File:Suomen koronavirusepidemia ennuste prophet 1.svg
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[edit]DescriptionSuomen koronavirusepidemia ennuste prophet 1.svg |
Suomi: Suomen koronavirusepidemian päivittäisten tapausten ennuste. |
Date | |
Source | Own work |
Author | Merikanto |
Additional information
[edit]R code to produce this image |
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##########################################
##
## calculate forecast of covid-19
## using prophet
##
## 14.4.2022
## v 0000.0006
##
##
#
#install.packages("rvest")
#install.packages("readtext")
#install.packages("stringi")
#install.packages("datamart")
#install.packages("XML")
#install.packages("svglite")
#install.packages("ggplot2")
#install.packages("tidyr")
#install.packages("stringr")
#install.packages("stringi")
#install.packages("tibble")
Sys.setlocale("LC_ALL","Finnish")
options(encoding = "UTF-8")
library(ggplot2)
library(svglite)
library(rvest)
library(readtext)
library(stringi)
library(stringr)
library(datamart)
library(XML)
library(jsonlite)
library(rjstat)
library(tibble)
library(caTools)
library(mgcv)
library(repmis)
library(lubridate)
library(tidyverse)
library(tidyr)
library(dplyr)
library(data.table)
#library(covid19.analytics)
library(R0)
library(EpiEstim)
library(prophet)
## choices
## 1 finnish wiki data, 2 aggregated cases data
## 3 solanpaa finnish data 4 thl cube json data
#
load_data_from=3
yala=0
yyla=15000
beginday1='01/11/2021'
forecastendday1<-"2022/07/01"
## 1 from finnish wiki, 2 cases from net, 3 from net 2
plottaa=1 ## must be 1
tulosta_svg=1 # plot to out svg 0, 1 of 2
tulosfilee1="/Users/himot/akor1/prophet1.svg"
## ggolot smooth curves pars
spanni=0.2
#spanni=0.5
metodi="loess"
## NOTE date limits change this
#datelimits1=c('1/3/2020', '9/11/2020')
## display date limits
today=Sys.Date()-12
#print(today)
today1=format(today, "%d/%m/%Y")
today2=format(today, "%Y/%m/%d")
print(today1)
#print(today2)
#stop(-1)
datelimits1=c(beginday1, today1)
print(today1)
print(datelimits1)
#stop(-1)
## dates of dataset
paivat1=seq(as.Date("2020/4/1"), 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$text
etsittava1="1. huhtikuuta 2020 alkaen"
len1=nchar(texti0)
k1=regexpr(pattern=etsittava1, texti0)
k1b=len1-k1
texti1=strtail(texti0,k1b)
sink("out1.txt")
print (texti1)
sink()
etsittava2="</table>"
k2=regexpr(pattern=etsittava2, texti1)
texti2=strhead(texti1,k2)
sample1<-minimal_html(texti2)
tabu1 <- html_table(sample1, fill=TRUE)[[1]]
colnames(tabu1) <- c("V1","V2", "V3","V4", "V5","V6", "V7","V8" )
#print(tabu1)
sairaalassa00<-tabu1$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)
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(paivat1, sairaalassa)
xy<-data.frame(paivat1, uusia_tapauksia)
xyz<-data.frame(paivat1, sairaalassa, teholla)
dfout1<-data.frame(paivat1, 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
dfine <- read.csv(file = 'https://datahub.io/core/covid-19/r/countries-aggregated.csv')
#head(dfine)
#class(dfine)
#tail(dfine, 5)
dfinland <- dfine[ which(dfine$Country=='Finland'), ]
#head(dfinland)
kols <- c("Date", "Confirmed","Recovered","Deaths")
tapaukset <- dfinland[kols]
#head(tapaukset)
len1=nrow(tapaukset)
#len1
len2=len1-1
len3=len2
confirmed<-tapaukset$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$Deaths[1]
dailydeaths[1]<-0
#confirmed
#deaths
m=1
for(n in 2:(len3+1)) {
a<-confirmed[n]
b<-confirmed[m]
#print (a)
#print (b)
cee<- (a-b)
#print(cee)
dailycases[n]=cee
m=m+1
}
mm=1
for(nn in 2:(len3+1)) {
aa<-deaths[nn]
bb<-deaths[mm]
#print ("_")
#print (aa)
#print (bb)
ceb=aa-bb
#if (ceb<0) ceb=0
#print(ceb)
dailydeaths[nn]=ceb
mm=mm+1
}
#deaths
#dailycases
#dailydeaths
dfout1<-dfinland
#print(nrow(dfinland))
#print(length(dailydeaths))
dfout1 <- cbind(dfout1, data.frame(dailycases))
dfout1 <- cbind(dfout1, data.frame(dailydeaths))
#head(dfout1)
dfout2<-within(dfout1, rm(Country))
names(dfout2) <- c('Date','Confirmed','Recovered','Deaths', 'DailyConfirmed','DailyDeaths')
#head(dfout2)
write.csv2(dfout2, "/Users/himot/akor1/finland_data1.csv");
daate1<-dfout2$Date
dailydeaths1<-dfout2$DailyDeaths
dailycases1<-dailycases
#daate1
#daate2<-gsub("2020-", "", daate1)
daate2<-daate1
leenu<-length(daate2)
#alkupvm<-50
alkupvm<-1
daate3<-daate2[alkupvm:leenu]
dailydeaths3<-dailydeaths1[alkupvm:leenu]
dailycases3<-dailycases1[alkupvm:leenu]
#daate3
#dailydeaths3
# barplot(dailydeaths3, main="Koronaviruskuolemat päivittäin vuonna 2020",
# names.arg=daate3)
dataf1 <- data.frame("Date" = daate3, "Paivitt_kuolemat"=dailydeaths3)
# str(dataf1)
dataf2 <- data.frame("Date" = daate3, "Paivitt_tapaukset"=dailycases3)
# str(dataf2)
write.csv(dataf1, "/Users/himot/akor1/dailydeaths1.csv", row.names=T)
write.csv(dataf2, "/Users/himot/akor1/dailycases1.csv", row.names=T)
indf1 <- read.csv(file = '/Users/himot/akor1/dailycases1.csv')
#head(indf1)
cases1<-indf1$Paivitt_tapaukset
dates1<-indf1$Date
len1=length(cases1)
dates2<-as.Date(dates1)
paivat<-1:len1
xy<-data.frame(daate3, dailycases3)
}
calculate_r0_with_r0<-function(xy2)
{
## calculate r0 w/r0 package
dates<-as.Date(xy2$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$Cases)
nummeros<-1:length(dates)
num<-cases
#names<-nummeros
names<-dates
lenu=length(dates)
cases[is.na(cases)] <- 1
cases[(cases<0)] <- cases*-1
cases[cases==0] <- 1
# compute a MA(7)
ma1<-moving_average(cases,madays1,mean)
r0t1<-calculate_multiple_r0(ma1)
r0avg1<-moving_average(r0t1, madays2, mean)
xypaluu<-data.frame(dates,r0t1)
#plot(r0t1)
#print (r0t1)
#stop(-1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
forecast_profet<-function(xy2, futuredays)
{
## calculate r0 w/r0 package
ds<-as.Date(xy2$Dates)
y<-as.integer(xy2$Cases)
nummeros<-1:length(ds)
lenu=length(ds)
df<-data.frame(ds,y)
m <- prophet(df)
future <- make_future_dataframe(m, periods = futuredays)
forecast <- predict(m, future)
#str(future)
#str(forecast)
futu_days=future$ds
futu_trendi=forecast$trend
futu_trendi_upper=forecast$trend_upper
futu_trendi_lower=forecast$trend_lower
futu_yhat=forecast$yhat
futu_yhat_upper=forecast$yhat_upper
futu_yhat_lower=forecast$yhat_lower
futu_weekly=forecast$weekly
futu_weekly_upper=forecast$weekly_upper
futu_weekly_lower=forecast$weekly_lower
xypaluu<-data.frame(as.Date(futu_days),futu_yhat)
# xypaluu<-data.frame(as.Date(futu_days),futu_weekly)
#plot(r0t1)
#print (r0t1)
#stop(-1)
names(xypaluu)<-c("paivat","r0")
return(xypaluu)
}
lataa_thl_tapaukset_kuolleet<-function()
{
url1<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-492118&column=dateweek20200101-508804L"
cube1 <- fromJSONstat(url1, naming = "label", use_factors = F, silent = T)
res01 <- cube1[[1]]
#res00
url2<-"https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"
cube2 <- fromJSONstat(url2, naming = "label", use_factors = F, silent = T)
res02 <- cube2[[1]]
#res02
#stop (-1)
paiva=as.Date(res01$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)
}
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$new_deaths
dataf1 <- data.frame("Date" = dates, "Paivitt_kuolemat"=dailydeaths)
dataf2 <- data.frame("Date" = dates, "Paivitt_tapaukset"=dailycases)
write.csv(dataf1, "./dailydeaths1.csv", row.names=T)
write.csv(dataf2, "./dailycases1.csv", row.names=T)
xy0<-data.frame(dates, dailycases)
names(xy0)<-c("Dates", "Cases")
xy<-na.omit(xy0)
return(xy)
}
if(load_data_from==1)
{
xy<-load_data_from_finnish_wiki()
print (xy)
}
if(load_data_from==2)
{
xy<-load_data_from_aggregated()
}
if(load_data_from==3)
{
xy<-download_solanpaa_finnish_data()
}
if(load_data_from==4)
{
xy<-lataa_thl_tapaukset_kuolleet()
}
names(xy)<-c("Dates","Cases")
#print (xy)
#print (beginday1)
select_datelimit_begin=as.Date(beginday1,format="%d/%m/%Y")
select_datelimit_end=as.Date(today1, format= "%d/%m/%Y")
#format(select_datelimit_begin, "%Y-%m-%d")
#print(select_datelimit_begin)
#print(select_datelimit_end)
#2020-12-16
#xy2<-xy[xy$Dates >= select_datelimit_begin & xy$Dates <= select_datelimit_end]
#xy20<-xy[xy$Dates >= select_datelimit_begin,]
#xy2<-xy20[xy2$Dates <= select_datelimit_end, ]
#str(xy2)
# xy2 <- subset(xy, Dates > "2015-01-01" & Dates < "2015-12-31")
## xy2 <- subset(xy, Dates > select_datelimit_begin & Dates < select_datelimit_end )
xyt<-xy
setDT(xyt)
#testset[date %between% c("2013-08-02", "2013-11-01")]
#xyt2<-xyt[Dates %between% c(select_datelimit_begin,select_datelimit_end)]
#xyt2 <- subset(xyt, Dates > "2021-07-01" & Dates < "2021-12-04")
xyt20 <- subset(xyt, Dates > select_datelimit_begin )
xyt2 <- subset(xyt20, Dates < select_datelimit_end )
# xy2<-xy[xy$Dates >= select_datelimit_begin,]
print(select_datelimit_begin)
print(select_datelimit_end)
print(xyt2)
xy2<-as.data.frame(xyt2)
# stop(-1)
cases1<-xy2$Cases
dates1<-xy2$Dates
xy3<-data.frame( as.Date(dates1),as.integer(cases1) )
names(xy3)<-c("Dates", "Cases")
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)
#plot(arrat$paivat, arrat$r0)
arrat<-arrat2
#str(arrat)
#head(arrat)
sarrat1<-arrat1
sarrat2<-sarrat1
names(sarrat1)<-c("Dates","Cases")
datelimits2=c(today1, as.Date(forecastendday1,"%Y/%m/%d"))
datelimits3=c(as.Date(beginday1, "%d/%m/%Y" ), as.Date(forecastendday1,"%Y/%m/%d"))
daysek1<-seq(today, as.Date(forecastendday1), "days")
lendaysek1<-length(daysek1)
bed1<-as.Date(beginday1, "%d/%m/%Y")
#end1<-as.Date(forecastendday1)+1
end1<-as.Date(forecastendday1)-1
print(bed1)
print (end1)
# stop(-1)
daysek2<-seq(bed1, end1, "days")
daysek3<-seq(as.Date(beginday1,"%d/%m/%Y" ),as.Date(forecastendday1), "days")
###
###
basedata<-xy2
names(basedata)<-c("ds", "y")
#farrat1<-forecast_profet(xy2, lendaysek1)
profeta <- prophet(basedata,interval.width = 0.1, yearly.seasonality =0, weekly.seasonality = TRUE)
future <- make_future_dataframe(profeta, periods = lendaysek1)
forecast1 <- predict(profeta, future)
plot(profeta, forecast1)
# print(forecast1)
yhat1<-forecast1$yhat
#lower1<-yhat1-forecast1$trend_lower/2
#upper1<-yhat1+forecast1$trend_upper/2
lower1<-yhat1-forecast1$yhat_lower
upper1<-yhat1+forecast1$yhat_upper
# tail(forecast1[c(‘ds’, ‘yhat’, ‘yhat_lower’, ‘yhat_upper’)])
# head(forecast1)
# print(daysek2)
# stop(-1)
print(daysek2)
# print(yhat1)
# print(lower1)
# print(upper1)
# print(length(daysek2))
# print(length(yhat1))
farrat1<-data.frame(daysek2,yhat1,lower1, upper1)
print(farrat1)
# stop(-1)
names(farrat1)<-c("Dates", "Cases", "Lower", "Upper")
print(datelimits3)
#print(farrat1)
# stop(-1)
#plot(arrat$paivat, arrat$r0)
# plot(farrat1$paivat, farrat1$r0)
# lines(farrat1$paivat, farrat1$r0 )
# lines(sarrat2$paivat, sarrat1$r0 )
# stop(-1)
arrat<-farrat1
names(arrat)<-c("Dates","Forecast")
arrat$Forecast<-as.integer(arrat$Forecast)
marrat <- left_join(farrat1, xy3, by=c("Dates"))
names(marrat)<-c("Dates","Forecast","Lower", "Upper","Cases")
print (marrat)
#stop(-1)
if(tulosta_svg==1)
{
#svg(filename=tulosfilee1, width=6, height=3, pointsize=12)
svg(filename=tulosfilee1, width=9, height=5, pointsize=12)
}
if(plottaa==1)
{
metodi="loess"
print ("Ggplotting ...")
# ggplot(arrat, aes(x =Dates , y = Cases)) +ylim(yala, yyla)+xlim(as.Date(datelimits3, format="%d/%m/%Y") )+
ggplot(marrat, aes(x =Dates , y = Forecast)) +
#geom_ribbon(aes(x=Dates, ymin = Lower, ymax = Upper), fill = "lightblue") +
#geom_line(aes(y=Forecast), size=2, color="blue")+
#geom_line(aes(y=Lower), size=1)+
#geom_line(aes(y=Upper), size=1, color="red", linetype = "dashed")+
#annotate(geom="text", x=as.Date("2021/5/1"), y=300, size=5, label="Punainen kayra arvioitu maksimi",
# color="red")+
ylim(yala, yyla) +xlim(as.Date(datelimits3, format="%d/%m/%Y") )+
#ggtitle("Koronavirustapauksia /pv ennuste") +
labs(title = "Ennuste: koronavirustapauksia Suomessa",
subtitle = "jos muutos jatkuu samaa vauhtia",
caption = "")+
xlab("Kuukausi") + ylab("Koronatapauksia")+
theme(title=element_text(size=17), axis.text=element_text(size=14,face="bold"),axis.title=element_text(size=15,face="bold"))+
#geom_point() +
geom_smooth( fill="#a0a0ff",span=spanni, method=metodi, level=0.99999, size=3)+
geom_smooth( fill="#9090ff", span=spanni,method=metodi, level=0.97) +
geom_smooth( fill="#8a08af", span=spanni, method=metodi,level=0.8) +
geom_point(aes(y=Cases), size=3)
#geom_hline(yintercept=1.0, linetype="dashed", color = "red", size=1)
}
if(tulosta_svg==1)
{
dev.off()
}
|
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[edit]I, the copyright holder of this work, hereby publish it under the following license:
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- Under the following conditions:
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