1. 3. COVID-19 statistics from aggregated data from net site
   4. with Python
   5. active case plot
   6. Input from internet site: cases, recovered, deaths.
   7. Calculates active cases.
   8. version 0003.0004
   9. 24.7.2022

paiva1="2021-09-01" paiva2="2022-07-07" ymax1=1200 ymax2=25

import math as math import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker import locale from datetime import datetime, timedelta import matplotlib.dates as mdates from scipy import interpolate import scipy.signal from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator, MaxNLocator)

from scipy.signal import savgol_filter

from mizani.breaks import date_breaks, minor_breaks from mizani.formatters import date_format

from bs4 import BeautifulSoup import requests

1. import locale

from datetime import datetime, timedelta

import matplotlib.ticker as ticker import matplotlib.dates as mdates from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator)

from matplotlib.ticker import MaxNLocator from matplotlib.ticker import ScalarFormatter

def format_func(value, tick_number):

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

1. locale.setlocale(locale.LC_ALL, 'fi_FI')

1. 1. very basic exponential r0 calculation

def calculate_r0(time1, time2, val1, val2): k=0 td=time2-time1 ## #optim #td=1 gr0=math.log(val2/val1) gr=gr0/td 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 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()) if (elem1!=): toipuneita2.append(int(elem1)) else: toipuneita2.append(0) 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)

for n in range(1,len1): toipu0=toipuneita3[n] if (toipu0==0): paikka=n toipui=toipu1 break toipu1=toipu0

#print (toipui) #print (paikka)

## oletus: toipuu suureksi osaksi akuutista vaiheesta 3 viikossa

for n in range(paikka,len1): toipui=toipui+uusiatapauksia3[n-21]-uusiakuolleita3[n] toipuneita3[n]=toipui print(toipuneita3[n])

#quit(-1)

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)

def get_solanpaa_fi_data(): url="https://covid19.solanpaa.fi/data/fin_cases.json" response = requests.get(url,allow_redirects=True) open('solanpaa_fi.json', 'w').write(response.text) with open('solanpaa_fi.json') as f: sola1=pd.read_json(f)

sola1_top = sola1.head() #print (sola1_top) #Rt […] #Rt_lower […] #Rt_upper […] #Rt_lower50 […] #Rt_upper50 […] #Rt_lower90 […] #Rt_upper90 […] #new_cases_uks […] #new_cases_uks_lower50 […] #new_cases_uks_upper50 […] #new_cases_uks_lower90 […] #new_cases_uks_upper90 […] #new_cases_uks_lower […] #new_cases_uks_upper […]

dada1=sola1["date"] casa1=sola1["cases"] death1=sola1["deaths"] newcasa1=sola1["new_cases"] newdeath1=sola1["new_deaths"] hosp1=sola1["hospitalized"] icu1=sola1["in_icu"] rt=sola1["Rt"] newcasauks=sola1["new_cases_uks"]

print(dada1) len1=len(dada1) toipuneita=np.zeros(len1) toipuneita=toipuneita*0 aktiivisia=toipuneita

toipui=0 for n in range(0,len1):

if(n>21): toipui=toipui+newcasa1[n-21]-newdeath1[n]

toipuneita[n]=toipui

aktiivisia=casa1-toipuneita-death1 #print(toipuneita)

#print("KKKK") #print (dada1[0+21])

data = {'Date':dada1, 'Tapauksia':casa1, 'Kuolemia':death1, 'Sairaalassa':hosp1, 'Teholla':icu1, 'Uusia_tapauksia':newcasa1, 'Uusia_kuolemia':newdeath1, 'R':rt, 'Uusia_tapauksia_ennuste':newcasauks, 'Toipuneita':toipuneita, 'Aktiivisia':aktiivisia, }

df = pd.DataFrame(data)

return(df)

def get_ecdc_fi_hospital_data(): url="https://opendata.ecdc.europa.eu/covid19/hospitalicuadmissionrates/json/" response = requests.get(url,allow_redirects=True) open('ecdc_hoic.json', 'w').write(response.text) with open('ecdc_hoic.json') as f: sola1=pd.read_json(f)

#print(sola1.head())

sola2=sola1.loc[sola1["country"]=='Finland']

#sola2.to_csv (r'ecdc_hospital_finland_origo.csv', index = True, header=True, sep=';')

#print(sola2.head())

dada0=sola2["date"] hosp0=sola2["value"] country0=sola2["country"]

len1=len(dada0) len2=int(len1/2) #print (len2)

dada1=dada0[1:len2-1] hosp1=np.array(hosp0[1:len2-1]) icu1=np.array(hosp0[len2:len1])

#print(dada1) print (icu1) quit(-1)

data = {'Date':dada1, 'Sairaalassa':hosp1, 'Teholla':icu1 }

df = pd.DataFrame(data)

df.to_csv (r'ecdc_hospital_finland.csv', index = True, header=True, sep=';')

return df

def get_thl_fi_open_data(): ## thl open data, 1.2.2021

url1="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"

url2="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-492118&column=dateweek20200101-508804L"

headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'} response1 = requests.get(url1,headers=headers,allow_redirects=True) open('thl_cases1.json', 'w').write(response1.text) with open('thl_cases1.json') as json_file1: data1 = json.load(json_file1) #print(data1)

headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'} response2 = requests.get(url2,headers=headers,allow_redirects=True) open('thl_deaths1.json', 'w').write(response2.text) with open('thl_deaths1.json') as json_file2: data2 = json.load(json_file2) #print(data1)

k2=data1['dataset'] k3=k2['dimension'] k4=k3['dateweek20200101'] k5=k4['category'] k6=k5['label'] k8a=k6.keys() k8b=k6.values() d1=k2['value']

m2=data2['dataset'] m3=m2['dimension'] m4=m3['dateweek20200101'] m5=m4['category'] m6=m5['label'] m8a=m6.keys() m8b=m6.values() d2=m2['value']

#print (d1)

d1a=d1.keys() d1b=d1.values() d2a=d2.keys() d2b=d2.values()

#print (k8b) #print (d1a) #print (d1b) #print (d2a) #print (d2b)

len1=len(k8b)

#print(len1)

#dates0=np.datetime64(np.array(list(k8b))) dates0=list(k8b)

casekeys=np.array(list(d1a)).astype(int) cases0=np.array(list(d1b)).astype(int) deathkeys=np.array(list(d2a)).astype(int) deaths0=np.array(list(d2b)).astype(int)

#print(dates0) #print(casekeys) #print(cases0)

kasetab1=np.empty(len1).astype(int) kasetab1[casekeys]=cases0 deathtab1=np.empty(len1).astype(int) deathtab1[deathkeys]=deaths0

#print (len(dates0)) #print (len(kasetab1))

datax = {'Date':dates0, 'Uusia_tapauksia':kasetab1, 'Uusia_kuolemia':deathtab1 }

df = pd.DataFrame(datax)

return(df)

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

df=get_solanpaa_fi_data()

1. quit(-1)

1. df=load_country_cases("Finland")

1. df=load_fin_wiki_data()
2. print(df)
3. quit(-1)

df2=cut_by_dates(df, paiva1,paiva2)

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

df.to_csv (r'kovadata1.csv', index = True, header=True, sep=';')

dates0=df2['Date']

1. cases0=df2['Daily_Cases']

dailycases1=df2['Uusia_tapauksia'] dailydeaths1=df2['Uusia_kuolemia']

konf1=np.array(df2['Tapauksia']) rekov1=np.array(df2['Toipuneita']) death1=np.array(df2['Kuolemia']) hospital1=np.array(df2['Sairaalassa']) icu1=np.array(df2['Teholla'])

1. rekr1 = rekov1.rolling(window=spanni).mean()
2. rekr2 = rekr1.rolling(window=spanni).mean()

rekr2 = scipy.signal.savgol_filter(rekov1,7, 1)

aktiv1=konf1-rekr2-death1

dailycases_savgol_1 = scipy.signal.savgol_filter(dailycases1,7, 1)

pos1=len(dailycases_savgol_1)-2

time2=pos1-0 time1=pos1-21

val1=dailycases_savgol_1[time1] val2=dailycases_savgol_1[time2]

ro00=calculate_r0(time1, time2, val1, val2)

ro=round(ro00,2)

print("R0") print (ro)

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

ax.legend(fontsize=14)

ax2=ax.twinx()

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

plt.xticks(fontsize=18)

plt.yticks(fontsize=18, rotation=0)

ax2.set_xlabel('Pvm 2020-2021', color='g',size=18) ax2.set_ylabel('Akuutteja tapauksia (akuutein vaihe)', color='#800000',size=18) ax2.set_title('Akuutteja koronavirustapauksia ja sairaalassa', color='b',size=22) ax.set_ylabel('Sairaalassa', color='#3f7f00', size=18)

ax.tick_params(axis='both', which='major', labelsize=18)

ax2.plot(dates0, aktiv1,'#800000',linewidth=4.0, label='Akuuteimpia tapauksia') ax.plot(dates0, hospital1,'#3f7f00',linestyle="--",linewidth=4.0, label='Sairaalassa')

locator1 = mdates.MonthLocator() # every month

1. Specify the format - %b gives us Jan, Feb...
2. dateformat1 = mdates.DateFormatter('%b')

dateformat1 = mdates.DateFormatter('%d.%m')

ax.xaxis.set_major_formatter(dateformat1) ax.xaxis.set_major_locator(locator1)

ax2.yaxis.set_major_locator(MaxNLocator(integer=True))

ax.legend(loc = (.05,.90), fontsize=14,frameon = False) ax2.legend( loc = (.05, .82),fontsize=14, frameon = False)

plt.show()

1. fig.savefig('suomen_aktiivisia_koronatapauksia_arvio_2.svg')
2. df_aktive.to_csv (r'active1.csv', index = True, header=True, sep=';')

1. print(dfout)