Module coa.dbinfo
Project : PyCoA - Copyright ©pycoa.fr Date : april 2020 - march 2022 Authors : Olivier Dadoun, Julien Browaeys, Tristan Beau License: See joint LICENSE file Module : report About
This is the PyCoA rapport module it gives all available information concerning a database key words
Expand source code
# -*- coding: utf-8 -*-
"""Project : PyCoA - Copyright ©pycoa.fr
Date : april 2020 - march 2022
Authors : Olivier Dadoun, Julien Browaeys, Tristan Beau
License: See joint LICENSE file
Module : report
About
-----
This is the PyCoA rapport module it gives all available information concerning a database key words
"""
from coa.error import *
def generic_info(namedb, keys):
'''
Return information on the available keyswords for the database selected
'''
mydico = {}
if namedb == 'spf':
urlmaster1='https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/'
urlmaster2='https://www.data.gouv.fr/fr/datasets/synthese-des-indicateurs-de-suivi-de-lepidemie-covid-19/'
urlmaster3='https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/'
urlmaster5='https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1'
urlmaster4='https://www.data.gouv.fr/fr/datasets/indicateurs-de-suivi-de-lepidemie-de-covid-19/'
urlmaster6='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-indicateurs-sur-les-variants/'
urlmaster7='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-focus-par-niveau-scolaire/'
urlmaster8='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-indicateurs-sur-les-mutations/'
urlmaster9='https://www.data.gouv.fr/en/datasets/donnees-des-urgences-hospitalieres-et-de-sos-medecins-relatives-a-lepidemie-de-covid-19/'
url1='https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7'
url2='https://www.data.gouv.fr/fr/datasets/r/6fadff46-9efd-4c53-942a-54aca783c30c'
url3='https://www.data.gouv.fr/fr/datasets/r/406c6a23-e283-4300-9484-54e78c8ae675'
url4='https://www.data.gouv.fr/fr/datasets/r/4acad602-d8b1-4516-bc71-7d5574d5f33e'
url5='https://www.data.gouv.fr/fr/datasets/r/32a16487-3dd3-4326-9d2b-317e5a3b2daf'
url6='https://www.data.gouv.fr/fr/datasets/r/16f4fd03-797f-4616-bca9-78ff212d06e8'
url7='https://www.data.gouv.fr/fr/datasets/r/c0f59f00-3ab2-4f31-8a05-d317b43e9055'
url8='https://www.data.gouv.fr/fr/datasets/r/4d3e5a8b-9649-4c41-86ec-5420eb6b530c'
url9='https://www.data.gouv.fr/en/datasets/r/eceb9fb4-3ebc-4da3-828d-f5939712600a'
spfdic = {
'tot_dc':
['tot_dc:FILLIT',url1,urlmaster1],
'cur_hosp':
['cur_hosp:FILLIT',url1,urlmaster1],
'tot_rad':
['tot_rad:FILLIT',url1,urlmaster1],
'cur_rea':
['cur_rea:FILLIT',url1,urlmaster1],
'cur_idx_tx_incid':
['cur_idx_tx_incid: Taux d\'incidence (activité épidémique : Le taux d\'incidence correspond au nombre de personnes testées\
positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté à la taille de la population. \
Il est exprimé pour 100 000 habitants)',url2,urlmaster2],
'cur_idx_R':
['cur_idx_R:FILLIT',url4,urlmaster4],
'cur_taux_crib':
['cur_taux_crib:FILLIT',url4,urlmaster2],
'cur_idx_taux_occupation_sae':
['cur_idx_taux_occupation_sae:FILLIT',url4,urlmaster4],
'cur_taux_pos':
['cur_taux_pos: Taux de positivité des tests virologiques (Le taux de positivité correspond au nombre de personnes testées positives\
(RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté au nombre total de personnes testées positives ou \
négatives sur une période donnée ; et qui n‘ont jamais été testées positive dans les 60 jours précédents.)',url4,urlmaster2],
'tot_vacc1':
['tot_vacc1: (nom initial n_cum_dose1)',url5,urlmaster5],
'tot_vacc2':
['tot_vacc2: (nom initial n_cum_dose2)',url5,urlmaster5],
'tot_vacc3':
['tot_vacc3: (nom initial n_cum_dose3)',url5,urlmaster5],
'tot_vacc4':
['tot_vacc4: (nom initial n_cum_dose4)',url5,urlmaster5],
'tot_rappel_vacc':
['tot_rappel_vacc: (nom initial n_rappel)',url5,urlmaster5],
'tot_incid_hosp':
['tot_incid_hosp: Nombre total de personnes hospitalisées',url2,urlmaster2],
'tot_incid_rea':
['tot_incid_rea: Nombre total d\'admissions en réanimation',url2,urlmaster2],
'tot_incid_rad':
['tot_incid_rad: Nombre total de retours à domicile',url2,urlmaster2],
'tot_incid_dc':
['tot_incid_dc: Nombre total de personnes décédées',url2,urlmaster2],
'tot_P':
['tot_P: Nombre total de tests positifs',url3,urlmaster3],
'tot_T':
['tot_T: Nombre total de tests réalisés',url3,urlmaster3],
'cur_idx_Prc_tests_PCR_TA_crible' :
['Prc_tests_PCR_TA_crible: % de tests PCR criblés parmi les PCR positives.',url6,urlmaster6],
'cur_idx_Prc_susp_501Y_V1' :
['Prc_susp_501Y_V1: % de tests avec suspicion de variant 20I/501Y.V1 (UK).\n Royaume-Uni (UK): code Nexstrain= 20I/501Y.V1.',url6,urlmaster6],
'cur_idx_Prc_susp_501Y_V2_3' :
['Prc_susp_501Y_V2_3: % de tests avec suspicion de variant 20H/501Y.V2 (ZA) ou 20J/501Y.V3 (BR).Afrique du Sud (ZA) : \
code Nexstrain= 20H/501Y.V2. Brésil (BR) : code Nexstrain= 20J/501Y.V3',url6,urlmaster6],
'cur_idx_Prc_susp_IND' :
['Prc_susp_IND: % de tests avec une détection de variant mais non identifiable',url6,urlmaster6],
'cur_idx_Prc_susp_ABS' :
['Prc_susp_ABS: % de tests avec une absence de détection de variant',url6,urlmaster6],
'cur_idx_ti':
['ti : taux d\'incidence hebdomadaire rapporté à la population pour 100 000 habitants , par semaine calendaire (en milieu scolaire)',url7,urlmaster7],
'cur_idx_tp':
['tp :Le taux de positivité hebdomadaire rapporté 100 tests réalisés, par semaine calendaire (en milieu scolaire)',url7,urlmaster7],
'nb_crib' : ['Nombre de tests criblés',url8,urlmaster8],
'nb_pos' : ['Nombre de tests positifs',url8,urlmaster8],
'tx_crib' : ['Taux tests criblés',url8,urlmaster8],
'cur_idx_tx_A1':['FILL IT',url8,urlmaster8],
'cur_idx_tx_B1':['FILL IT',url8,urlmaster8],
'cur_idx_tx_C1':['FILL IT',url8,urlmaster8],
'cur_nb_A0' : ['Nombre des tests positifs pour lesquels la recherche de mutation A est négatif (A = E484K)',url8,urlmaster8],
'cur_nb_A1' : ['Nombre des tests positifs pour lesquels la recherche de mutation A est positif (A = E484K)',url8,urlmaster8],
'tx_A1' : ['Taux de présence mutation A (A = E484K)',url8,urlmaster8],
'cur_nb_B0' : ['Nombre des tests positifs pour lesquels la recherche de mutation B est négatif (B = E484Q)',url8,urlmaster8],
'cur_nb_B1' : ['Nombre des tests positifs pour lesquels la recherche de mutation B est positif (B = E484Q)',url8,urlmaster8],
'tx_B1' : ['Taux de présence mutation B (B = E484Q)',url8,urlmaster8],
'cur_nb_C0' : ['Nombre des tests positifs pour lesquels la recherche de mutation C est négatif (C = L452R)',url8,urlmaster8],
'cur_nb_C1' : ['Nombre des tests positifs pour lesquels la recherche de mutation C est positif (C = L452R)',url8,urlmaster8],
'tx_C1' : ['Taux de présence mutation C (C = L452R)',url8,urlmaster8],
'cur_nbre_pass_corona' : [' Nombre de passages aux urgences pour suspicion de COVID-19 (nbre_pass_corona)',url9,urlmaster9],
}
mydico = spfdic
elif namedb == 'spfnational':
spfn = {
'cur_reanimation': ['(nom d\'origine patients_reanimation) en current réa '],\
'cur_hospitalises': ['(nom d\'origine patients_hospitalises) en current patients hospitalises '],\
'total_cas_confirmes': ['total_cas_confirmes: total cumulé du nombre de décès'],\
'total_deces_hopital': ['total_deces_hopital: total deces hopital '],\
'total_patients_gueris': ['total_patients_gueris: total patients gueris'],\
'total_deces_ehpad': ['total cumulé deces ehpad'],\
'total_cas_confirmes_ehpad': ['total cumulé confirmes ehpad'],\
'total_cas_possibles_ehpad': ['total cumulé possibles ehpad'],\
}
for k,v in spfn.items():
spfn[k].append('https://www.data.gouv.fr/fr/datasets/r/d3a98a30-893f-47f7-96c5-2f4bcaaa0d71')
spfn[k].append('https://www.data.gouv.fr/en/datasets/donnees-relatives-a-lepidemie-de-covid-19-en-france-vue-densemble/')
mydico = spfn
elif namedb == 'insee':
insee = {
'tot_deaths_since_2018-01-01':['tot deaths number_of_deaths integrated since 2018-01-01 '],\
}
for k,v in insee.items():
insee[k].append('https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/')
insee[k].append('https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/')
mydico = insee
elif namedb == 'opencovid19':
op19 = {
'tot_deces':['tot_deces: total cumulé du nombre de décès au niveau national'],
'tot_cas_confirmes':['tot_cas_confirmes: total cumulé du nombre de cas confirmes au niveau national'],
'cur_reanimation':['cur_reanimation: nombre de personnes en réanimation'],
'cur_hospitalises':['cur_hospitalises: nombre de personnes en hospitalisation'],
'tot_gueris':['tot_gueris: total cumulé du nombre de gueris au niveau national'],
'tot_nouvelles_hospitalisations':['tot_nouvelles_hospitalisations: total cumulé du nombre d\'hospitalisation au niveau national'],
'tot_nouvelles_reanimations':['tot_nouvelles_reanimations: tot_nouvelles_reanimations: total cumulé du nombre réanimations au niveau national'],
'tot_depistes':['tot_depistes: total cumulé du nombre de personnes dépistées (testées par PCR) au niveau national'],
}
for k,v in op19.items():
op19[k].append('https://raw.githubusercontent.com/opencovid19-fr/data/master/dist/chiffres-cles.csv')
op19[k].append('https://github.com/opencovid19-fr/data')
mydico = op19
elif namedb == 'opencovid19national':
op19nat = {
'tot_deces':['tot_deces: total cumulé du nombre de décès'],
'tot_cas_confirmes':['tot_cas_confirmes: total cumulé du nombre de cas confirmés'],
'tot_cas_ehpad':['tot_cas_ehpad: total cumulé du nombre de cas en EHPAD'],
'tot_cas_confirmes_ehpad':['total cumulé du nombre de cas positifs en EHPAD'],
'tot_cas_possibles_ehpad':['tot_cas_possibles_ehpad:FILLIT'],
'tot_deces_ehpad':['total cumulé du nombre de décès en EHPAD'],
'cur_reanimation':['cur_hospitalises: nombre de personnes en reanimation'],
'cur_hospitalises':['cur_hospitalises: nombre de personnes en hospitalisation'],
'tot_gueris':['total cumulé du nombre de gueris'],
'tot_nouvelles_hospitalisations':['tot_nouvelles_hospitalisations: total cumulé du nombre d\'hospitalisation'],
'tot_nouvelles_reanimations':['tot_nouvelles_reanimations: tot_nouvelles_reanimations: total cumulé du nombre réanimations'],
'tot_depistes':['tot_depistes: total cumulé du nombre de personnes dépistées (testées par PCR)']
}
for k,v in op19nat.items():
op19nat[k].append('https://raw.githubusercontent.com/opencovid19-fr/data/master/dist/chiffres-cles.csv')
op19nat[k].append('https://github.com/opencovid19-fr/data')
mydico = op19nat
elif namedb == 'obepine':
obe = {
'idx_obepine':['tot_deces: total cumulé du nombre de décès']}
for k,v in obe.items():
obe[k].append('https://www.data.gouv.fr/fr/datasets/r/89196725-56cf-4a83-bab0-170ad1e8ef85')
obe[k].append('https://www.data.gouv.fr/en/datasets/surveillance-du-sars-cov-2-dans-les-eaux-usees-1')
mydico = obe
elif namedb == 'owid':
owid={
'total_deaths':['total_deaths: Total deaths attributed to COVID-19'],
'total_cases':['total_cases: Total confirmed cases of COVID-19'],
'total_tests':['total_tests: Total tests for COVID-19'],
'cur_new_tests':['cur_new_tests (original name new_tests): New tests for COVID-19 (only calculated for consecutive days)'],
'total_vaccinations':['total_vaccinations: Total number of COVID-19 vaccination doses administered'],
'total_population':['total_population: total population of a given country'],
'total_people_fully_vaccinated_per_hundred':['total_people_fully_vaccinated_per_hundred (original name people_fully_vaccinated_per_hundred): Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population'],
'total_boosters':['total_boosters: Total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the vaccination protocol)'],
'total_people_vaccinated':['total_people_vaccinated (original name people_vaccinated): Total number of people who received at least one vaccine dose'],
'total_people_fully_vaccinated':['total_people_fully_vaccinated (original name people_fully_vaccinated): Total number of people who received all doses prescribed by the vaccination protocol'],
'total_people_vaccinated_per_hundred':['total_people_vaccinated_per_hundred (original name people_vaccinated_per_hundred): total_people_vaccinated_per_hundred:Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population'],
'total_cases_per_million':['total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people'],
'total_deaths_per_million':['total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people'],
'total_vaccinations_per_hundred':['total_vaccinations_per_hundred: COVID19 vaccine doses administered per 100 people'],
'cur_reproduction_rate':['cur_reproduction_rate (original name reproduction_rate): Real-time estimate of the effective reproduction rate (R) of COVID-19. See https://github.com/crondonm/TrackingR/tree/main/Estimates-Database'],
'cur_icu_patients':['cur_icu_patients (orignal name icu_patients): Number of COVID-19 patients in intensive care units (ICUs) on a given day'],
'cur_hosp_patients':['cur_hosp_patients (original name hosp_patients): Number of COVID-19 patients in hospital on a given day'],
'cur_weekly_hosp_admissions':['cur_weekly_hosp_admissions (original name weekly_hosp_admissions): Number of COVID-19 patients in hospital on a given week'],
'cur_idx_positive_rate':['cur_idx_positive_rate (original name positive_rate): The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case)'],
'total_gdp_per_capita':['Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available'],
}
for k,v in owid.items():
owid[k].append("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv")
owid[k].append("https://github.com/owid")
mydico = owid
elif namedb == 'jhu':
jhu = {
'tot_deaths':['tot_deaths (original name deaths): counts include confirmed and probable (where reported).',\
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'],
'tot_confirmed':['tot_confirmed (original name confirmed): counts include confirmed and probable (where reported).',\
'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'],
'tot_recovered':['tot_recovered (original name recovered): cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project (https://covidtracking.com/)',\
'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv']
}
for k,v in jhu.items():
jhu[k].append("https://github.com/CSSEGISandData/COVID-19")
mydico = jhu
elif namedb == 'jhu-usa':
jhuusa = {
'tot_deaths':['tot_deaths (original name deaths): counts include confirmed and probable (where reported).',\
'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv'],
'tot_confirmed':['tot_confirmed (original name confirmed): counts include confirmed and probable (where reported).',\
'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv']
}
for k,v in jhuusa.items():
jhuusa[k].append("https://github.com/CSSEGISandData/COVID-19")
mydico = jhuusa
elif namedb == 'moh':
moh = {
'tot_cases':['tot_cases: total cases (from original data cases_new )'],\
'hosp_covid':['hosp_covid: hosp_covid'],\
'daily_partial':['daily_partial: daily_partial'],\
'daily_full':['daily_full: daily_full'],\
'icu_covid':['icu_covid: icu_covid'],\
'beds_icu_covid':['beds_icu_covid: beds_icu_covid'],\
}
for k,v in moh.items():
moh[k].append("https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_state.csv")
moh[k].append("https://github.com/MoH-Malaysia/covid19-public")
mydico = moh
elif namedb == 'minciencia':
mi = {
'cases':['cases: Casos confirmados'],\
}
for k,v in mi.items():
mi[k].append("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19_std.csv")
mi[k].append("https://github.com/MinCiencia")
mydico = mi
elif namedb == 'covid19india':
india = {
'tot_Deceased':['tot_Deceased (original name: Deceased): Total number of Deceased'],\
'tot_Confirmed':['tot_Confirmed (original name: Confirmed): Total number of Confirmed'],\
'tot_Recovered':['tot_Recovered ( original name: Recovered): Total number of Recovered'],\
'tot_Tested':['tot_Tested ( original name: Tested): Total number of Tested']
}
for k,v in india.items():
india[k].append('https://api.covid19india.org/csv/latest/states.csv')
india[k].append('https://www.covid19india.org/')
mydico = india
elif namedb == 'dpc':
ita = {
'tot_cases':['tot_cases:original name totale_casi + FILLIT'],\
'tot_deaths':['tot_deaths:original name deceduti + FILLIT']
}
for k,v in ita.items():
ita[k].append('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv')
ita[k].append('https://github.com/pcm-dpc/COVID-19')
mydico = ita
elif namedb == 'rki':
rki = {
'tot_deaths':['tot_deaths:FILLIT','https://github.com/jgehrcke/covid-19-germany-gae/raw/master/deaths-rki-by-ags.csv'],
'tot_cases':['tot_cases:FILLIT','https://github.com/jgehrcke/covid-19-germany-gae/raw/master/deaths-rki-by-ags.csv'],
}
for k,v in rki.items():
rki[k].append('https://github.com/jgehrcke/covid-19-germany-gae')
mydico = rki
elif namedb == 'escovid19data':
esco = {
'tot_deaths':['Cumulative deaths (original name deceased)'],\
'tot_cases':['Number of new COVID-19 cases detected with PCR (original name cases_accumulated_PCR)'],\
'cur_hosp':['Hospitalized (original name hospitalized)'],\
'tot_hosp':['Cumulative Hospitalized (original name hospitalized_accumulated)'],\
'cur_hosp_per100k':['Intensive care per 100,000 inhabitants (original name hospitalized_per_100000'],\
'cur_icu':['UCI, intensive care patient, (original name intensive_care)'],\
'tot_recovered':['Recovered (original name recovered)'],\
'tot_cases_per100k':['Cumulative cases per 100,000 inhabitants (original name cases_per_cienmil)'],\
'cur_icu_per1M' :['Intensive care per 1000,000 inhabitants (original name intensive_care_per_1000000)'],\
'tot_deaths_per100k':['Cumulative deaths per 100,000 inhabitants (original name deceassed_per_100000)'],\
'tot_cases_per100k':['Intensive care per 100,000 inhabitants, (original name hospitalized_per_100000)'],\
'incidence':['Cases in 14 days by 100,000 inhabitants (original name ia14'],\
'population':['Inhabitants of the province (orignal name poblacion)'],\
}
for k,v in esco.items():
esco[k].append('https://raw.githubusercontent.com/montera34/escovid19data/master/data/output/covid19-provincias-spain_consolidated.csv')
esco[k].append('https://github.com/montera34/escovid19data')
mydico = esco
elif namedb == 'sciensano':
sci = {
'cur_hosp':['Total number of lab-confirmed hospitalized COVID-19 patients at the moment of reporting, including ICU (prevalence) (original name TOTAL_IN)'],\
'cur_icu':['Total number of lab-confirmed hospitalized COVID-19 patients in ICU at the moment of reporting (prevalence) (original name TOTAL_IN_ICU)'],\
'cur_resp':['Total number of lab-confirmed hospitalized COVID-19 patients under respiratory support at the moment of reporting (prevalence) (original name TOTAL_IN_RESP)'],\
'cur_ecmo':['Total number of lab-confirmed hospitalized COVID-19 patients on ECMO at the moment of reporting (prevalence) (orginale name TOTAL_IN_ECMO)']
}
for k,v in sci.items():
sci[k].append('https://epistat.sciensano.be/Data/COVID19BE_HOSP.csv')
sci[k].append('https://epistat.wiv-isp.be/covid/')
mydico = sci
elif namedb == 'phe':
url='https://api.coronavirus.data.gov.uk/v2/data?areaType=ltla&metric='
phe = {
'tot_deaths':['Total number of deaths (original name cumDeaths28DaysByDeathDate)',url+'cumDeaths28DaysByDeathDate'+'&format=csv'],\
'tot_cases':['Total number of cases (originale name cumCasesBySpecimenDate)',url+'cumCasesBySpecimenDate'+'&format=csv'],
'tot_tests':['Total number of tests (originale name cumTestByPublishDate)',url+'cumLFDTests'+'&format=csv'],
'tot_vacc1':['Total number of tot_vacc1 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedFirstDoseByVaccinationDate'+'&format=csv'],
'tot_vacc2':['Total number of tot_vacc2 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedSecondDoseByVaccinationDate'+'&format=csv'],
'tot_vacc3':['Total number of tot_vacc3 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedThirddDoseByVaccinationDate'+'&format=csv'],
'cur_B.1.617.2':['Current variant B.1.617.2',url +'https://covid-surveillance-data.cog.sanger.ac.uk/download/lineages_by_ltla_and_week.tsv'],
}
for k,v in phe.items():
phe[k].append('https://coronavirus.data.gov.uk/details/download')
mydico = phe
elif namedb == 'dgs':
dgs = {
'tot_cases':['original name confirmados_1']}
for k,v in dgs.items():
dgs[k].append('https://raw.githubusercontent.com/dssg-pt/covid19pt-data/master/data_concelhos_new.csv')
dgs[k].append('https://github.com/dssg-pt/covid19pt-data')
mydico = dgs
elif namedb == 'covidtracking':
url='https://covidtracking.com/data/download/all-states-history.csv'
cotra = {
'tot_death': ['Cumulative deaths tot_death (original name death)'],\
'tot_hosp': ['Cumulative hospitalized (original name hospitalizedCumulative)'],\
'cur_hosp': ['Current hospitalized (original name hospitalizedCurrently)'],\
'tot_icu': ['(original name inIcuCumulative)'],\
'cur_icu': ['(original inIcuCurrently)'],\
'tot_neg_test': ['(original negative)'],\
'tot_pos_test': ['(original positive)'],\
'tot_onVentilator': ['(original onVentilatorCumulative)'],\
'cur_onVentilator': ['(original onVentilatorCurrently)'],\
'tot_test': ['(original totalTestResults)'],\
}
for k,v in cotra.items():
cotra[k].append(url)
cotra[k].append('https://covidtracking.com/analysis-updates/five-major-metrics-covid-19-data')
mydico = cotra
elif namedb == 'risklayer':
url='https://docs.google.com/spreadsheets/d/e/2PACX-1vQ-JLawOH35vPyOk39w0tjn64YQLlahiD2AaNfjd82pgQ37Jr1K8KMHOqJbxoi4k2FZVYBGbZ-nsxhi/pub?output=csv'
risk = {
'tot_positive': ['tot_positive ... fill it '],\
'tot_incidence': ['tot_incidence ... fill it'],\
}
for k,v in risk.items():
risk[k].append(url)
risk[k].append('https://www.risklayer-explorer.com/event/100/detail')
mydico = risk
elif namedb == 'europa':
url='https://raw.githubusercontent.com/ec-jrc/COVID-19/master/data-by-region/jrc-covid-19-all-days-by-regions.csv'
euro = {
'tot_deaths':['Orginal name CumulativeDeceased'],
'tot_positive':['sum the Original name CurrentlyPositive'],
'cur_hosp':['Orginal name Hospitalized'],
'cur_icu':['Orginal name IntensiveCare'],
}
for k,v in euro.items():
euro[k].append(url)
euro[k].append('https://github.com/ec-jrc/COVID-19/tree/master/data-by-region')
mydico = euro
elif namedb == 'imed':
imed = {
'tot_cases':['tot_cases (original name cases)',\
'https://github.com/iMEdD-Lab/open-data/blob/master/COVID-19/greece_cases_v2.csv'],
'tot_deaths':['tot_deaths (original name deaths)',\
'https://github.com/iMEdD-Lab/open-data/blob/master/COVID-19/greece_deaths_v2.csv']
}
for k,v in imed.items():
imed[k].append("https://github.com/iMEdD-Lab/open-data/tree/master/COVID-19")
mydico = imed
elif namedb == 'govcy':
mi = {
'tot_deaths':['total deaths attributed to Covid-19 disease (total deaths)'],\
'tot_cases':['total number of confirmed cases (total cases)'],\
'cur_hosp':['number of patients with covid-19 hospitalized cases (Hospitalised Cases)'],\
'cur_severe':['number of patients with covid-19 hospitalized in severe cases (Severe Cases)'],\
'cur_icu':['number of patients with covid-19 admitted to ICUs (Cases In ICUs)'],\
'cur_incub':['number of patients with covid-19 admitted to ICUs (Incubated Cases)'],\
'tot_pcr':['extract from PCR_daily tests performedtotal number of PCR tests performed (PCR_daily tests performed)'],\
'tot_rat':['total number of rapid antigen (RAT) tests performed (total RA tests)'],\
'tot_test':['total numbers of PCR and RA tests performed (total tests)'],\
}
for k,v in mi.items():
mi[k].append("https://www.data.gov.cy/sites/default/files/CY%20Covid19%20Open%20Data%20-%20Extended%20-%20new_247.csv")
mi[k].append("https://www.data.gov.cy/node/4617?language=en")
mydico = mi
else:
raise CoaKeyError('Error in the database selected, please check !')
if keys not in mydico:
raise CoaKeyError(keys + ': this keyword doesn\'t exist for this database !')
else:
return mydico[keys]Functions
def generic_info(namedb, keys)-
Return information on the available keyswords for the database selected
Expand source code
def generic_info(namedb, keys): ''' Return information on the available keyswords for the database selected ''' mydico = {} if namedb == 'spf': urlmaster1='https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/' urlmaster2='https://www.data.gouv.fr/fr/datasets/synthese-des-indicateurs-de-suivi-de-lepidemie-covid-19/' urlmaster3='https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/' urlmaster5='https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1' urlmaster4='https://www.data.gouv.fr/fr/datasets/indicateurs-de-suivi-de-lepidemie-de-covid-19/' urlmaster6='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-indicateurs-sur-les-variants/' urlmaster7='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-focus-par-niveau-scolaire/' urlmaster8='https://www.data.gouv.fr/fr/datasets/donnees-de-laboratoires-pour-le-depistage-indicateurs-sur-les-mutations/' urlmaster9='https://www.data.gouv.fr/en/datasets/donnees-des-urgences-hospitalieres-et-de-sos-medecins-relatives-a-lepidemie-de-covid-19/' url1='https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7' url2='https://www.data.gouv.fr/fr/datasets/r/6fadff46-9efd-4c53-942a-54aca783c30c' url3='https://www.data.gouv.fr/fr/datasets/r/406c6a23-e283-4300-9484-54e78c8ae675' url4='https://www.data.gouv.fr/fr/datasets/r/4acad602-d8b1-4516-bc71-7d5574d5f33e' url5='https://www.data.gouv.fr/fr/datasets/r/32a16487-3dd3-4326-9d2b-317e5a3b2daf' url6='https://www.data.gouv.fr/fr/datasets/r/16f4fd03-797f-4616-bca9-78ff212d06e8' url7='https://www.data.gouv.fr/fr/datasets/r/c0f59f00-3ab2-4f31-8a05-d317b43e9055' url8='https://www.data.gouv.fr/fr/datasets/r/4d3e5a8b-9649-4c41-86ec-5420eb6b530c' url9='https://www.data.gouv.fr/en/datasets/r/eceb9fb4-3ebc-4da3-828d-f5939712600a' spfdic = { 'tot_dc': ['tot_dc:FILLIT',url1,urlmaster1], 'cur_hosp': ['cur_hosp:FILLIT',url1,urlmaster1], 'tot_rad': ['tot_rad:FILLIT',url1,urlmaster1], 'cur_rea': ['cur_rea:FILLIT',url1,urlmaster1], 'cur_idx_tx_incid': ['cur_idx_tx_incid: Taux d\'incidence (activité épidémique : Le taux d\'incidence correspond au nombre de personnes testées\ positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté à la taille de la population. \ Il est exprimé pour 100 000 habitants)',url2,urlmaster2], 'cur_idx_R': ['cur_idx_R:FILLIT',url4,urlmaster4], 'cur_taux_crib': ['cur_taux_crib:FILLIT',url4,urlmaster2], 'cur_idx_taux_occupation_sae': ['cur_idx_taux_occupation_sae:FILLIT',url4,urlmaster4], 'cur_taux_pos': ['cur_taux_pos: Taux de positivité des tests virologiques (Le taux de positivité correspond au nombre de personnes testées positives\ (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté au nombre total de personnes testées positives ou \ négatives sur une période donnée ; et qui n‘ont jamais été testées positive dans les 60 jours précédents.)',url4,urlmaster2], 'tot_vacc1': ['tot_vacc1: (nom initial n_cum_dose1)',url5,urlmaster5], 'tot_vacc2': ['tot_vacc2: (nom initial n_cum_dose2)',url5,urlmaster5], 'tot_vacc3': ['tot_vacc3: (nom initial n_cum_dose3)',url5,urlmaster5], 'tot_vacc4': ['tot_vacc4: (nom initial n_cum_dose4)',url5,urlmaster5], 'tot_rappel_vacc': ['tot_rappel_vacc: (nom initial n_rappel)',url5,urlmaster5], 'tot_incid_hosp': ['tot_incid_hosp: Nombre total de personnes hospitalisées',url2,urlmaster2], 'tot_incid_rea': ['tot_incid_rea: Nombre total d\'admissions en réanimation',url2,urlmaster2], 'tot_incid_rad': ['tot_incid_rad: Nombre total de retours à domicile',url2,urlmaster2], 'tot_incid_dc': ['tot_incid_dc: Nombre total de personnes décédées',url2,urlmaster2], 'tot_P': ['tot_P: Nombre total de tests positifs',url3,urlmaster3], 'tot_T': ['tot_T: Nombre total de tests réalisés',url3,urlmaster3], 'cur_idx_Prc_tests_PCR_TA_crible' : ['Prc_tests_PCR_TA_crible: % de tests PCR criblés parmi les PCR positives.',url6,urlmaster6], 'cur_idx_Prc_susp_501Y_V1' : ['Prc_susp_501Y_V1: % de tests avec suspicion de variant 20I/501Y.V1 (UK).\n Royaume-Uni (UK): code Nexstrain= 20I/501Y.V1.',url6,urlmaster6], 'cur_idx_Prc_susp_501Y_V2_3' : ['Prc_susp_501Y_V2_3: % de tests avec suspicion de variant 20H/501Y.V2 (ZA) ou 20J/501Y.V3 (BR).Afrique du Sud (ZA) : \ code Nexstrain= 20H/501Y.V2. Brésil (BR) : code Nexstrain= 20J/501Y.V3',url6,urlmaster6], 'cur_idx_Prc_susp_IND' : ['Prc_susp_IND: % de tests avec une détection de variant mais non identifiable',url6,urlmaster6], 'cur_idx_Prc_susp_ABS' : ['Prc_susp_ABS: % de tests avec une absence de détection de variant',url6,urlmaster6], 'cur_idx_ti': ['ti : taux d\'incidence hebdomadaire rapporté à la population pour 100 000 habitants , par semaine calendaire (en milieu scolaire)',url7,urlmaster7], 'cur_idx_tp': ['tp :Le taux de positivité hebdomadaire rapporté 100 tests réalisés, par semaine calendaire (en milieu scolaire)',url7,urlmaster7], 'nb_crib' : ['Nombre de tests criblés',url8,urlmaster8], 'nb_pos' : ['Nombre de tests positifs',url8,urlmaster8], 'tx_crib' : ['Taux tests criblés',url8,urlmaster8], 'cur_idx_tx_A1':['FILL IT',url8,urlmaster8], 'cur_idx_tx_B1':['FILL IT',url8,urlmaster8], 'cur_idx_tx_C1':['FILL IT',url8,urlmaster8], 'cur_nb_A0' : ['Nombre des tests positifs pour lesquels la recherche de mutation A est négatif (A = E484K)',url8,urlmaster8], 'cur_nb_A1' : ['Nombre des tests positifs pour lesquels la recherche de mutation A est positif (A = E484K)',url8,urlmaster8], 'tx_A1' : ['Taux de présence mutation A (A = E484K)',url8,urlmaster8], 'cur_nb_B0' : ['Nombre des tests positifs pour lesquels la recherche de mutation B est négatif (B = E484Q)',url8,urlmaster8], 'cur_nb_B1' : ['Nombre des tests positifs pour lesquels la recherche de mutation B est positif (B = E484Q)',url8,urlmaster8], 'tx_B1' : ['Taux de présence mutation B (B = E484Q)',url8,urlmaster8], 'cur_nb_C0' : ['Nombre des tests positifs pour lesquels la recherche de mutation C est négatif (C = L452R)',url8,urlmaster8], 'cur_nb_C1' : ['Nombre des tests positifs pour lesquels la recherche de mutation C est positif (C = L452R)',url8,urlmaster8], 'tx_C1' : ['Taux de présence mutation C (C = L452R)',url8,urlmaster8], 'cur_nbre_pass_corona' : [' Nombre de passages aux urgences pour suspicion de COVID-19 (nbre_pass_corona)',url9,urlmaster9], } mydico = spfdic elif namedb == 'spfnational': spfn = { 'cur_reanimation': ['(nom d\'origine patients_reanimation) en current réa '],\ 'cur_hospitalises': ['(nom d\'origine patients_hospitalises) en current patients hospitalises '],\ 'total_cas_confirmes': ['total_cas_confirmes: total cumulé du nombre de décès'],\ 'total_deces_hopital': ['total_deces_hopital: total deces hopital '],\ 'total_patients_gueris': ['total_patients_gueris: total patients gueris'],\ 'total_deces_ehpad': ['total cumulé deces ehpad'],\ 'total_cas_confirmes_ehpad': ['total cumulé confirmes ehpad'],\ 'total_cas_possibles_ehpad': ['total cumulé possibles ehpad'],\ } for k,v in spfn.items(): spfn[k].append('https://www.data.gouv.fr/fr/datasets/r/d3a98a30-893f-47f7-96c5-2f4bcaaa0d71') spfn[k].append('https://www.data.gouv.fr/en/datasets/donnees-relatives-a-lepidemie-de-covid-19-en-france-vue-densemble/') mydico = spfn elif namedb == 'insee': insee = { 'tot_deaths_since_2018-01-01':['tot deaths number_of_deaths integrated since 2018-01-01 '],\ } for k,v in insee.items(): insee[k].append('https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/') insee[k].append('https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/') mydico = insee elif namedb == 'opencovid19': op19 = { 'tot_deces':['tot_deces: total cumulé du nombre de décès au niveau national'], 'tot_cas_confirmes':['tot_cas_confirmes: total cumulé du nombre de cas confirmes au niveau national'], 'cur_reanimation':['cur_reanimation: nombre de personnes en réanimation'], 'cur_hospitalises':['cur_hospitalises: nombre de personnes en hospitalisation'], 'tot_gueris':['tot_gueris: total cumulé du nombre de gueris au niveau national'], 'tot_nouvelles_hospitalisations':['tot_nouvelles_hospitalisations: total cumulé du nombre d\'hospitalisation au niveau national'], 'tot_nouvelles_reanimations':['tot_nouvelles_reanimations: tot_nouvelles_reanimations: total cumulé du nombre réanimations au niveau national'], 'tot_depistes':['tot_depistes: total cumulé du nombre de personnes dépistées (testées par PCR) au niveau national'], } for k,v in op19.items(): op19[k].append('https://raw.githubusercontent.com/opencovid19-fr/data/master/dist/chiffres-cles.csv') op19[k].append('https://github.com/opencovid19-fr/data') mydico = op19 elif namedb == 'opencovid19national': op19nat = { 'tot_deces':['tot_deces: total cumulé du nombre de décès'], 'tot_cas_confirmes':['tot_cas_confirmes: total cumulé du nombre de cas confirmés'], 'tot_cas_ehpad':['tot_cas_ehpad: total cumulé du nombre de cas en EHPAD'], 'tot_cas_confirmes_ehpad':['total cumulé du nombre de cas positifs en EHPAD'], 'tot_cas_possibles_ehpad':['tot_cas_possibles_ehpad:FILLIT'], 'tot_deces_ehpad':['total cumulé du nombre de décès en EHPAD'], 'cur_reanimation':['cur_hospitalises: nombre de personnes en reanimation'], 'cur_hospitalises':['cur_hospitalises: nombre de personnes en hospitalisation'], 'tot_gueris':['total cumulé du nombre de gueris'], 'tot_nouvelles_hospitalisations':['tot_nouvelles_hospitalisations: total cumulé du nombre d\'hospitalisation'], 'tot_nouvelles_reanimations':['tot_nouvelles_reanimations: tot_nouvelles_reanimations: total cumulé du nombre réanimations'], 'tot_depistes':['tot_depistes: total cumulé du nombre de personnes dépistées (testées par PCR)'] } for k,v in op19nat.items(): op19nat[k].append('https://raw.githubusercontent.com/opencovid19-fr/data/master/dist/chiffres-cles.csv') op19nat[k].append('https://github.com/opencovid19-fr/data') mydico = op19nat elif namedb == 'obepine': obe = { 'idx_obepine':['tot_deces: total cumulé du nombre de décès']} for k,v in obe.items(): obe[k].append('https://www.data.gouv.fr/fr/datasets/r/89196725-56cf-4a83-bab0-170ad1e8ef85') obe[k].append('https://www.data.gouv.fr/en/datasets/surveillance-du-sars-cov-2-dans-les-eaux-usees-1') mydico = obe elif namedb == 'owid': owid={ 'total_deaths':['total_deaths: Total deaths attributed to COVID-19'], 'total_cases':['total_cases: Total confirmed cases of COVID-19'], 'total_tests':['total_tests: Total tests for COVID-19'], 'cur_new_tests':['cur_new_tests (original name new_tests): New tests for COVID-19 (only calculated for consecutive days)'], 'total_vaccinations':['total_vaccinations: Total number of COVID-19 vaccination doses administered'], 'total_population':['total_population: total population of a given country'], 'total_people_fully_vaccinated_per_hundred':['total_people_fully_vaccinated_per_hundred (original name people_fully_vaccinated_per_hundred): Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population'], 'total_boosters':['total_boosters: Total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the vaccination protocol)'], 'total_people_vaccinated':['total_people_vaccinated (original name people_vaccinated): Total number of people who received at least one vaccine dose'], 'total_people_fully_vaccinated':['total_people_fully_vaccinated (original name people_fully_vaccinated): Total number of people who received all doses prescribed by the vaccination protocol'], 'total_people_vaccinated_per_hundred':['total_people_vaccinated_per_hundred (original name people_vaccinated_per_hundred): total_people_vaccinated_per_hundred:Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population'], 'total_cases_per_million':['total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people'], 'total_deaths_per_million':['total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people'], 'total_vaccinations_per_hundred':['total_vaccinations_per_hundred: COVID19 vaccine doses administered per 100 people'], 'cur_reproduction_rate':['cur_reproduction_rate (original name reproduction_rate): Real-time estimate of the effective reproduction rate (R) of COVID-19. See https://github.com/crondonm/TrackingR/tree/main/Estimates-Database'], 'cur_icu_patients':['cur_icu_patients (orignal name icu_patients): Number of COVID-19 patients in intensive care units (ICUs) on a given day'], 'cur_hosp_patients':['cur_hosp_patients (original name hosp_patients): Number of COVID-19 patients in hospital on a given day'], 'cur_weekly_hosp_admissions':['cur_weekly_hosp_admissions (original name weekly_hosp_admissions): Number of COVID-19 patients in hospital on a given week'], 'cur_idx_positive_rate':['cur_idx_positive_rate (original name positive_rate): The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case)'], 'total_gdp_per_capita':['Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available'], } for k,v in owid.items(): owid[k].append("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv") owid[k].append("https://github.com/owid") mydico = owid elif namedb == 'jhu': jhu = { 'tot_deaths':['tot_deaths (original name deaths): counts include confirmed and probable (where reported).',\ 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'], 'tot_confirmed':['tot_confirmed (original name confirmed): counts include confirmed and probable (where reported).',\ 'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'], 'tot_recovered':['tot_recovered (original name recovered): cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project (https://covidtracking.com/)',\ 'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'] } for k,v in jhu.items(): jhu[k].append("https://github.com/CSSEGISandData/COVID-19") mydico = jhu elif namedb == 'jhu-usa': jhuusa = { 'tot_deaths':['tot_deaths (original name deaths): counts include confirmed and probable (where reported).',\ 'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv'], 'tot_confirmed':['tot_confirmed (original name confirmed): counts include confirmed and probable (where reported).',\ 'https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv'] } for k,v in jhuusa.items(): jhuusa[k].append("https://github.com/CSSEGISandData/COVID-19") mydico = jhuusa elif namedb == 'moh': moh = { 'tot_cases':['tot_cases: total cases (from original data cases_new )'],\ 'hosp_covid':['hosp_covid: hosp_covid'],\ 'daily_partial':['daily_partial: daily_partial'],\ 'daily_full':['daily_full: daily_full'],\ 'icu_covid':['icu_covid: icu_covid'],\ 'beds_icu_covid':['beds_icu_covid: beds_icu_covid'],\ } for k,v in moh.items(): moh[k].append("https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_state.csv") moh[k].append("https://github.com/MoH-Malaysia/covid19-public") mydico = moh elif namedb == 'minciencia': mi = { 'cases':['cases: Casos confirmados'],\ } for k,v in mi.items(): mi[k].append("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19_std.csv") mi[k].append("https://github.com/MinCiencia") mydico = mi elif namedb == 'covid19india': india = { 'tot_Deceased':['tot_Deceased (original name: Deceased): Total number of Deceased'],\ 'tot_Confirmed':['tot_Confirmed (original name: Confirmed): Total number of Confirmed'],\ 'tot_Recovered':['tot_Recovered ( original name: Recovered): Total number of Recovered'],\ 'tot_Tested':['tot_Tested ( original name: Tested): Total number of Tested'] } for k,v in india.items(): india[k].append('https://api.covid19india.org/csv/latest/states.csv') india[k].append('https://www.covid19india.org/') mydico = india elif namedb == 'dpc': ita = { 'tot_cases':['tot_cases:original name totale_casi + FILLIT'],\ 'tot_deaths':['tot_deaths:original name deceduti + FILLIT'] } for k,v in ita.items(): ita[k].append('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv') ita[k].append('https://github.com/pcm-dpc/COVID-19') mydico = ita elif namedb == 'rki': rki = { 'tot_deaths':['tot_deaths:FILLIT','https://github.com/jgehrcke/covid-19-germany-gae/raw/master/deaths-rki-by-ags.csv'], 'tot_cases':['tot_cases:FILLIT','https://github.com/jgehrcke/covid-19-germany-gae/raw/master/deaths-rki-by-ags.csv'], } for k,v in rki.items(): rki[k].append('https://github.com/jgehrcke/covid-19-germany-gae') mydico = rki elif namedb == 'escovid19data': esco = { 'tot_deaths':['Cumulative deaths (original name deceased)'],\ 'tot_cases':['Number of new COVID-19 cases detected with PCR (original name cases_accumulated_PCR)'],\ 'cur_hosp':['Hospitalized (original name hospitalized)'],\ 'tot_hosp':['Cumulative Hospitalized (original name hospitalized_accumulated)'],\ 'cur_hosp_per100k':['Intensive care per 100,000 inhabitants (original name hospitalized_per_100000'],\ 'cur_icu':['UCI, intensive care patient, (original name intensive_care)'],\ 'tot_recovered':['Recovered (original name recovered)'],\ 'tot_cases_per100k':['Cumulative cases per 100,000 inhabitants (original name cases_per_cienmil)'],\ 'cur_icu_per1M' :['Intensive care per 1000,000 inhabitants (original name intensive_care_per_1000000)'],\ 'tot_deaths_per100k':['Cumulative deaths per 100,000 inhabitants (original name deceassed_per_100000)'],\ 'tot_cases_per100k':['Intensive care per 100,000 inhabitants, (original name hospitalized_per_100000)'],\ 'incidence':['Cases in 14 days by 100,000 inhabitants (original name ia14'],\ 'population':['Inhabitants of the province (orignal name poblacion)'],\ } for k,v in esco.items(): esco[k].append('https://raw.githubusercontent.com/montera34/escovid19data/master/data/output/covid19-provincias-spain_consolidated.csv') esco[k].append('https://github.com/montera34/escovid19data') mydico = esco elif namedb == 'sciensano': sci = { 'cur_hosp':['Total number of lab-confirmed hospitalized COVID-19 patients at the moment of reporting, including ICU (prevalence) (original name TOTAL_IN)'],\ 'cur_icu':['Total number of lab-confirmed hospitalized COVID-19 patients in ICU at the moment of reporting (prevalence) (original name TOTAL_IN_ICU)'],\ 'cur_resp':['Total number of lab-confirmed hospitalized COVID-19 patients under respiratory support at the moment of reporting (prevalence) (original name TOTAL_IN_RESP)'],\ 'cur_ecmo':['Total number of lab-confirmed hospitalized COVID-19 patients on ECMO at the moment of reporting (prevalence) (orginale name TOTAL_IN_ECMO)'] } for k,v in sci.items(): sci[k].append('https://epistat.sciensano.be/Data/COVID19BE_HOSP.csv') sci[k].append('https://epistat.wiv-isp.be/covid/') mydico = sci elif namedb == 'phe': url='https://api.coronavirus.data.gov.uk/v2/data?areaType=ltla&metric=' phe = { 'tot_deaths':['Total number of deaths (original name cumDeaths28DaysByDeathDate)',url+'cumDeaths28DaysByDeathDate'+'&format=csv'],\ 'tot_cases':['Total number of cases (originale name cumCasesBySpecimenDate)',url+'cumCasesBySpecimenDate'+'&format=csv'], 'tot_tests':['Total number of tests (originale name cumTestByPublishDate)',url+'cumLFDTests'+'&format=csv'], 'tot_vacc1':['Total number of tot_vacc1 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedFirstDoseByVaccinationDate'+'&format=csv'], 'tot_vacc2':['Total number of tot_vacc2 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedSecondDoseByVaccinationDate'+'&format=csv'], 'tot_vacc3':['Total number of tot_vacc3 (originale name cumCasesBySpecimenDate)',url+'cumPeopleVaccinatedThirddDoseByVaccinationDate'+'&format=csv'], 'cur_B.1.617.2':['Current variant B.1.617.2',url +'https://covid-surveillance-data.cog.sanger.ac.uk/download/lineages_by_ltla_and_week.tsv'], } for k,v in phe.items(): phe[k].append('https://coronavirus.data.gov.uk/details/download') mydico = phe elif namedb == 'dgs': dgs = { 'tot_cases':['original name confirmados_1']} for k,v in dgs.items(): dgs[k].append('https://raw.githubusercontent.com/dssg-pt/covid19pt-data/master/data_concelhos_new.csv') dgs[k].append('https://github.com/dssg-pt/covid19pt-data') mydico = dgs elif namedb == 'covidtracking': url='https://covidtracking.com/data/download/all-states-history.csv' cotra = { 'tot_death': ['Cumulative deaths tot_death (original name death)'],\ 'tot_hosp': ['Cumulative hospitalized (original name hospitalizedCumulative)'],\ 'cur_hosp': ['Current hospitalized (original name hospitalizedCurrently)'],\ 'tot_icu': ['(original name inIcuCumulative)'],\ 'cur_icu': ['(original inIcuCurrently)'],\ 'tot_neg_test': ['(original negative)'],\ 'tot_pos_test': ['(original positive)'],\ 'tot_onVentilator': ['(original onVentilatorCumulative)'],\ 'cur_onVentilator': ['(original onVentilatorCurrently)'],\ 'tot_test': ['(original totalTestResults)'],\ } for k,v in cotra.items(): cotra[k].append(url) cotra[k].append('https://covidtracking.com/analysis-updates/five-major-metrics-covid-19-data') mydico = cotra elif namedb == 'risklayer': url='https://docs.google.com/spreadsheets/d/e/2PACX-1vQ-JLawOH35vPyOk39w0tjn64YQLlahiD2AaNfjd82pgQ37Jr1K8KMHOqJbxoi4k2FZVYBGbZ-nsxhi/pub?output=csv' risk = { 'tot_positive': ['tot_positive ... fill it '],\ 'tot_incidence': ['tot_incidence ... fill it'],\ } for k,v in risk.items(): risk[k].append(url) risk[k].append('https://www.risklayer-explorer.com/event/100/detail') mydico = risk elif namedb == 'europa': url='https://raw.githubusercontent.com/ec-jrc/COVID-19/master/data-by-region/jrc-covid-19-all-days-by-regions.csv' euro = { 'tot_deaths':['Orginal name CumulativeDeceased'], 'tot_positive':['sum the Original name CurrentlyPositive'], 'cur_hosp':['Orginal name Hospitalized'], 'cur_icu':['Orginal name IntensiveCare'], } for k,v in euro.items(): euro[k].append(url) euro[k].append('https://github.com/ec-jrc/COVID-19/tree/master/data-by-region') mydico = euro elif namedb == 'imed': imed = { 'tot_cases':['tot_cases (original name cases)',\ 'https://github.com/iMEdD-Lab/open-data/blob/master/COVID-19/greece_cases_v2.csv'], 'tot_deaths':['tot_deaths (original name deaths)',\ 'https://github.com/iMEdD-Lab/open-data/blob/master/COVID-19/greece_deaths_v2.csv'] } for k,v in imed.items(): imed[k].append("https://github.com/iMEdD-Lab/open-data/tree/master/COVID-19") mydico = imed elif namedb == 'govcy': mi = { 'tot_deaths':['total deaths attributed to Covid-19 disease (total deaths)'],\ 'tot_cases':['total number of confirmed cases (total cases)'],\ 'cur_hosp':['number of patients with covid-19 hospitalized cases (Hospitalised Cases)'],\ 'cur_severe':['number of patients with covid-19 hospitalized in severe cases (Severe Cases)'],\ 'cur_icu':['number of patients with covid-19 admitted to ICUs (Cases In ICUs)'],\ 'cur_incub':['number of patients with covid-19 admitted to ICUs (Incubated Cases)'],\ 'tot_pcr':['extract from PCR_daily tests performedtotal number of PCR tests performed (PCR_daily tests performed)'],\ 'tot_rat':['total number of rapid antigen (RAT) tests performed (total RA tests)'],\ 'tot_test':['total numbers of PCR and RA tests performed (total tests)'],\ } for k,v in mi.items(): mi[k].append("https://www.data.gov.cy/sites/default/files/CY%20Covid19%20Open%20Data%20-%20Extended%20-%20new_247.csv") mi[k].append("https://www.data.gov.cy/node/4617?language=en") mydico = mi else: raise CoaKeyError('Error in the database selected, please check !') if keys not in mydico: raise CoaKeyError(keys + ': this keyword doesn\'t exist for this database !') else: return mydico[keys]