countries-aggregated.csv'
dfine <- read.csv(file=srkurl)
- print(dfine000)
- str(dfine000)
- head(dfine000,20)
- stop(-1)
- dfine<-as.data.frame(dfine000)
- head(dfine)
- class(dfine)
- print(dfine)
- tail(dfine)
- stop(-1)
dfinland <- dfine[ which(dfine$Country=='Finland'), ]
head(dfinland)
- print(dfinland)
- stop(-1)
kols <- c("Date", "Confirmed","Recovered","Deaths")
tapaukset <- dfinland[kols]
- head(tapaukset)
len1=nrow(tapaukset)
- len1
len2=len1-1
len3=len2
confirmed<-tapaukset$Confirmed
deaths<-tapaukset$Deaths
dailycases <- vector()
dailycases <- c(dailycases, 0:(len2))
dailydeaths <- vector()
dailydeaths <- c(dailydeaths, 0:(len2))
m=0
dailycases[1]<-tapaukset$Confirmed[1]
- dailydeaths[1]<-tapaukset$Deaths[1]
dailydeaths[1]<-0
- confirmed
- deaths
m=1
for(n in 2:(len3+1)) {
a<-confirmed[n]
b<-confirmed[m]
#print (a)
#print (b)
cee<- (a-b)
#print(cee)
dailycases[n]=cee
m=m+1
}
mm=1
for(nn in 2:(len3+1)) {
aa<-deaths[nn]
bb<-deaths[mm]
#print ("_")
#print (aa)
#print (bb)
ceb=aa-bb
#if (ceb<0) ceb=0
#print(ceb)
dailydeaths[nn]=ceb
mm=mm+1
}
- deaths
- dailycases
- dailydeaths
dfout1<-dfinland
- print(nrow(dfinland))
- print(length(dailydeaths))
dfout1 <- cbind(dfout1, data.frame(dailycases))
dfout1 <- cbind(dfout1, data.frame(dailydeaths))
- head(dfout1)
dfout2<-within(dfout1, rm(Country))
names(dfout2) <- c('Date','Confirmed','Recovered','Deaths', 'DailyConfirmed','DailyDeaths')
datez1<-dfout2['Date']
dailyz1<-dfout2['DailyConfirmed']
deathz1<-dfout2['DailyDeaths']
dfout3<-cbind(datez1, dailyz1)
return(dfout3)
- head(dfout2)
write.csv2(dfout2, "/Users/himot/akor1/finland_data1.csv");
daate1<-dfout2$Date
dailydeaths1<-dfout2$DailyDeaths
dailycases1<-dailycases
- daate1
- daate2<-gsub("2020-", "", daate1)
daate2<-daate1
leenu<-length(daate2)
- alkupvm<-50
alkupvm<-1
daate3<-daate2[alkupvm:leenu]
dailydeaths3<-dailydeaths1[alkupvm:leenu]
dailycases3<-dailycases1[alkupvm:leenu]
- daate3
- dailydeaths3
pv0<-tabu1$V1
len1=length(pv0)
daates1 <- vector(mode="character", length=len1)
- print(pv0)
n=1
for(n in 1:len1)
{
it1<-pv0[n]
#print(it1)
qq1<-str_split(it1, "\\[")1
qq2<-qq1[1]
qq3<-gsub(" ", "", qq2, fixed = TRUE)
daates1[n]=qq3
}
daates2=as.Date(daates1, format="%d.%m.%Y")
print(daates2)
# barplot(dailydeaths3, main="Koronaviruskuolemat päivittäin vuonna 2020",
# names.arg=daate3)
dataf1 <- data.frame("Date" = daates2, "Paivitt_kuolemat"=dailydeaths3)
- str(dataf1)
dataf2 <- data.frame("Date" = daates2, "Paivitt_tapaukset"=dailycases3)
- str(dataf2)
write.csv(dataf1, "/Users/himot/akor1/dailydeaths1.csv", row.names=T)
write.csv(dataf2, "/Users/himot/akor1/dailycases1.csv", row.names=T)
indf1 <- read.csv(file = '/Users/himot/akor1/dailycases1.csv')
#head(indf1)
cases1<-indf1$Paivitt_tapaukset
dates1<-indf1$Date
len1=length(cases1)
dates2<-as.Date(dates1)
paivat<-1:len1
xy<-data.frame(daates2, dailycases3)
return(xy)
}
download_solanpaa_finnish_data<-function()
{
solanpaa_fi="https://covid19.solanpaa.fi/data/fin_cases.json"
cache_file="solanpaa_fi.json"
download.file(solanpaa_fi, cache_file)
j1 <- fromJSON(cache_file)
## maybe errori
dates<-as.Date(j1$date)
dailycases<-j1$new_cases
dailydeaths<-j1$new_deaths
dataf1 <- data.frame("Date" = dates, "Paivitt_kuolemat"=dailydeaths)
dataf2 <- data.frame("Date" = dates, "Paivitt_tapaukset"=dailycases)
write.csv(dataf1, "./dailydeaths1.csv", row.names=T)
write.csv(dataf2, "./dailycases1.csv", row.names=T)
xy0<-data.frame(dates, dailycases)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
return(xy)
}
calculate_r0_with_r0<-function(xy2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
bekini=as.Date(dates[1])
enti=as.Date(dates[lenu])
#print(bekini)
#print(enti)
#stop(-1)
#enti=lenu
#bekini=enti*0+1
#enti=as.integer(enti)
#bekini=as.integer(bekini)
df1 <- setNames(num, names)
mGT<-generation.time("gamma", c(3, 1.5))
#TD <- est.R0.TD(df1, mGT, begin=1, end=length(dates), nsim=200)
#TD <- est.R0.TD(df1, mGT, begin=bekini, end=enti, nsim=200)
TD <- est.R0.TD(df1, mGT, begin=bekini, end=enti, nsim=200)
TD.5D <- smooth.Rt(TD, 5)
paivat1<-TD.5D$epid$t
paivat2<-as.Date(paivat1)
r0t1<-TD.5D$R
conf1<-TD.5D$conf.int
xypaluu<-data.frame(paivat1,r0t1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
calculate_r0_with_epiestim<-function(xy2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
incid<-cases
bekini=as.Date(dates[1])
enti=as.Date(dates[lenu])
config<-make_config( list(mean_si = 2.6,std_si = 1.5) )
res<-estimate_R(incid,method="parametric_si", config = config)
plot(res)
resr<-res$R
str(resr)
meanr<-resr$Mean
medianr<-resr$Median
quantile95<-resr$Quantile.0.95
quantile05<-resr$Quantile.0.05
quantile75<-resr$Quantile.0.75
quantile25<-resr$Quantile.0.25
meanr
daydexes<-resr$t_start
daydexes
plot(daydexes, meanr)
dayss<-as.Date(dates[daydexes])
print (dayss)
#stop(-1)
plot(dayss, meanr)
xypaluu<-data.frame(dayss,meanr)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
calculate_r0_with_simple_exponent_moving_average<-function(xy2, madays1, madays2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$Dates)
cases<-as.integer(xy2$Cases)
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
# compute a MA(7)
ma1<-moving_average(cases,madays1,mean)
r0t1<-calculate_multiple_r0(ma1)
r0avg1<-moving_average(r0t1, madays2, mean)
xypaluu<-data.frame(dates,r0t1)
plot(r0t1)
print (r0t1)
#stop(-1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
lataa_thl_tapaukset_kuolleet<-function()
{
## oriko
##url1<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-492118&column=dateweek20200101-508804L"
# viikoittain
url1<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=hcdmunicipality2020-445222.&column=dateweek20200101-509030&filter=measure-444833"
cube1 <- fromJSONstat(url1, naming = "label", use_factors = F, silent = T)
head(cube1)
res01 <- cube11
head(res01,40)
print("Pazka")
stop(-1)
#res00
url2<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"
cube2 <- fromJSONstat(url2, naming = "label", use_factors = F, silent = T)
res02 <- cube21
#res02
#stop (-1)
paiva=as.Date(res01$dateweek20200101)
kuolleet=as.integer(res01$value)
tapaukset=as.integer(res02$value)
kuolin_prosentit=kuolleet/tapaukset
kuolin_prosentit=kuolin_prosentit*10000
kuolin_prosentit=as.integer(kuolin_prosentit)
kuolin_prosentit=as.double(kuolin_prosentit)
kuolin_prosentit=kuolin_prosentit/100.0
#print (paiva)
#print (kuolleet)
#stop(-1)
#print (tapaukset)
#print (kuolin_prosentit )
df1<-data.frame(paiva,tapaukset, kuolleet, kuolin_prosentit)
names(df1)<-c("Paiva", "Tapauksia", "Kuolleita", "Kuolinprosentti")
#write.csv2(df1, "./kuolleet_ikaryhmittain.csv", sep = ";" )
write.csv(df1, "./thl_tapaukset_kuolleet.csv")
xy0<-data.frame(paiva, tapaukset)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
#return(df1)
}
nth_element <- function(vector, starting_position, n) {
vector[seq(starting_position, length(vector), n)]
}
get_thl_hospital_data<-function()
{
url_base2="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19care/fact_epirapo_covid19care.json"
request2 <- "?row=dateweek20200101-509093L&column=measure-547523.547516.547531.456732.&fo=1"
url2 <- paste0(url_base2, request2)
cube2 <- fromJSONstat(url2, naming = "label", use_factors = F, silent = T)
- print (cube2)
- head(cube2, 40)
- stop(-1)
res02 <- cube21
head(res02, 40)
daates00<-res021
- print(daates00)
days0<-as.Date(daates00)
days1<-nth_element(days0, 1, 4)
- print(days1)
gecko1<-as.integer(res023)
- print(head(gecko1,40))
gecko2<-matrix(gecko1,nrow=4)
- print (head(gecko2,20))
sairaalassa1=gecko2[1,]+gecko2[2,]+gecko2[4,]
teholla1=gecko2[4,]
sairaalassa1[is.na(sairaalassa1)]<-0
teholla1[is.na(teholla1)] <- 0
print (head(sairaalassa1,50))
- print (teholla1)
df1<-data.frame(days1, sairaalassa1, teholla1)
names(df1)<-c("Paiva", "Sairaalassa", "Teholla")
df0<-data.frame(days1, sairaalassa1)
names(df0)<-c("Paiva", "Sairaalassa")
write.csv(df0, "./thl_sairaalassa.csv")
xy0<-data.frame(days1, sairaalassa1)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
return(xy)
- return(df0)
}
- main program
if(load_data_from==1)
{
xy<-load_data_from_finnish_wiki()
print (xy)
}
if(load_data_from==2)
{
xy<-load_data_from_aggregated()
names(xy)<-c("Dates","Cases")
print("Aggreg data")
##stop(-1)
}
if(load_data_from==3)
{
xy<-download_solanpaa_finnish_data()
}
if(load_data_from==4)
{
xy<-lataa_thl_tapaukset_kuolleet()
}
if(load_data_from==5)
{
xy<-get_thl_hospital_data()
names(xy)<-c("Dates","Cases")
print("From THL hospital data")
#print(xy)
#head(xy)
#stop(-1)
}
- print (xy)
## quit(-1)
#print (beginday1)
select_datelimit_begin=as.Date(beginday1,format="%d/%m/%Y")
select_datelimit_end=as.Date(today1, format="%d/%m/%Y")
#format(select_datelimit_begin, "%Y-%m-%d")
print(select_datelimit_begin)
print(select_datelimit_end)
#2020-12-16
xy2<-xy[xy$Dates >= select_datelimit_begin & xy$Dates <= select_datelimit_end,]
#xy2<-xy[xy$Dates >= select_datelimit_begin,]
print("xy2")
print(xy2)
#stop(-1)
cases1<-xy2$Cases
dates1<-xy2$Dates
len1=length(cases1)
dates2<-as.Date(dates1)
paivat<-1:len1
## test code
arrat0<-calculate_r0_with_simple_exponent_moving_average(xy2, 14,7)
#arrat1<-calculate_r0_with_r0(xy2)
#arrat2<-calculate_r0_with_epiestim(xy2)
#print("calcu ok")
#plot(arrat$paivat, arrat$r0)
# arrat<-arrat2
arrat<-arrat0
str(arrat)
head(arrat)
#plot(arrat$paivat, arrat$r0)
- stop(-1)
if(tulosta_svg==1)
{
#svg(filename=tulosfilee1, width=8, height=3, pointsize=12)
svg(filename=tulosfilee1, width=widthi, height=heighti, pointsize=12)
}
if(plottaa==1)
{
metodi="loess"
print ("ggplot")
#ggplot(arrat, aes(x =paivat , y = r0)) +ylim(0.6, 1.8)+xlim(as.Date(datelimits1, format="%d/%m/%Y") )+
ggplot(arrat, aes(x =paivat , y = r0)) +ylim(yala, yyla)+xlim(as.Date(datelimits1, format="%d/%m/%Y") )+
ggtitle("Arvioitu koronaviruksen perusuusiutumisluku R0") +
xlab("Kuukausi") + ylab("R0")+
theme(title=element_text(size=15), axis.text=element_text(size=12,face="bold"),axis.title=element_text(size=14,face="bold"))+
geom_point() +
geom_smooth( fill="#a0a0ff",span=spanni, method=metodi, level=0.99, size=3)+
geom_smooth( fill="#9090ff", span=spanni,method=metodi, level=0.7) +
geom_smooth( fill="#8a08af", span=spanni, method=metodi,level=0.5) +
geom_hline(yintercept=1.0, linetype="dashed", color = "red", size=1)
}
if(tulosta_svg==1)
{
dev.off()
}
Licensing
[edit]- You are free:
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- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
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current | 16:06, 23 July 2022 | 900 × 450 (104 KB) | Merikanto (talk | contribs) | Update | |
13:05, 15 April 2022 | 900 × 450 (170 KB) | Merikanto (talk | contribs) | update | ||
06:35, 14 April 2022 | 900 × 450 (161 KB) | Merikanto (talk | contribs) | Update | ||
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07:13, 25 October 2021 | 900 × 450 (117 KB) | Merikanto (talk | contribs) | Update | ||
05:03, 6 October 2021 | 900 × 450 (110 KB) | Merikanto (talk | contribs) | update |
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Height | 360pt |