File:Koronaviruksen R0 Suomessa 6.svg

1. 3. calculaters COVID-19 R0 using simple exponential estimation formula
   4. and smoothed data
   6. COVID-19 statistics from aggregated data from net site
   7. with Python
   8. Input from internet site: cases, recovered, deats.
   11. 11.01.2021
   12. 0001.0001

paiva1="2020-02-28" paiva2="2021-02-22"

1. ymax1=1000
2. ymax2=30

import math as math import numpy as np import scipy as sp import pandas as pd import matplotlib.pyplot as plt

import matplotlib.ticker as ticker

from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator)

import datetime as dt import matplotlib.pyplot as plt import matplotlib.dates as mdates

from scipy.signal import savgol_filter from bs4 import BeautifulSoup import requests

1. 1. very basic exponential r0 calculation

def calculate_r0(time1, time2, val1, val2): k=0 ##td=time2-time1 ## #optim td=1 #print("td") #print (td) #print(val1, val2) gr0=math.log(val2/val1) gr=gr0/td #print (val2, val1) #print(gr) if(gr!=0): td= math.log(2.0)/gr else: return(1)

tau=5.0

k=math.log(2.0)/td

r0=math.exp(k*tau) if(r0==32): r0=1

if(r0>32): r0=4

return(r0)

def calculate_multiple_r0(daata1): lenu1=len(daata1) print (lenu1) #daata2=list(range(1,lenu1,1)) daata2 = [0] * lenu1 for n in range(1,lenu1-2,1): valju1=daata1[n-1] valju2=daata1[n] if(math.isnan(valju1)): valju1=1 if(math.isnan(valju2)): valju2=1 if(valju1==0): valju1=1 if(valju2==0): valju2=1 timex1=0 timex2=1 r0=calculate_r0(0, 1, valju1, valju2) daata2[n]=r0 print (n, r0)

return(daata2)

def format_func(value, tick_number):

   N = int(np.round(value/10))
   if N == 0:
       return "0"
   else:
       return r"${0}\pv$".format(N)
       
       

def smooth(x,window_len=14,window='hanning'):

if x.ndim != 1: return(-1)

if x.size < window_len: return(-1)

if window_len<3: return (x)

if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: return(-1)

s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]

if window == 'flat': w=np.ones(window_len,'d') else: w=eval('np.'+window+'(window_len)')

y=np.convolve(w/w.sum(),s,mode='valid')

return y

def cut_by_dates(dfx, start_date, end_date): mask = (dfx['Date'] >= start_date) & (dfx['Date'] <= end_date) dfx2 = dfx.loc[mask] #print(dfx2) return(dfx2)

def load_country_cases(maa): dfin = pd.read_csv('https://datahub.io/core/covid-19/r/countries-aggregated.csv', parse_dates=['Date']) countries = [maa] dfin = dfin[dfin['Country'].isin(countries)] #print (head(dfin)) #quit(-1) selected_columns = dfin"Date", "Confirmed", "Recovered", "Deaths" df2 = selected_columns.copy()

df=df2 len1=len(df["Date"]) aktiv2= [None] * len1 for n in range(0,len1-1): aktiv2[n]=0

dates=df['Date'] rekov1=df['Recovered'] konf1=df['Confirmed'] death1=df['Deaths'] #print(dates) spanni=6

#print(rekov1)

#quit(-1)

rulla = rekov1.rolling(window=spanni).mean() rulla2 = rulla.rolling(window=spanni).mean()

tulosrulla=rulla2 tulosrulla= tulosrulla.replace(np.nan, 0) tulosrulla=np.array(tulosrulla).astype(int) rulla2=tulosrulla

x=np.linspace(0,len1,len1); #print("kupla") #print(tulosrulla)

#print(konf1) #print(death1) #print(aktiv2) konf1=np.array(konf1).astype(int) death1=np.array(death1).astype(int) #print(konf1) #quit(-1)

for n in range(0,(len1-1)): #print("luzmu") rulla2[n]=tulosrulla[n] #print ("luzmu2") #aktiv2[n]=konf1[n]-death1[n]-rulla2[n] aktiv2[n]=konf1[n] #print(rulla2[n])

#quit(-1)

#aktiv3=np.array(aktiv2).astype(int)

dailycases1= [0] * len1 dailydeaths1= [0] * len1

for n in range(1,(len1-1)): dailycases1[n]=konf1[n]-konf1[n-1] if (dailycases1[n]<0): dailycases1[n]=0

for n in range(1,(len1-1)): dailydeaths1[n]=death1[n]-death1[n-1] if (dailydeaths1[n]<0): dailydeaths1[n]=0

#quit(-1) df.insert (2, "Daily_Cases", dailycases1) df.insert (3, "Daily_Deaths", dailydeaths1) df['ActiveEst']=aktiv2 #print (df) dfout = df'Date', 'Confirmed','Deaths','Recovered', 'ActiveEst','Daily_Cases','Daily_Deaths' #print(df) #print(dfout) print(".") return(dfout)

def load_fin_wiki_data(): url="https://fi.wikipedia.org/wiki/Suomen_koronaviruspandemian_aikajana" response = requests.get(url) soup = BeautifulSoup(response.text, 'lxml') table = soup.find_all('table')[0] # Grab the first table df = pd.read_html(str(table))[0] #print(df) #Päivä Tapauksia Uusia tapauksia Sairaalassa Teholla Kuolleita Uusia kuolleita Toipuneita

df2 = df'Tapauksia','Uusia tapauksia','Sairaalassa','Teholla','Kuolleita','Uusia kuolleita','Toipuneita'

kaikkiatapauksia=df['Tapauksia'] toipuneita=df['Toipuneita'] uusiatapauksia=df['Uusia tapauksia'] sairaalassa=df['Sairaalassa'] teholla=df['Teholla'] kuolleita=df['Kuolleita'] uusiakuolleita=df['Uusia kuolleita']

len1=len(kaikkiatapauksia)

kaikkiatapauksia2=[] toipuneita2=[] uusiatapauksia2=[] sairaalassa2=[] teholla2=[] kuolleita2=[] uusiakuolleita2=[]

for n in range(0,len1): elem0=kaikkiatapauksia[n] elem1 = .join(c for c in elem0 if c.isdigit()) elem2=int(elem1) kaikkiatapauksia2.append(elem2) elem0=toipuneita[n] elem1 = .join(c for c in elem0 if c.isdigit()) toipuneita2.append(int(elem1)) elem0=uusiatapauksia[n] elem1 = .join(c for c in elem0 if c.isdigit()) uusiatapauksia2.append(int(elem1)) elem0=sairaalassa[n] #elem1 = .join(c for c in elem0 if c.isdigit()) sairaalassa2.append(int(elem0)) elem0=teholla[n] #elem1 = .join(c for c in elem0 if c.isdigit()) teholla2.append(int(elem0)) elem0=kuolleita[n] #elem1 = .join(c for c in elem0 if c.isdigit()) kuolleita2.append(int(elem0)) elem0=uusiakuolleita[n] #elem1 = .join(c for c in elem0 if c.isdigit()) uusiakuolleita2.append(int(elem0))

#kaikkiatapauksia3=np.array(kaikkiatapauksia2).astype(int) #print("---") #print(kaikkiatapauksia2) #print(toipuneita2)

kaikkiatapauksia3=np.array(kaikkiatapauksia2).astype(int) toipuneita3=np.array(toipuneita2).astype(int) uusiatapauksia3=np.array(uusiatapauksia2).astype(int) sairaalassa3=np.array(sairaalassa2).astype(int) teholla3=np.array(teholla2).astype(int) kuolleita3=np.array(kuolleita2) .astype(int) uusiakuolleita3=np.array(uusiakuolleita2).astype(int)

napapaiva1 = np.datetime64("2020-04-01") timedelta1= np.timedelta64(len(kaikkiatapauksia3),'D') napapaiva2 = napapaiva1+timedelta1

#dada1 = np.linspace(napapaiva1.astype('f8'), napapaiva2.astype('f8'), dtype='<M8[D]') dada1 = pd.date_range(napapaiva1, napapaiva2, periods=len(kaikkiatapauksia3)).to_pydatetime()

#print(dada1)

data = {'Date':dada1, 'Kaikkia tapauksia':kaikkiatapauksia3, "Uusia tapauksia":uusiatapauksia3, "Sairaalassa":sairaalassa3, "Teholla":teholla3, "Kuolleita":kuolleita3, "Uusiakuolleita":uusiakuolleita3, "Toipuneita":toipuneita3 }

df2 = pd.DataFrame(data)

#print(kaikkiatapauksia3) #print ("Fin wiki data.") return(df2)

1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. main proge

1. df=load_fin_wiki_data()

df=load_country_cases("Finland")

1. print(df)
2. quit(-1)

df2=cut_by_dates(df, paiva1,paiva2)

1. print(df2)
2. quit(-1)

dates0=df2['Date'] dailycases1=df2['Daily_Cases'] dailydeaths1=df2['Daily_Deaths']

1. cases0=df2['Daily_Cases']
2. dailycases1=df2['Uusia tapauksia']
3. dailydeaths1=df2['Uusiakuolleita']

daily1=dailycases1 x=dates0

moving7a=smooth(np.array(daily1), window="hamming")

1. print (moving7a)

1. moving7=as.np.array(moving7a)

moving71=moving7a.astype(int)

1. print(moving71)
2. exit(-1)
3. calculate_r0(0, 1, 1, 100)

1. r0s1=calculate_multiple_r0(daily1)

r0s1=calculate_multiple_r0(moving71)

r0s2=np.convolve(r0s1, np.ones((14,))/14, mode='valid')

r0s2a=np.round(r0s2,3)

r0s4=np.convolve(r0s2a, np.ones((7,))/7, mode='valid')

len1=len(dates0)

hooline=[1]*len1

print(r0s1)

fig, ax = plt.subplots(constrained_layout=True)

ax.legend(fontsize=14)

1. secax = ax.secondary_xaxis('top')
2. secax.set_xlabel('paivia')

plt.title("Koronaviruksen R0 Suomessa",fontsize=22) plt.xlabel('Pvm (2020 - 2021)',fontsize=18) plt.ylabel('Tarttuvuus R0',fontsize=18) plt.xticks(fontsize=16) plt.yticks(fontsize=16) plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d.%m'))

1. plt.gca().xaxis.set_major_locator(mdates.DayLocator())
2. plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(interval=1))

plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))

1. plt.hlines(1,0,200)

plt.plot(x,r0s2a, linewidth=3, color="Blue") plt.plot(x,hooline, "--", linewidth=3, color="green" ) plt.gcf().autofmt_xdate()

1. plt.plot(dates4, daily1)
2. plt.ylim(0.0,3.0)

1. plt.xticks(x, dates3, fontsize=14, rotation=70)
2. plt.xticks(np.arange(0, len1, 10))

print (len(daily1)) print(len(r0s2))

print ("Test EXIT_")

1. plt.xticks(np.arange(0, len1, 10))

plt.show()