cowidev.vax.batch

cowidev.vax.batch.argentina

class cowidev.vax.batch.argentina.Argentina[source]

Bases: CountryVaxBase

_build_df(data)[source]
_build_df_age_group(data)[source]
_check_data_age(data)[source]
_parse_data(data)[source]
_parse_data_age(data)[source]
age_group_valid = {'12-17', '18-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90-99', '<12', '>=100'}
export()[source]
location: str = 'Argentina'
pipe_age_cumsum(df)[source]
pipe_age_date(df)[source]
pipe_aggregate_vaccines(df: DataFrame)[source]
pipe_base_cumsum(df: DataFrame)[source]
pipe_base_metrics(df)[source]
pipe_base_vaccines(df: DataFrame)[source]
pipe_vaccine(df: DataFrame)[source]
pipeline(df: DataFrame) Series[source]
pipeline_age(df)[source]
pipeline_base(df: DataFrame)[source]
pipeline_manufacturer(df: DataFrame) Series[source]
read()[source]
read_age()[source]
source_url = 'https://covidstats.com.ar/ws/vacunadosargentina?portipovacuna=1'
source_url_age = 'https://covidstats.com.ar/ws/vacunadosargentina?porgrupoetario=1'
source_url_ref = 'https://covidstats.com.ar/'
vaccine_mapping = {'AstraZeneca ChAdOx1 S recombinante': 'Oxford/AstraZeneca', 'COVISHIELD ChAdOx1nCoV COVID 19': 'Oxford/AstraZeneca', 'Cansino Ad5 nCoV': 'CanSino', 'Moderna ARNm': 'Moderna', 'Moderna Pediátrica': 'Moderna', 'Pfizer BioNTech Comirnaty': 'Pfizer/BioNTech', 'Pfizer Pediátrica': 'Pfizer/BioNTech', 'Sinopharm Vacuna SARSCOV 2 inactivada': 'Sinopharm/Beijing', 'Sputnik V COVID19 Instituto Gamaleya': 'Sputnik V'}
cowidev.vax.batch.argentina.main()[source]

cowidev.vax.batch.australia

class cowidev.vax.batch.australia.Australia[source]

Bases: CountryVaxBase

columns_rename = {'dose_1': 'people_vaccinated', 'dose_2': 'people_fully_vaccinated', 'dose_3': 'total_boosters'}
export()[source]
location: str = 'Australia'
pipe_age_groups(df)[source]
pipe_age_metadata(df: DataFrame) DataFrame[source]
pipe_age_numeric(df)[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_total_vaccinations(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
read() DataFrame[source]
read_age() DataFrame[source]
source_file = 'https://covidbaseau.com/people-vaccinated.csv'
source_url = {'age_1d': 'https://covidbaseau.com/historical/Vaccinations%20By%20Age%20Group%20and%20State%20First.csv', 'age_2d': 'https://covidbaseau.com/historical/Vaccinations%20By%20Age%20Group%20and%20State%20Second.csv', 'main': 'https://covidbaseau.com/people-vaccinated.csv'}
source_url_ref = 'https://covidbaseau.com/'
vaccine_timeline = {'Moderna': '2021-03-06', 'Novavax': '2022-02-17', 'Oxford/AstraZeneca': '2021-03-06', 'Pfizer/BioNTech': '2021-01-01'}
cowidev.vax.batch.australia.main()[source]

cowidev.vax.batch.austria

class cowidev.vax.batch.austria.Austria[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Austria'
one_dose_vaccines: str = ['Janssen']
pipe_date(df: DataFrame) DataFrame[source]
pipe_filter_rows(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_quick_fix(df: DataFrame) DataFrame[source]
pipe_reshape(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://info.gesundheitsministerium.gv.at/data/COVID19_vaccination_doses_timeline_v202206.csv'
source_url_ref: str = 'https://info.gesundheitsministerium.gv.at/opendata/'
vaccine_mapping: dict = {'AstraZeneca': 'Oxford/AstraZeneca', 'BioNTechPfizer': 'Pfizer/BioNTech', 'Janssen': 'Johnson&Johnson', 'Moderna': 'Moderna', 'Novavax': 'Novavax', 'Valneva': 'Valneva'}
cowidev.vax.batch.austria.main()[source]

cowidev.vax.batch.belgium

class cowidev.vax.batch.belgium.Belgium[source]

Bases: CountryVaxBase

export()[source]
pipe_add_totals(df: DataFrame) DataFrame[source]
pipe_aggregate(df: DataFrame) DataFrame[source]
pipe_cumsum(df: DataFrame) DataFrame[source]
pipe_dose_check(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_vaccine_name(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
cowidev.vax.batch.belgium.main()[source]

cowidev.vax.batch.bolivia

class cowidev.vax.batch.bolivia.Bolivia[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Bolivia'
pipe_columns_out(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read()[source]
source_url: list = {'doses_1': 'https://github.com/dquintani/vacunacion/raw/main/datos/primeras_bidosis_acumulado.csv', 'doses_2': 'https://github.com/dquintani/vacunacion/raw/main/datos/segundas_bidosis_acumulado.csv ', 'doses_boosters_1': 'https://github.com/dquintani/vacunacion/raw/main/datos/dosis_refuerzo1_acumulado.csv', 'doses_unique': 'https://github.com/dquintani/vacunacion/raw/main/datos/unicas_acumulado.csv'}
source_url_ref: str = 'https://github.com/dquintani/vacunacion/'
cowidev.vax.batch.bolivia.main()[source]

cowidev.vax.batch.canada

class cowidev.vax.batch.canada.Canada[source]

Bases: CountryVaxBase

age_pattern: str = '0?(\\d{1,2})(?:–0?(\\d{1,2})|\\+)'
cols_age: dict = {'age': 'age', 'numtotal_additional': 'people_with_booster', 'numtotal_atleast1dose': 'people_vaccinated', 'numtotal_fully': 'people_fully_vaccinated', 'week_end': 'date'}
cols_man: dict = {'numtotal_dose1_admin': 'total_vaccinations', 'numtotal_dose2_admin': 'total_vaccinations', 'numtotal_dose3_admin': 'total_vaccinations', 'numtotal_dose4_admin': 'total_vaccinations', 'numtotal_dose5+_admin': 'total_vaccinations', 'numtotal_dosenotreported_admin': 'total_vaccinations', 'product_name': 'vaccine', 'week_end': 'date'}
export()[source]
location: str = 'Canada'
max_filtered_dates: int = 3
max_removed_rows: int = 22
pipe_filter_dp(df: DataFrame) DataFrame[source]
pipe_get_totals(df: DataFrame) DataFrame[source]
pipe_make_monotonic(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_vaccine_timeline(df: DataFrame, df_man: DataFrame) DataFrame[source]
pipeline(df: DataFrame, df_man: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
read_age() DataFrame[source]
read_manufacturer() DataFrame[source]
source_name: str = 'Public Health Agency of Canada'
source_url: str = 'https://api.covid19tracker.ca/reports'
source_url_a: str = 'https://health-infobase.canada.ca/src/data/covidLive/vaccination-coverage-byAgeAndSex-overTimeDownload.csv'
source_url_age: str = 'https://health-infobase.canada.ca/covid-19/vaccination-coverage/'
source_url_m: str = 'https://health-infobase.canada.ca/src/data/covidLive/vaccination-administration-bydosenumber2.csv'
source_url_man: str = 'https://health-infobase.canada.ca/covid-19/vaccine-administration/'
source_url_ref: str = 'https://covid19tracker.ca/vaccinationtracker.html'
vaccine_mapping: dict = {'AstraZeneca Vaxzevria/COVISHIELD': 'Oxford/AstraZeneca', 'Janssen': 'Johnson&Johnson', 'Janssen Jcovden': 'Johnson&Johnson', 'Medicago Covifenz': 'Medicago', 'Moderna Spikevax': 'Moderna', 'Moderna Spikevax (ages 6 months-5 years)': 'Moderna', 'Not reported': None, 'Novavax': 'Novavax', 'Novavax Nuvaxovid': 'Novavax', 'Pfizer-BioNTech Comirnaty': 'Pfizer/BioNTech', 'Pfizer-BioNTech Comirnaty (ages 12 years and older)': 'Pfizer/BioNTech', 'Pfizer-BioNTech Comirnaty (ages 5-11 years)': 'Pfizer/BioNTech', 'Pfizer-BioNTech Comirnaty pediatric 5-11 years': 'Pfizer/BioNTech', 'Total': None, 'Unknown': None}
cowidev.vax.batch.canada.main()[source]

cowidev.vax.batch.chile

class cowidev.vax.batch.chile.Chile[source]

Bases: CountryVaxBase

export()[source]
pipe_add_metadata(df: DataFrame) DataFrame[source]
pipe_add_vaccine_list(df: DataFrame) DataFrame[source]
pipe_calculate_metrics(df: DataFrame) DataFrame[source]
pipe_exclude_total(df: DataFrame, colname: str) DataFrame[source]
pipe_keep_total(df: DataFrame, colname: str) DataFrame[source]
pipe_melt(df: DataFrame, id_vars: list) DataFrame[source]
pipe_pivot(df: DataFrame, index: list) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_rename_vaccines(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
pipeline_vaccinations(df: DataFrame) DataFrame[source]
read(url: str) DataFrame[source]
save_vaccine_list(df: DataFrame) DataFrame[source]
cowidev.vax.batch.chile.main()[source]

cowidev.vax.batch.czechia

class cowidev.vax.batch.czechia.Czechia[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Czechia'
pipeline(df: DataFrame) DataFrame[source]
source_url = 'https://onemocneni-aktualne.mzcr.cz/api/v2/covid-19/ockovani.csv'
cowidev.vax.batch.czechia.aggregate_by_date(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.aggregate_by_date_vaccine(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.base_pipeline(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.breakdown_per_vaccine(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.check_vaccine_names(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.enrich_cumulated_sums(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.enrich_location(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.enrich_metadata(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.enrich_source(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.format_date(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.infer_one_dose_vaccines(df: DataFrame) DataFrame[source]
cowidev.vax.batch.czechia.main()[source]
cowidev.vax.batch.czechia.read(source: str) DataFrame[source]
cowidev.vax.batch.czechia.remove_vaccines(df: DataFrame, vaccine_schedule: dict) dict[source]
cowidev.vax.batch.czechia.translate_vaccine_names(df: DataFrame) DataFrame[source]

cowidev.vax.batch.denmark

class cowidev.vax.batch.denmark.Denmark[source]

Bases: CountryVaxBase

_download_and_extract_data(url, output_path)[source]
_get_num_gap_days(df_current)[source]
_load_data(path)[source]

Get link to latest pdf.

_read_data(path) DataFrame[source]
_read_single_shots_bfill(index=None, date_limit=None)[source]

Read single shots using bfill (iterates over old links)

_read_single_shots_daily(path) dict[source]
date_limit_one_dose = '2021-05-27'
property date_limit_one_dose_ddmmyyyy
export()[source]
location: str = 'Denmark'
pipe_metrics(df: DataFrame, df_current: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame, df_current: DataFrame) DataFrame[source]
read(gap_days, bfill=True) DataFrame[source]
read_current()[source]
regions_accepted = {'Hovedstaden', 'Midtjylland', 'Nordjylland', 'Sjælland', 'Syddanmark'}
source_url_ref = 'https://covid19.ssi.dk/overvagningsdata/download-fil-med-vaccinationsdata'
vaccines_mapping = {'AstraZeneca Covid-19 vaccine': 'Oxford/AstraZeneca', 'Janssen COVID-19 vaccine': 'Johnson&Johnson', 'Moderna Covid-19 Vaccine': 'Moderna', 'Moderna/Spikevax Covid-19 0,5 ml': 'Moderna', 'Moderna/Spikevax Covid-19 Vacc.': 'Moderna', 'Pfizer BioNTech Covid-19 vacc': 'Pfizer/BioNTech', 'Pfizer/Comirnaty Original/Omikron BA1': 'Pfizer/BioNTech'}
cowidev.vax.batch.denmark._build_filepath(path, filename)[source]

Build filepath.

cowidev.vax.batch.denmark._load_datafile(path)[source]

Read csv file.

cowidev.vax.batch.denmark.main()[source]

cowidev.vax.batch.ecdc

class cowidev.vax.batch.ecdc.ECDC[source]

Bases: CountryVaxBase

_filter_age_targetgroup(df_c: DataFrame)[source]
_load_country_mapping(iso_path: str)[source]
_vaccine_timeseries(df: DataFrame)[source]

Get Series with the vaccine timeseries for all countries.

Format:

location -> {vaccine_1: start_date_1, vaccine_2: start_date_2, …}

_weekday_to_date(d)[source]
property country_mapping
export()[source]
export_age(df: DataFrame)[source]
export_main(df: DataFrame)[source]
export_manufacturer(df: DataFrame)[source]
location: str = 'ECDC'
pipe_age_checks(df: DataFrame) DataFrame[source]
pipe_age_filter_entries(df: DataFrame) DataFrame[source]

More granular filter. Keep entries where data is deemed reliable.

  1. Checks field ALL is equal to sum of all other ages (within 5% error). If not filters rows out.

  2. If percentage of unknown doses is above 5% of total doses, filters row out.

pipe_age_filter_locations(df: DataFrame) DataFrame[source]

Filter locations and keep only valid ones.

Validity is defined as a country having all age groups defined by AGE_GROUPS_MUST_HAVE.

pipe_age_groups(df: DataFrame) DataFrame[source]

Build age groups.

pipe_age_relative_metrics(df: DataFrame, df_og: DataFrame) DataFrame[source]
pipe_base(df: DataFrame) DataFrame[source]
pipe_cumsum(df: DataFrame, group_field_renamed: str | None = None) DataFrame[source]
pipe_filter_locations(df: DataFrame)[source]

Filters countries to be excluded and those with a high number of

pipe_filter_targetgroup(df: DataFrame)[source]
pipe_group(df: DataFrame, group_field: str | None = None, group_field_renamed: str | None = None) DataFrame[source]
pipe_initial_check(df: DataFrame) DataFrame[source]
pipe_manufacturer_filter_entries(df: DataFrame)[source]
pipe_manufacturer_filter_locations(df: DataFrame)[source]

Filters countries to be excluded and those with a high number of unknown doses.

pipe_rename_vaccines(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame, vax_timeline)[source]
pipeline(df: DataFrame)[source]
pipeline_age(df: DataFrame)[source]
pipeline_common(df: DataFrame, group_field: str | None = None, group_field_renamed: str | None = None) DataFrame[source]
pipeline_manufacturer(df: DataFrame)[source]
read()[source]
source_url = 'https://opendata.ecdc.europa.eu/covid19/vaccine_tracker/csv/data.csv'
source_url_ref = 'https://www.ecdc.europa.eu/en/publications-data/data-covid-19-vaccination-eu-eea'
vaccine_mapping = {'AZ': 'Oxford/AstraZeneca', 'BECNBG': 'Sinopharm/Beijing', 'BHACOV': 'Covaxin', 'COM': 'Pfizer/BioNTech', 'COMBA.1': 'Pfizer/BioNTech', 'JANSS': 'Johnson&Johnson', 'MOD': 'Moderna', 'MODBA.1': 'Moderna', 'NVX': 'Novavax', 'NVXD': 'Novavax', 'SIN': 'Sinovac', 'SPU': 'Sputnik V', 'UNK': 'Unknown'}
cowidev.vax.batch.ecdc.main()[source]

cowidev.vax.batch.ecuador

class cowidev.vax.batch.ecuador.Ecuador[source]

Bases: CountryVaxBase

columns_rename = {'dosis_total': 'total_vaccinations', 'dosis_unica': 'single_shots', 'fecha': 'date', 'primera_dosis': 'people_vaccinated', 'refuerzo_1': 'boosters_1', 'refuerzo_2': 'boosters_2', 'segunda_dosis': 'people_fully_vaccinated'}
columns_rename_manuf = {'administered_at': 'date', 'dosis_total': 'total_vaccinations', 'fabricante': 'vaccine'}
export()[source]
location: str = 'Ecuador'
pipe_checks(df: DataFrame) DataFrame[source]
pipe_column_rename(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_exclude_dp(df: DataFrame) DataFrame[source]
pipe_manuf_aggregate(df: DataFrame) DataFrame[source]
pipe_manuf_date(df: DataFrame) DataFrame[source]
pipe_manuf_rename_cols(df: DataFrame) DataFrame[source]
pipe_manuf_vaccine_checks(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_vaccines(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
read_manuf() DataFrame[source]
source_url = {'main': 'https://github.com/andrab/ecuacovid/raw/master/datos_crudos/vacunas/vacunas.csv', 'manufacturer': 'https://github.com/andrab/ecuacovid/raw/master/datos_crudos/vacunometro/fabricantes.csv'}
source_url_ref = 'https://github.com/andrab/ecuacovid'
vaccine_mapping = {'CanSino': 'CanSino', 'Oxford/AstraZeneca': 'Oxford/AstraZeneca', 'Pfizer/BioNTech': 'Pfizer/BioNTech', 'Sinovac': 'Sinovac'}
vax_timeline = {'CanSino': '2021-08-03', 'Oxford/AstraZeneca': '2021-03-17', 'Pfizer/BioNTech': '2020-12-01', 'Sinovac': '2021-03-06'}
cowidev.vax.batch.ecuador.main()[source]

cowidev.vax.batch.estonia

class cowidev.vax.batch.estonia.Estonia[source]

Bases: CountryVaxBase

_parse_data() DataFrame[source]
_parse_metric(df, series, measurement, metric_name) DataFrame[source]
export()[source]
location: str = 'Estonia'
pipe_location(df: DataFrame) DataFrame[source]
pipe_source(df: DataFrame) DataFrame[source]
pipe_vaccine_name(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://opendata.digilugu.ee/covid19/vaccination/v3/opendata_covid19_vaccination_total.csv'
source_url_ref: str = 'https://opendata.digilugu.ee'
cowidev.vax.batch.estonia.main()[source]

cowidev.vax.batch.france

class cowidev.vax.batch.france.France[source]

Bases: CountryVaxBase

export()[source]
location: str = 'France'
source_name = 'Public Health France'
source_url = 'https://www.data.gouv.fr/fr/datasets/r/b273cf3b-e9de-437c-af55-eda5979e92fc'
source_url_ref = 'https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1/'
cowidev.vax.batch.france.main()[source]

cowidev.vax.batch.germany

class cowidev.vax.batch.germany.Germany[source]

Bases: CountryVaxBase

_check_vaccines(df: DataFrame)[source]

Get vaccine columns mapped to Vaccine names.

_vaccine_start_dates(df: DataFrame)[source]
calculate_metrics(df: DataFrame) DataFrame[source]
columns_rename: str = {'impfungen_boost1_kumulativ': 'total_boosters', 'impfungen_boost2_kumulativ': 'total_boosters_2', 'impfungen_kumulativ': 'total_vaccinations', 'personen_gi_kumulativ': 'people_fully_vaccinated', 'personen_min1_kumulativ': 'people_vaccinated'}
enrich_location(df: DataFrame) DataFrame[source]
enrich_source(df: DataFrame) DataFrame[source]
enrich_vaccine(df: DataFrame) DataFrame[source]
export()[source]
fully_vaccinated_mapping: str = {'impfungen_astra_gi_kumulativ': 'full_astra', 'impfungen_biontech_gi_kumulativ': 'full_biontech', 'impfungen_johnson_gi_kumulativ': 'full_jj', 'impfungen_moderna_gi_kumulativ': 'full_moderna', 'impfungen_novavax_gi_kumulativ': 'full_nova'}
location: str = 'Germany'
melt_manufacturers(df: DataFrame) DataFrame[source]
pipe_sanity_checks(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
regex_doses_colnames: str = 'impfungen_([a-zA-Z]*)_kumulativ'
select_output_columns(df: DataFrame) DataFrame[source]
source_url: str = 'https://impfdashboard.de/static/data/germany_vaccinations_timeseries_v3.tsv'
source_url_ref: str = 'https://impfdashboard.de/'
translate_columns(df: DataFrame) DataFrame[source]
translate_vaccine_columns(df: DataFrame) DataFrame[source]
vaccine_mapping: str = {'impfungen_astra_kumulativ': 'Oxford/AstraZeneca', 'impfungen_biontech_kumulativ': 'Pfizer/BioNTech', 'impfungen_johnson_kumulativ': 'Johnson&Johnson', 'impfungen_moderna_kumulativ': 'Moderna', 'impfungen_novavax_kumulativ': 'Novavax', 'impfungen_valneva_kumulativ': 'Valneva'}
cowidev.vax.batch.germany.main()[source]

cowidev.vax.batch.greece

class cowidev.vax.batch.greece.Greece[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Greece'
pipe_date(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url = 'https://www.data.gov.gr/api/v1/query/mdg_emvolio?date_from=2020-12-28'
source_url_ref = 'https://www.data.gov.gr/datasets/mdg_emvolio/'
token = 'b1ef5949bebace574a0d7e58b5cdf4018353121e'
cowidev.vax.batch.greece.main()[source]

cowidev.vax.batch.hong_kong

class cowidev.vax.batch.hong_kong.HongKong[source]

Bases: CountryVaxBase

age_valid = {'0-11': '0-19', '12-19': '0-19', '20-29': '20-29', '30-39': '30-39', '40-49': '40-49', '50-59': '50-59', '60-69': '60-69', '70-79': '70-79', '80 and above': '80-'}
export()[source]
location: str = 'Hong Kong'
pipe_add_metadata(df: DataFrame) DataFrame[source]
pipe_add_vaccines(df: DataFrame) DataFrame[source]
pipe_age_agg(df: DataFrame)[source]
pipe_age_checks(df: DataFrame)[source]
pipe_age_filter(df: DataFrame)[source]
pipe_age_groups(df: DataFrame)[source]
pipe_age_pivot(df: DataFrame)[source]
pipe_calculate_metrics(df: DataFrame) DataFrame[source]
pipe_filter_dp(df: DataFrame) DataFrame[source]
pipe_reshape(df: DataFrame) DataFrame[source]
pipe_sum_manufacturer(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = ' https://www.fhb.gov.hk/download/opendata/COVID19/vaccination-rates-over-time-by-age.csv'
source_url_ref: str = 'https://data.gov.hk/en-data/dataset/hk-fhb-fhbcovid19-vaccination-rates-over-time-by-age'
vaccine_mapping: dict = {'BioNTech': 'Pfizer/BioNTech', 'Sinovac': 'Sinovac'}
vaccines_valid = ['Sinovac', 'BioNTech']
cowidev.vax.batch.hong_kong.main()[source]

cowidev.vax.batch.indonesia

class cowidev.vax.batch.indonesia.Indonesia[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Indonesia'
pipe_add_latest_boosters(df: DataFrame) DataFrame[source]
pipe_merge_current(df: DataFrame, df_current: DataFrame) DataFrame[source]
pipe_merge_legacy(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame, df_current: DataFrame) DataFrame[source]
read() DataFrame[source]
read_current()[source]
source_url = 'https://data.covid19.go.id/public/api/pemeriksaan-vaksinasi.json'
source_url_ref = 'https://data.covid19.go.id/public/index.html'
cowidev.vax.batch.indonesia.main()[source]

cowidev.vax.batch.ireland

class cowidev.vax.batch.ireland.Ireland[source]

Bases: CountryVaxBase

_parse_data_boosters(data: dict) int[source]
_parse_data_primary(data: dict) int[source]
export()[source]
location: str = 'Ireland'
pipe_date(df: DataFrame) DataFrame[source]
pipe_filter(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) str[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url = {'booster': 'https://services-eu1.arcgis.com/z6bHNio59iTqqSUY/arcgis/rest/services/COVID19_HSE_vaccine_booster_dose_daily/FeatureServer/0/query', 'primary': 'https://services-eu1.arcgis.com/z6bHNio59iTqqSUY/arcgis/rest/services/COVID19_Daily_Vaccination/FeatureServer/0/query'}
source_url_ref = 'https://covid19ireland-geohive.hub.arcgis.com/'
cowidev.vax.batch.ireland.main()[source]

cowidev.vax.batch.israel

class cowidev.vax.batch.israel.Israel[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Israel'
pipe_date(df: DataFrame) DataFrame[source]
pipe_filter_date(df: DataFrame) DataFrame[source]
pipe_location(df: DataFrame) DataFrame[source]
pipe_nulls_as_nans(df: DataFrame) DataFrame[source]
pipe_output_columns(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_select_min_date(df: DataFrame) DataFrame[source]
pipe_source(df: DataFrame) DataFrame[source]
pipe_total_boosters(df: DataFrame) DataFrame[source]
pipe_total_vaccinations(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df)[source]
read() DataFrame[source]
read_age()[source]
source_url: str = 'https://datadashboardapi.health.gov.il/api/queries/vaccinated'
source_url_age: str = 'https://github.com/dancarmoz/israel_moh_covid_dashboard_data/raw/master/vaccinated_by_age.csv'
source_url_age_old = 'https://github.com/dancarmoz/israel_moh_covid_dashboard_data/raw/master/old_files/vaccinated_by_age_2022_01_25.csv'
source_url_ref: str = 'https://datadashboard.health.gov.il/COVID-19/general'
cowidev.vax.batch.israel.main()[source]

cowidev.vax.batch.italy

class cowidev.vax.batch.italy.Italy[source]

Bases: CountryVaxBase

_check_vaccines(df: DataFrame) DataFrame[source]
columns: list = ['data', 'forn', 'eta', 'd1', 'd2', 'dpi', 'db1', 'db2']
columns_rename: dict = {'data': 'date', 'eta': 'age_group', 'forn': 'vaccine'}
enrich_location(df: DataFrame) DataFrame[source]
enrich_source(df: DataFrame) DataFrame[source]
enrich_vaccine(df: DataFrame) DataFrame[source]
export() None[source]
get_final_numbers(df: DataFrame) DataFrame[source]
get_people_fully_vaccinated(df: DataFrame) DataFrame[source]
get_people_vaccinated(df: DataFrame) DataFrame[source]
get_total_vaccinations(df: DataFrame) DataFrame[source]
get_total_vaccinations_by_manufacturer(df: DataFrame) DataFrame[source]
location: str = 'Italy'
one_dose_vaccines: list = ['Johnson&Johnson']
pipeline(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
rename_columns(df: DataFrame) DataFrame[source]
source_url: str = 'https://raw.githubusercontent.com/italia/covid19-opendata-vaccini/master/dati/somministrazioni-vaccini-latest.csv'
translate_vaccine_columns(df: DataFrame) DataFrame[source]
vaccine_mapping: dict = {'Janssen': 'Johnson&Johnson', 'Moderna': 'Moderna', 'ND': 'unknown', 'Novavax': 'Novavax', 'Pfizer Pediatrico': 'Pfizer/BioNTech', 'Pfizer/BioNTech': 'Pfizer/BioNTech', 'Vaxzevria (AstraZeneca)': 'Oxford/AstraZeneca'}
vaccine_start_dates(df: DataFrame) List[Tuple[str, str]][source]
vax_date_mapping = None
cowidev.vax.batch.italy.main()[source]

cowidev.vax.batch.japan

class cowidev.vax.batch.japan.Japan[source]

Bases: CountryVaxBase

_parse_df(df: DataFrame, date_col: str, ind: list, metrics: dict) DataFrame[source]
_read_xlsx(url: str, sheets: dict, metrics: dict) dict[source]
age_group_remain: str = '12-64'
age_groups: dict = {'5-11': ['うち小児接種'], '65-': ['うち高齢者'], 'all': ['すべて']}
age_groups_bst: dict = {'5-11': ['うち小児接種'], '65-': ['うち高齢者'], 'all': ['すべて']}
cols_early: dict = {'内1回目': 'dose1', '内2回目': 'dose2', '日付': 'date'}
export()[source]
location: str = 'Japan'
metrics: dict = {'dose1': ['内1回目'], 'dose2': ['内2回目']}
metrics_age: dict = {'dose1': 'people_vaccinated', 'dose2': 'people_fully_vaccinated', 'dose3': 'people_with_booster'}
metrics_bst: dict = {'dose3': []}
metrics_bst2: dict = {'dose4': []}
pipe_aggregate(df: DataFrame) DataFrame[source]
pipe_early(df: DataFrame) DataFrame[source]
pipe_latest(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
read_early() DataFrame[source]
read_latest() DataFrame[source]
sheets: dict = {'一般接種': {'date': '接種日', 'header': [2, 3, 4], 'ind': {'5-11': ['うち小児接種'], '65-': ['うち高齢者'], 'all': ['すべて']}, 'name': 'general'}, '医療従事者等': {'date': '集計日', 'header': [2, 3], 'ind': [], 'name': 'healthcare'}, '総接種回数': None, '職域接種': {'date': '集計日', 'header': [2, 3], 'ind': [], 'name': 'workplace'}, '重複': {'date': '公表日', 'header': [2, 3], 'ind': [], 'name': 'overlap'}}
sheets_bst: dict = {'一般接種': {'date': '接種日', 'header': [1, 2], 'ind': {'5-11': ['うち小児接種'], '65-': ['うち高齢者'], 'all': ['すべて']}, 'name': 'general'}, '総接種回数': None, '職域接種': {'date': '集計日', 'header': [2, 3], 'ind': ['接種回数'], 'name': 'workplace'}, '重複': {'date': '公表日', 'header': [2, 3], 'ind': ['接種回数'], 'name': 'overlap'}}
sheets_bst2: dict = {'一般接種': {'date': '接種日', 'header': [1, 2, 3, 4], 'ind': ['曜日', 'すべて', ''], 'name': 'general'}, '総接種回数': None}
source_name: str = "Prime Minister's Office"
source_url: str = 'https://www.kantei.go.jp/jp/content/vaccination_data5.xlsx'
source_url_bst: str = 'https://www.kantei.go.jp/jp/content/booster_data.xlsx'
source_url_bst2: str = 'https://www.kantei.go.jp/jp/content/booster2nd_data.xlsx'
source_url_early: str = 'https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/vaccine_sesshujisseki.html'
source_url_ref: str = 'https://www.kantei.go.jp/jp/headline/kansensho/vaccine.html'
vaccine_mapping: dict = {'アストラゼネカ社': 'Oxford/AstraZeneca', 'ファイザー社': 'Pfizer/BioNTech', 'モデルナ社': 'Moderna', '接種回数(合計)': None, '武田社(ノババックス)': 'Novavax'}
cowidev.vax.batch.japan.main()[source]

cowidev.vax.batch.jersey

class cowidev.vax.batch.jersey.Jersey[source]

Bases: CountryVaxBase

_extract_age_group(age_group_raw)[source]
export()[source]

Generalized.

pipe_age_create_groups(df: DataFrame) DataFrame[source]
pipe_age_filter(df: DataFrame) DataFrame[source]
pipe_age_fix_dp(df: DataFrame) DataFrame[source]
pipe_age_minmax_values(df: DataFrame) DataFrame[source]
pipe_age_rename_columns(df: DataFrame) DataFrame[source]
pipe_age_select_columns(df: DataFrame) DataFrame[source]
pipe_enrich_columns(df: DataFrame) DataFrame[source]
pipe_enrich_vaccine_name(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_metrics_scale_100(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_select_columns(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
read()[source]
cowidev.vax.batch.jersey.main()[source]

cowidev.vax.batch.latvia

class cowidev.vax.batch.latvia.Latvia[source]

Bases: CountryVaxBase

_read_one(url: str)[source]
_remove_vaccines(df, approval_timeline)[source]
export()[source]
location: str = 'Latvia'
pipe_aggregate(df: DataFrame) DataFrame[source]
pipe_base(df: DataFrame) DataFrame[source]
pipe_cumsum(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_pivot(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
source_page = 'https://data.gov.lv/dati/eng/dataset/covid19-vakcinacijas'
source_url_1 = 'https://data.gov.lv/dati/datastore/dump/51725018-49f3-40d1-9280-2b13219e026f'
source_url_2 = 'https://data.gov.lv/dati/datastore/dump/9320d913-a4a2-4172-b521-73e58c2cfe83'
cowidev.vax.batch.latvia.main()[source]

cowidev.vax.batch.lithuania

class cowidev.vax.batch.lithuania.Lithuania[source]

Bases: CountryVaxBase

_find_vaccines(date)[source]
export()[source]
location: str = 'Lithuania'
pipe_add_vaccines(df: DataFrame) DataFrame[source]
pipe_clean_coverage(df: DataFrame) DataFrame[source]
pipe_clean_doses(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_parse_dates(df: DataFrame) DataFrame[source]
query_params_coverage: dict = {'f': 'json', 'outFields': 'date,vaccination_state,all_cum', 'resultOffset': 0, 'resultRecordCount': 32000, 'resultType': 'standard', 'returnGeometry': False, 'spatialRel': 'esriSpatialRelIntersects', 'where': "municipality_code='00' AND vaccination_state<>'01dalinai'"}
query_params_doses: dict = {'f': 'json', 'outFields': 'date,vaccines_used_cum,vaccine_name', 'resultOffset': 0, 'resultRecordCount': 32000, 'resultType': 'standard', 'returnGeometry': False, 'spatialRel': 'esriSpatialRelIntersects', 'where': "municipality_code='00'"}
read(url, params)[source]
source_url_coverage: str = 'https://services3.arcgis.com/MF53hRPmwfLccHCj/arcgis/rest/services/covid_vaccinations_chart_new/FeatureServer/0/query'
source_url_doses: str = 'https://services3.arcgis.com/MF53hRPmwfLccHCj/arcgis/rest/services/covid_vaccinations_by_drug_name_new/FeatureServer/0/query'
source_url_ref: str = 'https://experience.arcgis.com/experience/cab84dcfe0464c2a8050a78f817924ca/page/page_3/'
vaccine_mapping = {'AstraZeneca': 'Oxford/AstraZeneca', 'Johnson & Johnson': 'Johnson&Johnson', 'Moderna': 'Moderna', 'Novavax': 'Novavax', 'Pfizer-BioNTech': 'Pfizer/BioNTech', 'Pfizer-BioNTech BA.1': 'Pfizer/BioNTech', 'Pfizer-BioNTech BA.4-5': 'Pfizer/BioNTech'}
cowidev.vax.batch.lithuania.main()[source]

cowidev.vax.batch.luxembourg

class cowidev.vax.batch.luxembourg.Luxembourg[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Luxembourg'
pipe_correct_time_series(df: DataFrame) DataFrame[source]

Since 2021-04-14 Luxembourg is using J&J, therefore dose2 == people_fully_vaccinated no longer works. As a temporary fix while they report the necessary data, we’re inserting one PDF report to avoid showing an old value for people_fully_vaccinated in dashboard that re-use our latest totals without showing how old they are. The publisher was contacted on 2021-O9-21 https://twitter.com/redouad/status/1439992459166158857

pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_running_totals(df: DataFrame) DataFrame[source]
pipe_vaccines(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url = 'https://data.public.lu/en/datasets/r/af7cd843-dfe5-440a-9ab2-d22ffef8844c'
source_url_ref = 'https://data.public.lu/en/datasets/donnees-covid19/#_'
cowidev.vax.batch.luxembourg.main()[source]

cowidev.vax.batch.malaysia

class cowidev.vax.batch.malaysia.Malaysia[source]

Bases: CountryVaxBase

_vax_1d = ['cansino']
_vax_2d = ['pfizer', 'astra', 'sinovac', 'sinopharm', 'pending']
export()[source]
location: str = 'Malaysia'
pipe_calculate_metrics(df: DataFrame) DataFrame[source]
pipe_check_columns(df: DataFrame) DataFrame[source]
pipe_columns_out(df: DataFrame) DataFrame[source]
pipe_filter_columns(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url = 'https://github.com/MoH-Malaysia/covid19-public/raw/main/vaccination/vax_malaysia.csv'
source_url_ref = 'https://github.com/MoH-Malaysia/covid19-public'
cowidev.vax.batch.malaysia.main()[source]

cowidev.vax.batch.malta

class cowidev.vax.batch.malta.Malta[source]

Bases: CountryVaxBase

columns_rename: dict = {'Date of Vaccination': 'date', 'Fully vaccinated (2 of 2 or 1 of 1)': 'people_fully_vaccinated', 'Received one dose (1 of 2 or 1 of 1)': 'people_vaccinated', 'Total 2nd Booster doses': 'total_boosters_2', 'Total Booster doses': 'total_boosters', 'Total Vaccination Doses': 'total_vaccinations'}
export()[source]
location: str = 'Malta'
pipe_check_columns(df: DataFrame) DataFrame[source]
pipe_correct_data(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_exclude_data_points(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://github.com/COVID19-Malta/COVID19-Cases/raw/master/COVID-19%20Malta%20-%20Vaccination%20Data.csv'
source_url_ref: str = 'https://github.com/COVID19-Malta/COVID19-Cases'
cowidev.vax.batch.malta.main()[source]

cowidev.vax.batch.netherlands

class cowidev.vax.batch.netherlands.Netherlands[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Netherlands'
pipe_date(df: DataFrame) DataFrame[source]
pipe_filter_rows(df: DataFrame) DataFrame[source]
pipe_get_vax_timeline(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_metrics_aggregate(df: DataFrame) DataFrame[source]
pipe_metrics_cumsum(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read()[source]
source_url: str = 'https://github.com/mzelst/covid-19/raw/master/data-rivm/vaccines-ecdc/vaccines_administered_nl.csv'
source_url_ref = 'https://github.com/mzelst/covid-19'
vaccines_mapping: dict = {'Johnson&Johnson': 'Johnson&Johnson', 'Moderna': 'Moderna', 'NVXD': 'Novavax', 'Oxford/AstraZeneca': 'Oxford/AstraZeneca', 'Pfizer/BioNTech': 'Pfizer/BioNTech'}
vax_timeline: dict = None
cowidev.vax.batch.netherlands.main()[source]

cowidev.vax.batch.new_zealand

class cowidev.vax.batch.new_zealand.NewZealand[source]

Bases: CountryVaxBase

Parses the link from the soup.

_read_latest(soup)[source]

Reads the latest data from the soup.

base_url = 'https://www.health.govt.nz'
columns_cumsum = ['people_vaccinated', 'people_fully_vaccinated', 'third_dose', 'total_boosters', 'total_boosters_2']
export()[source]

Exports the data to CSV

location: str = 'New Zealand'
pipe_boosters(df: DataFrame) DataFrame[source]

Calculates the total boosters.

pipe_cumsum(df: DataFrame) DataFrame[source]

Calculates cumulative sum of the columns.

pipe_date(df: DataFrame) DataFrame[source]

Formats the date column.

pipe_latest_metrics(df: DataFrame) DataFrame[source]

pipes the latest metrics.

pipe_total_vaccinations(df: DataFrame) DataFrame[source]

Calculates the total vaccinations.

pipe_vaccine(df: DataFrame) DataFrame[source]

Builds the vaccine timeline.

pipeline(df: DataFrame) DataFrame[source]

Pipeline for the data

read() DataFrame[source]

Reads the data from the source.

rename_columns = {'Date': 'date', 'First Boosters': 'total_boosters', 'First doses': 'people_vaccinated', 'Second Boosters': 'total_boosters_2', 'Second doses': 'people_fully_vaccinated', 'Third primary doses': 'third_dose'}
source_url_ref = 'https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-vaccine-data'
vaccines_start_date = {'Novavax': '2022-03-14', 'Oxford/AstraZeneca': '2021-11-26', 'Pfizer/BioNTech': '2021-01-01'}
cowidev.vax.batch.new_zealand.main()[source]

cowidev.vax.batch.norway

class cowidev.vax.batch.norway.Norway[source]

Bases: CountryVaxBase

export()[source]
pipe_filter_rows(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read()[source]
cowidev.vax.batch.norway.main()[source]

cowidev.vax.batch.peru

class cowidev.vax.batch.peru.Peru[source]

Bases: CountryVaxBase

date_start = '2021-02-08'
export()[source]
location: str = 'Peru'
pipe_age_checks(df: DataFrame) DataFrame[source]
pipe_age_columns_out(df: DataFrame) DataFrame[source]
pipe_age_date(df: DataFrame) DataFrame[source]
pipe_checks(df: DataFrame) DataFrame[source]
pipe_filter_only_campaign(df: DataFrame) DataFrame[source]
pipe_format(df: DataFrame) DataFrame[source]
pipe_get_vax_timeline(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_total_vaccinations(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
read_age()[source]
read_manufacturer()[source]
source_url = 'https://github.com/jmcastagnetto/covid-19-peru-vacunas/raw/main/datos/vacunas_covid_resumen.csv'
source_url_age = 'https://github.com/jmcastagnetto/covid-19-peru-vacunas/raw/main/datos/vacunas_covid_rangoedad_owid.csv'
source_url_manufacturer = 'https://github.com/jmcastagnetto/covid-19-peru-vacunas/raw/main/datos/vacunas_covid_fabricante.csv'
source_url_ref = 'https://www.datosabiertos.gob.pe/dataset/vacunacion'
vaccine_mapping = {'ASTRAZENECA': 'Oxford/AstraZeneca', 'MODERNA': 'Moderna', 'PFIZER': 'Pfizer/BioNTech', 'SINOPHARM': 'Sinopharm/Beijing'}
vax_timeline = None
cowidev.vax.batch.peru.main()[source]

cowidev.vax.batch.portugal

class cowidev.vax.batch.portugal.Portugal[source]

Bases: CountryVaxBase

_pipe_metrics() DataFrame[source]
columns_rename: dict = {'data': 'date', 'pessoas_inoculadas': 'people_vaccinated', 'pessoas_reforço': 'total_boosters', 'pessoas_vacinadas_completamente': 'people_fully_vaccinated', 'vacinas': 'total_vaccinations'}
export()[source]
location: str = 'Portugal'
pipe_columns_out(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_dropna(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_sanity_checks(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://github.com/dssg-pt/covid19pt-data/raw/master/vacinas.csv'
source_url_ref: str = 'https://github.com/dssg-pt/covid19pt-data'
cowidev.vax.batch.portugal.add_boosters(df: DataFrame) DataFrame[source]
cowidev.vax.batch.portugal.main()[source]

cowidev.vax.batch.romania

class cowidev.vax.batch.romania.Romania[source]

Bases: CountryVaxBase

columns_rename: dict = {'immunized': 'people_fully_vaccinated', 'total_administered': 'total_vaccinations'}
export()[source]
location: str = 'Romania'
pipe_filter_rows_columns(df: DataFrame) DataFrame[source]
pipe_location(df: DataFrame) DataFrame[source]
pipe_manufacturer_cumsum(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_source(df: DataFrame) DataFrame[source]
pipe_store_timeline(df: DataFrame) DataFrame[source]
pipe_sum_vaccines(df: DataFrame) DataFrame[source]
pipe_unnest_data(df: DataFrame) DataFrame[source]
pipe_vaccines(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://d35p9e4fm9h3wo.cloudfront.net/latestData.json'
source_url_ref: str = 'https://datelazi.ro/'
vaccine_mapping: dict = {'astra_zeneca': 'Oxford/AstraZeneca', 'johnson_and_johnson': 'Johnson&Johnson', 'moderna': 'Moderna', 'pfizer': 'Pfizer/BioNTech', 'pfizer_pediatric': 'Pfizer/BioNTech'}
cowidev.vax.batch.romania.main()[source]

cowidev.vax.batch.saudi_arabia

class cowidev.vax.batch.saudi_arabia.SaudiArabia[source]

Bases: CountryVaxBase

export()[source]
location: str = 'Saudi Arabia'
pipeline(df: DataFrame)[source]
read()[source]
source_url = 'https://services6.arcgis.com/bKYAIlQgwHslVRaK/arcgis/rest/services/Vaccination_Individual_Total/FeatureServer/0/query?f=json&cacheHint=true&outFields=*&resultType=standard&returnGeometry=false&spatialRel=esriSpatialRelIntersects&where=1%3D1'
source_url_ref = 'https://covid19.moh.gov.sa/'
cowidev.vax.batch.saudi_arabia.main()[source]

cowidev.vax.batch.singapore

class cowidev.vax.batch.singapore.Singapore[source]

Bases: CountryVaxBase

_merge_primary_and_boosters(df_primary, df_boosters)[source]
export()[source]
pipe_filter_dp(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() str[source]
cowidev.vax.batch.singapore.main()[source]

cowidev.vax.batch.slovakia

class cowidev.vax.batch.slovakia.Slovakia[source]

Bases: CountryVaxBase

_week_to_date(row)[source]
date_start = datetime.datetime(2021, 1, 4, 0, 0)
export()[source]
location: str = 'Slovakia'
pipe_cumsum(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_out_columns(df: DataFrame) DataFrame[source]
pipe_reshape(df: DataFrame) DataFrame[source]
pipe_vaccine_checks(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read()[source]
source_url = 'https://github.com/Institut-Zdravotnych-Analyz/covid19-data/raw/main/Vaccination/OpenData_Slovakia_Vaccination_AgeGroup_District.csv'
source_url_ref = 'https://github.com/Institut-Zdravotnych-Analyz/covid19-data'
vaccine_mapping = {'ASTRAZENECA': 'Oxford/AstraZeneca', 'COMIRNATY': 'Pfizer/BioNTech', 'JANSSEN': 'Johnson&Johnson', 'MODERNA': 'Moderna', 'NUVAXOVID': 'Novavax', 'SPUTNIK': 'Sputnik V'}
vax_timeline = None
cowidev.vax.batch.slovakia.main()[source]

cowidev.vax.batch.slovenia

class cowidev.vax.batch.slovenia.Slovenia[source]

Bases: CountryVaxBase

_build_vaccine_str(d)[source]
_parse_data(data)[source]
export()[source]
location: str = 'Slovenia'
pipeline(df: DataFrame)[source]
read()[source]
source_url = 'https://api.sledilnik.org/api/vaccinations'
source_url_ref = 'https://covid-19.sledilnik.org/sl/stats'
vaccine_mapping = {'az': 'Oxford/AstraZeneca', 'janssen': 'Johnson&Johnson', 'moderna': 'Moderna', 'novavax': 'Novavax', 'pfizer': 'Pfizer/BioNTech'}
cowidev.vax.batch.slovenia.main()[source]

cowidev.vax.batch.south_korea

class cowidev.vax.batch.south_korea.SouthKorea[source]

Bases: CountryVaxBase

_check_format_multicols(df: DataFrame, columns_lv) DataFrame[source]
export()[source]
pipe_check_metrics(df: DataFrame)[source]
pipe_cumsum(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_man_aggregate(df: DataFrame) DataFrame[source]
pipe_man_melt(df: DataFrame) DataFrame[source]
pipe_metadata(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns_raw(df: DataFrame)[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_base(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
cowidev.vax.batch.south_korea.main()[source]

cowidev.vax.batch.spc

class cowidev.vax.batch.spc.SPC[source]

Bases: CountryVaxBase

_build_data_array(observations: dict, date_info: dict)[source]
_build_df(dix: dict, country: str)[source]
_build_df_list(data: dict)[source]
_parse_country_info(data: dict)[source]
_parse_date_info(data: dict)[source]
_parse_metrics_info(data: dict)[source]
_pretty_vaxdates(country, date_min)[source]
export()[source]
location: str = 'SPC'
parse_data(data: dict)[source]
pipe_drop_duplicates(df: DataFrame) DataFrame[source]
pipe_merge_boosters(df: DataFrame, country: str) DataFrame[source]

Adds the boosters data available in the csv.

pipe_merge_legacy(df: DataFrame, country: str) DataFrame[source]
pipe_vacine(df: DataFrame, country: str) DataFrame[source]
read()[source]
source_url = 'https://stats-nsi-stable.pacificdata.org/rest/data/SPC,DF_COVID_VACCINATION,1.0/D.CK+FJ+KI+NR+NC+PF+PG+PN+SB+TK+TO+VU+WF+WS.?startPeriod=2021-02-02&format=jsondata'
cowidev.vax.batch.spc.main()[source]

cowidev.vax.batch.sweden

class cowidev.vax.batch.sweden.Sweden[source]

Bases: CountryVaxBase

_get_df_teens_daily(df)[source]

[deprecated]

_merge_tables_daily(df_people, df_doses, df_boosters)[source]
_merge_tables_daily_split(df_adults, df_teens, df_doses)[source]

[deprecated

_read_daily_data() DataFrame[source]

Read daily data (latest) from HTML page.

_read_daily_data_age_split() DataFrame[source]

[deprecated] Read daily data (latest) from HTML page with two tables.

One table with adult numbers, the other one with teen numbers (12-15 yo).

_read_daily_data_boosters(df_1, df_2)[source]
_read_daily_data_doses(df)[source]
_read_daily_data_people(df)[source]
_read_weekly_data() DataFrame[source]

Read weekly data

This data is loaded from an excel. It contains very clean (but sparse, i.e. weekly) data.

_read_weekly_data_doses(dfs) DataFrame[source]

Read weekly data for number of vaccinations administered.

_read_weekly_data_people(dfs) DataFrame[source]

Read weekly data for number of vaccinated people.

export()[source]

Generalized.

merge_with_current_booster_data(output_path, df)[source]
pipe_add_boosters(df: DataFrame)[source]
pipe_columns(df: DataFrame) DataFrame[source]
pipe_out_columns(df: DataFrame)[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
cowidev.vax.batch.sweden.main()[source]

cowidev.vax.batch.switzerland

class cowidev.vax.batch.switzerland.Switzerland[source]

Bases: CountryVaxBase

_get_file_url() str[source]
_parse_data(doses_url, people_url, manufacturer_url)[source]
export()[source]
location: str = 'Switzerland'
pipe_age_checks(df)[source]
pipe_age_date(df)[source]
pipe_age_filter_region(df, geo_region)[source]
pipe_age_groups(df)[source]
pipe_age_location(df, location)[source]
pipe_age_pivot(df)[source]
pipe_age_rename_columns(df)[source]
pipe_age_select_cols(df)[source]
pipe_filter_country(df: DataFrame, country_code: str) DataFrame[source]
pipe_fix_metrics(df: DataFrame) DataFrame[source]
pipe_location(df: DataFrame, location: str) DataFrame[source]
pipe_pivot(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_source(df: DataFrame, country_code: str) DataFrame[source]
pipe_unique_rows(df: DataFrame)[source]
pipeline(df: DataFrame, location: str) DataFrame[source]
pipeline_age(df, location)[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
read_age()[source]
save_vaccine_timeline(df_manuf: DataFrame) DataFrame[source]
cowidev.vax.batch.switzerland._get_geo_region(location)[source]
cowidev.vax.batch.switzerland.main()[source]

cowidev.vax.batch.trinidad_and_tobago

class cowidev.vax.batch.trinidad_and_tobago.TrinidadTobago[source]

Bases: CountryVaxBase

_parse_data(data: dict) int[source]
export()[source]
location: str = 'Trinidad and Tobago'
pipe_checks(df: DataFrame) DataFrame[source]
pipe_data_correction(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_filter_dp(df: DataFrame) DataFrame[source]
pipe_legacy(df: DataFrame) DataFrame[source]
pipe_location(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_out_columns(df: DataFrame) DataFrame[source]
pipe_source(df: DataFrame) DataFrame[source]
pipe_vaccine_name(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source = 'https://services3.arcgis.com/x3I4DqUw3b3MfTwQ/arcgis/rest/services/service_7a519502598f492a9094fd0ad503cf80/FeatureServer/0/query'
source_ref = 'https://experience.arcgis.com/experience/59226cacd2b441c7a939dca13f832112/'
cowidev.vax.batch.trinidad_and_tobago.main()[source]

cowidev.vax.batch.ukraine

class cowidev.vax.batch.ukraine.Ukraine[source]

Bases: CountryVaxBase

_load_dose_data(dose_param, colname)[source]
export()[source]
location: str = 'Ukraine'
pipe_date(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
source_api_url: str = 'https://health-security.rnbo.gov.ua/api/vaccination/process/chart'
source_url: str = 'https://health-security.rnbo.gov.ua'
vaccines_mapping: dict = {'AstraZeneca': 'Oxford/AstraZeneca', 'Johnson & Johnson': 'Johnson&Johnson', 'Moderna': 'Moderna', 'Pfizer-BioNTech': 'Pfizer/BioNTech', 'Sinovac (CoronaVac)': 'Sinovac'}
cowidev.vax.batch.ukraine.main()[source]

cowidev.vax.batch.united_kingdom

class cowidev.vax.batch.united_kingdom.UnitedKingdom[source]

Bases: CountryVaxBase

_filter_location(df: DataFrame, location: str) DataFrame[source]
_read_metrics(filters)[source]
export()[source]
location: str = 'United Kingdom'
pipe_select_output_cols(df: DataFrame) DataFrame[source]
pipe_source_url(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read()[source]
source_url = 'https://coronavirus.data.gov.uk/details/vaccinations'
cowidev.vax.batch.united_kingdom.main()[source]

cowidev.vax.batch.united_states

class cowidev.vax.batch.united_states.UnitedStates[source]

Bases: CountryVaxBase

export()[source]
pipe_add_source(df: DataFrame) DataFrame[source]
pipe_add_vaccines(df: DataFrame) DataFrame[source]
pipe_clean_data(df: DataFrame) DataFrame[source]
pipe_filter_rows(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read() DataFrame[source]
read_manufacturer() DataFrame[source]
cowidev.vax.batch.united_states.main()[source]

cowidev.vax.batch.uruguay

class cowidev.vax.batch.uruguay.Uruguay[source]

Bases: CountryVaxBase

export()[source]
pipe_age_checks(df: DataFrame) DataFrame[source]
pipe_age_fix_age_max(df: DataFrame) DataFrame[source]
pipe_age_melt_pivot(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
pipeline_age(df: DataFrame) DataFrame[source]
pipeline_manufacturer(df: DataFrame) DataFrame[source]
read()[source]
cowidev.vax.batch.uruguay.main()[source]

cowidev.vax.batch.zimbabwe

class cowidev.vax.batch.zimbabwe.Zimbabwe[source]

Bases: CountryVaxBase

columns_rename: dict = {'date_reported': 'date', 'first_doses': 'people_vaccinated', 'second_doses': 'people_fully_vaccinated', 'third_doses': 'total_boosters'}
export()[source]
location: str = 'Zimbabwe'
pipe_columns(df: DataFrame) DataFrame[source]
pipe_date(df: DataFrame) DataFrame[source]
pipe_metrics(df: DataFrame) DataFrame[source]
pipe_rename_columns(df: DataFrame) DataFrame[source]
pipe_vaccine(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) DataFrame[source]
read() DataFrame[source]
source_url: str = 'https://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd'
cowidev.vax.batch.zimbabwe.main()[source]