cowidev.vax.batch

cowidev.vax.batch.argentina

class cowidev.vax.batch.argentina.Argentina

Bases: CountryVaxBase

_build_df(data)

_build_df_age_group(data)

_check_data_age(data)

_parse_data(data)

_parse_data_age(data)

age_group_valid = {'12-17', '18-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90-99', '<12', '>=100'}

export()

location: str = 'Argentina'

pipe_age_cumsum(df)

pipe_age_date(df)

pipe_aggregate_vaccines(df: DataFrame)

pipe_base_cumsum(df: DataFrame)

pipe_base_metrics(df)

pipe_base_vaccines(df: DataFrame)

pipe_vaccine(df: DataFrame)

pipeline(df: DataFrame) → Series

pipeline_age(df)

pipeline_base(df: DataFrame)

pipeline_manufacturer(df: DataFrame) → Series

read()

read_age()

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()

cowidev.vax.batch.australia

class cowidev.vax.batch.australia.Australia

Bases: CountryVaxBase

columns_rename = {'dose_1': 'people_vaccinated', 'dose_2': 'people_fully_vaccinated', 'dose_3': 'total_boosters'}

export()

location: str = 'Australia'

pipe_age_groups(df)

pipe_age_metadata(df: DataFrame) → DataFrame

pipe_age_numeric(df)

pipe_date(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_total_vaccinations(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

read() → DataFrame

read_age() → DataFrame

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()

cowidev.vax.batch.austria

class cowidev.vax.batch.austria.Austria

Bases: CountryVaxBase

export()

location: str = 'Austria'

one_dose_vaccines: str = ['Janssen']

pipe_date(df: DataFrame) → DataFrame

pipe_filter_rows(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_quick_fix(df: DataFrame) → DataFrame

pipe_reshape(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.belgium

class cowidev.vax.batch.belgium.Belgium

Bases: CountryVaxBase

export()

pipe_add_totals(df: DataFrame) → DataFrame

pipe_aggregate(df: DataFrame) → DataFrame

pipe_cumsum(df: DataFrame) → DataFrame

pipe_dose_check(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine_name(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

cowidev.vax.batch.belgium.main()

cowidev.vax.batch.bolivia

class cowidev.vax.batch.bolivia.Bolivia

Bases: CountryVaxBase

export()

location: str = 'Bolivia'

pipe_columns_out(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

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()

cowidev.vax.batch.canada

class cowidev.vax.batch.canada.Canada

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()

location: str = 'Canada'

max_filtered_dates: int = 3

max_removed_rows: int = 22

pipe_filter_dp(df: DataFrame) → DataFrame

pipe_get_totals(df: DataFrame) → DataFrame

pipe_make_monotonic(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine_timeline(df: DataFrame, df_man: DataFrame) → DataFrame

pipeline(df: DataFrame, df_man: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

read_age() → DataFrame

read_manufacturer() → DataFrame

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()

cowidev.vax.batch.chile

class cowidev.vax.batch.chile.Chile

Bases: CountryVaxBase

export()

pipe_add_metadata(df: DataFrame) → DataFrame

pipe_add_vaccine_list(df: DataFrame) → DataFrame

pipe_calculate_metrics(df: DataFrame) → DataFrame

pipe_exclude_total(df: DataFrame, colname: str) → DataFrame

pipe_keep_total(df: DataFrame, colname: str) → DataFrame

pipe_melt(df: DataFrame, id_vars: list) → DataFrame

pipe_pivot(df: DataFrame, index: list) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_rename_vaccines(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

pipeline_vaccinations(df: DataFrame) → DataFrame

read(url: str) → DataFrame

save_vaccine_list(df: DataFrame) → DataFrame

cowidev.vax.batch.chile.main()

cowidev.vax.batch.czechia

class cowidev.vax.batch.czechia.Czechia

Bases: CountryVaxBase

export()

location: str = 'Czechia'

pipeline(df: DataFrame) → DataFrame

source_url = 'https://onemocneni-aktualne.mzcr.cz/api/v2/covid-19/ockovani.csv'

cowidev.vax.batch.czechia.aggregate_by_date(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.aggregate_by_date_vaccine(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.base_pipeline(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.breakdown_per_vaccine(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.check_vaccine_names(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.enrich_cumulated_sums(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.enrich_location(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.enrich_metadata(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.enrich_source(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.format_date(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.infer_one_dose_vaccines(df: DataFrame) → DataFrame

cowidev.vax.batch.czechia.main()

cowidev.vax.batch.czechia.read(source: str) → DataFrame

cowidev.vax.batch.czechia.remove_vaccines(df: DataFrame, vaccine_schedule: dict) → dict

cowidev.vax.batch.czechia.translate_vaccine_names(df: DataFrame) → DataFrame

cowidev.vax.batch.denmark

class cowidev.vax.batch.denmark.Denmark

Bases: CountryVaxBase

_download_and_extract_data(url, output_path)

_get_file_links_bfill(index=None, date_limit=None)

_get_num_gap_days(df_current)

_load_data(path)

_parse_link_zip() → str

Get link to latest pdf.

_read_data(path) → DataFrame

_read_single_shots_bfill(index=None, date_limit=None)

Read single shots using bfill (iterates over old links)

_read_single_shots_daily(path) → dict

date_limit_one_dose = '2021-05-27'

property date_limit_one_dose_ddmmyyyy

export()

location: str = 'Denmark'

pipe_metrics(df: DataFrame, df_current: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame, df_current: DataFrame) → DataFrame

read(gap_days, bfill=True) → DataFrame

read_current()

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)

Build filepath.

cowidev.vax.batch.denmark._load_datafile(path)

Read csv file.

cowidev.vax.batch.denmark.main()

cowidev.vax.batch.ecdc

class cowidev.vax.batch.ecdc.ECDC

Bases: CountryVaxBase

_filter_age_targetgroup(df_c: DataFrame)

_load_country_mapping(iso_path: str)

_vaccine_timeseries(df: DataFrame)

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)

property country_mapping

export()

export_age(df: DataFrame)

export_main(df: DataFrame)

export_manufacturer(df: DataFrame)

location: str = 'ECDC'

pipe_age_checks(df: DataFrame) → DataFrame

pipe_age_filter_entries(df: DataFrame) → DataFrame

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

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

Build age groups.

pipe_age_relative_metrics(df: DataFrame, df_og: DataFrame) → DataFrame

pipe_base(df: DataFrame) → DataFrame

pipe_cumsum(df: DataFrame, group_field_renamed: Optional[str] = None) → DataFrame

pipe_filter_locations(df: DataFrame)

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

pipe_filter_targetgroup(df: DataFrame)

pipe_group(df: DataFrame, group_field: Optional[str] = None, group_field_renamed: Optional[str] = None) → DataFrame

pipe_initial_check(df: DataFrame) → DataFrame

pipe_manufacturer_filter_entries(df: DataFrame)

pipe_manufacturer_filter_locations(df: DataFrame)

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

pipe_rename_vaccines(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame, vax_timeline)

pipeline(df: DataFrame)

pipeline_age(df: DataFrame)

pipeline_common(df: DataFrame, group_field: Optional[str] = None, group_field_renamed: Optional[str] = None) → DataFrame

pipeline_manufacturer(df: DataFrame)

read()

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()

cowidev.vax.batch.ecuador

class cowidev.vax.batch.ecuador.Ecuador

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()

location: str = 'Ecuador'

pipe_checks(df: DataFrame) → DataFrame

pipe_column_rename(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_exclude_dp(df: DataFrame) → DataFrame

pipe_manuf_aggregate(df: DataFrame) → DataFrame

pipe_manuf_date(df: DataFrame) → DataFrame

pipe_manuf_rename_cols(df: DataFrame) → DataFrame

pipe_manuf_vaccine_checks(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccines(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

read_manuf() → DataFrame

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()

cowidev.vax.batch.estonia

class cowidev.vax.batch.estonia.Estonia

Bases: CountryVaxBase

_parse_data() → DataFrame

_parse_metric(df, series, measurement, metric_name) → DataFrame

export()

location: str = 'Estonia'

pipe_location(df: DataFrame) → DataFrame

pipe_source(df: DataFrame) → DataFrame

pipe_vaccine_name(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.france

class cowidev.vax.batch.france.France

Bases: CountryVaxBase

export()

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()

cowidev.vax.batch.germany

class cowidev.vax.batch.germany.Germany

Bases: CountryVaxBase

_check_vaccines(df: DataFrame)

Get vaccine columns mapped to Vaccine names.

_vaccine_start_dates(df: DataFrame)

calculate_metrics(df: DataFrame) → DataFrame

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

enrich_source(df: DataFrame) → DataFrame

enrich_vaccine(df: DataFrame) → DataFrame

export()

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

pipe_sanity_checks(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

regex_doses_colnames: str = 'impfungen_([a-zA-Z]*)_kumulativ'

select_output_columns(df: DataFrame) → DataFrame

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

translate_vaccine_columns(df: DataFrame) → DataFrame

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()

cowidev.vax.batch.greece

class cowidev.vax.batch.greece.Greece

Bases: CountryVaxBase

export()

location: str = 'Greece'

pipe_date(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.hong_kong

class cowidev.vax.batch.hong_kong.HongKong

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()

location: str = 'Hong Kong'

pipe_add_metadata(df: DataFrame) → DataFrame

pipe_add_vaccines(df: DataFrame) → DataFrame

pipe_age_agg(df: DataFrame)

pipe_age_checks(df: DataFrame)

pipe_age_filter(df: DataFrame)

pipe_age_groups(df: DataFrame)

pipe_age_pivot(df: DataFrame)

pipe_calculate_metrics(df: DataFrame) → DataFrame

pipe_filter_dp(df: DataFrame) → DataFrame

pipe_reshape(df: DataFrame) → DataFrame

pipe_sum_manufacturer(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.indonesia

class cowidev.vax.batch.indonesia.Indonesia

Bases: CountryVaxBase

export()

location: str = 'Indonesia'

pipe_add_latest_boosters(df: DataFrame) → DataFrame

pipe_merge_current(df: DataFrame, df_current: DataFrame) → DataFrame

pipe_merge_legacy(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame, df_current: DataFrame) → DataFrame

read() → DataFrame

read_current()

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()

cowidev.vax.batch.ireland

class cowidev.vax.batch.ireland.Ireland

Bases: CountryVaxBase

_parse_data_boosters(data: dict) → int

_parse_data_primary(data: dict) → int

export()

location: str = 'Ireland'

pipe_date(df: DataFrame) → DataFrame

pipe_filter(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → str

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.israel

class cowidev.vax.batch.israel.Israel

Bases: CountryVaxBase

export()

location: str = 'Israel'

pipe_date(df: DataFrame) → DataFrame

pipe_filter_date(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipe_nulls_as_nans(df: DataFrame) → DataFrame

pipe_output_columns(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_select_min_date(df: DataFrame) → DataFrame

pipe_source(df: DataFrame) → DataFrame

pipe_total_boosters(df: DataFrame) → DataFrame

pipe_total_vaccinations(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df)

read() → DataFrame

read_age()

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()

cowidev.vax.batch.italy

class cowidev.vax.batch.italy.Italy

Bases: CountryVaxBase

_check_vaccines(df: DataFrame) → DataFrame

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

enrich_source(df: DataFrame) → DataFrame

enrich_vaccine(df: DataFrame) → DataFrame

export() → None

get_final_numbers(df: DataFrame) → DataFrame

get_people_fully_vaccinated(df: DataFrame) → DataFrame

get_people_vaccinated(df: DataFrame) → DataFrame

get_total_vaccinations(df: DataFrame) → DataFrame

get_total_vaccinations_by_manufacturer(df: DataFrame) → DataFrame

location: str = 'Italy'

one_dose_vaccines: list = ['Johnson&Johnson']

pipeline(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

rename_columns(df: DataFrame) → DataFrame

source_url: str = 'https://raw.githubusercontent.com/italia/covid19-opendata-vaccini/master/dati/somministrazioni-vaccini-latest.csv'

translate_vaccine_columns(df: DataFrame) → DataFrame

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]]

vax_date_mapping = None

cowidev.vax.batch.italy.main()

cowidev.vax.batch.japan

class cowidev.vax.batch.japan.Japan

Bases: CountryVaxBase

_parse_df(df: DataFrame, date_col: str, ind: list, metrics: dict) → DataFrame

_read_xlsx(url: str, sheets: dict, metrics: dict) → dict

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()

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

pipe_early(df: DataFrame) → DataFrame

pipe_latest(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

read_early() → DataFrame

read_latest() → DataFrame

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()

cowidev.vax.batch.jersey

class cowidev.vax.batch.jersey.Jersey

Bases: CountryVaxBase

_extract_age_group(age_group_raw)

export()

Generalized.

pipe_age_create_groups(df: DataFrame) → DataFrame

pipe_age_filter(df: DataFrame) → DataFrame

pipe_age_fix_dp(df: DataFrame) → DataFrame

pipe_age_minmax_values(df: DataFrame) → DataFrame

pipe_age_rename_columns(df: DataFrame) → DataFrame

pipe_age_select_columns(df: DataFrame) → DataFrame

pipe_enrich_columns(df: DataFrame) → DataFrame

pipe_enrich_vaccine_name(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_metrics_scale_100(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_select_columns(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

read()

cowidev.vax.batch.jersey.main()

cowidev.vax.batch.latvia

class cowidev.vax.batch.latvia.Latvia

Bases: CountryVaxBase

_read_one(url: str)

_remove_vaccines(df, approval_timeline)

export()

location: str = 'Latvia'

pipe_aggregate(df: DataFrame) → DataFrame

pipe_base(df: DataFrame) → DataFrame

pipe_cumsum(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_pivot(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

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()

cowidev.vax.batch.lithuania

class cowidev.vax.batch.lithuania.Lithuania

Bases: CountryVaxBase

_find_vaccines(date)

export()

location: str = 'Lithuania'

pipe_add_vaccines(df: DataFrame) → DataFrame

pipe_clean_coverage(df: DataFrame) → DataFrame

pipe_clean_doses(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_parse_dates(df: DataFrame) → DataFrame

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_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()

cowidev.vax.batch.luxembourg

class cowidev.vax.batch.luxembourg.Luxembourg

Bases: CountryVaxBase

export()

location: str = 'Luxembourg'

pipe_correct_time_series(df: DataFrame) → DataFrame

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

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_running_totals(df: DataFrame) → DataFrame

pipe_vaccines(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.malaysia

class cowidev.vax.batch.malaysia.Malaysia

Bases: CountryVaxBase

_vax_1d = ['cansino']

_vax_2d = ['pfizer', 'astra', 'sinovac', 'sinopharm', 'pending']

export()

location: str = 'Malaysia'

pipe_calculate_metrics(df: DataFrame) → DataFrame

pipe_check_columns(df: DataFrame) → DataFrame

pipe_columns_out(df: DataFrame) → DataFrame

pipe_filter_columns(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.malta

class cowidev.vax.batch.malta.Malta

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()

location: str = 'Malta'

pipe_check_columns(df: DataFrame) → DataFrame

pipe_correct_data(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_exclude_data_points(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.netherlands

class cowidev.vax.batch.netherlands.Netherlands

Bases: CountryVaxBase

export()

location: str = 'Netherlands'

pipe_date(df: DataFrame) → DataFrame

pipe_filter_rows(df: DataFrame) → DataFrame

pipe_get_vax_timeline(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_metrics_aggregate(df: DataFrame) → DataFrame

pipe_metrics_cumsum(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

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()

cowidev.vax.batch.new_zealand

class cowidev.vax.batch.new_zealand.NewZealand

Bases: CountryVaxBase

_parse_file_link(soup: BeautifulSoup) → str

Parses the link from the soup.

_read_latest(soup)

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()

Exports the data to CSV

location: str = 'New Zealand'

pipe_boosters(df: DataFrame) → DataFrame

Calculates the total boosters.

pipe_cumsum(df: DataFrame) → DataFrame

Calculates cumulative sum of the columns.

pipe_date(df: DataFrame) → DataFrame

Formats the date column.

pipe_latest_metrics(df: DataFrame) → DataFrame

pipes the latest metrics.

pipe_total_vaccinations(df: DataFrame) → DataFrame

Calculates the total vaccinations.

pipe_vaccine(df: DataFrame) → DataFrame

Builds the vaccine timeline.

pipeline(df: DataFrame) → DataFrame

Pipeline for the data

read() → DataFrame

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()

cowidev.vax.batch.norway

class cowidev.vax.batch.norway.Norway

Bases: CountryVaxBase

export()

pipe_filter_rows(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

cowidev.vax.batch.norway.main()

cowidev.vax.batch.peru

class cowidev.vax.batch.peru.Peru

Bases: CountryVaxBase

date_start = '2021-02-08'

export()

location: str = 'Peru'

pipe_age_checks(df: DataFrame) → DataFrame

pipe_age_columns_out(df: DataFrame) → DataFrame

pipe_age_date(df: DataFrame) → DataFrame

pipe_checks(df: DataFrame) → DataFrame

pipe_filter_only_campaign(df: DataFrame) → DataFrame

pipe_format(df: DataFrame) → DataFrame

pipe_get_vax_timeline(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_total_vaccinations(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

read_age()

read_manufacturer()

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()

cowidev.vax.batch.portugal

class cowidev.vax.batch.portugal.Portugal

Bases: CountryVaxBase

_pipe_metrics() → DataFrame

columns_rename: dict = {'data': 'date', 'pessoas_inoculadas': 'people_vaccinated', 'pessoas_reforço': 'total_boosters', 'pessoas_vacinadas_completamente': 'people_fully_vaccinated', 'vacinas': 'total_vaccinations'}

export()

location: str = 'Portugal'

pipe_columns_out(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_dropna(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_sanity_checks(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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

cowidev.vax.batch.portugal.main()

cowidev.vax.batch.romania

class cowidev.vax.batch.romania.Romania

Bases: CountryVaxBase

columns_rename: dict = {'immunized': 'people_fully_vaccinated', 'total_administered': 'total_vaccinations'}

export()

location: str = 'Romania'

pipe_filter_rows_columns(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipe_manufacturer_cumsum(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_source(df: DataFrame) → DataFrame

pipe_store_timeline(df: DataFrame) → DataFrame

pipe_sum_vaccines(df: DataFrame) → DataFrame

pipe_unnest_data(df: DataFrame) → DataFrame

pipe_vaccines(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.saudi_arabia

class cowidev.vax.batch.saudi_arabia.SaudiArabia

Bases: CountryVaxBase

export()

location: str = 'Saudi Arabia'

pipeline(df: DataFrame)

read()

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()

cowidev.vax.batch.singapore

class cowidev.vax.batch.singapore.Singapore

Bases: CountryVaxBase

_merge_primary_and_boosters(df_primary, df_boosters)

export()

pipe_filter_dp(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → str

cowidev.vax.batch.singapore.main()

cowidev.vax.batch.slovakia

class cowidev.vax.batch.slovakia.Slovakia

Bases: CountryVaxBase

_week_to_date(row)

date_start = datetime.datetime(2021, 1, 4, 0, 0)

export()

location: str = 'Slovakia'

pipe_cumsum(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_out_columns(df: DataFrame) → DataFrame

pipe_reshape(df: DataFrame) → DataFrame

pipe_vaccine_checks(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

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()

cowidev.vax.batch.slovenia

class cowidev.vax.batch.slovenia.Slovenia

Bases: CountryVaxBase

_build_vaccine_str(d)

_parse_data(data)

export()

location: str = 'Slovenia'

pipeline(df: DataFrame)

read()

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()

cowidev.vax.batch.south_korea

class cowidev.vax.batch.south_korea.SouthKorea

Bases: CountryVaxBase

_check_format_multicols(df: DataFrame, columns_lv) → DataFrame

export()

pipe_check_metrics(df: DataFrame)

pipe_cumsum(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_man_aggregate(df: DataFrame) → DataFrame

pipe_man_melt(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns_raw(df: DataFrame)

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_base(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

cowidev.vax.batch.south_korea.main()

cowidev.vax.batch.spc

class cowidev.vax.batch.spc.SPC

Bases: CountryVaxBase

_build_data_array(observations: dict, date_info: dict)

_build_df(dix: dict, country: str)

_build_df_list(data: dict)

_parse_country_info(data: dict)

_parse_date_info(data: dict)

_parse_metrics_info(data: dict)

_pretty_vaxdates(country, date_min)

export()

location: str = 'SPC'

parse_data(data: dict)

pipe_drop_duplicates(df: DataFrame) → DataFrame

pipe_merge_boosters(df: DataFrame, country: str) → DataFrame

Adds the boosters data available in the csv.

pipe_merge_legacy(df: DataFrame, country: str) → DataFrame

pipe_vacine(df: DataFrame, country: str) → DataFrame

read()

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()

cowidev.vax.batch.sweden

class cowidev.vax.batch.sweden.Sweden

Bases: CountryVaxBase

_get_df_teens_daily(df)

[deprecated]

_merge_tables_daily(df_people, df_doses, df_boosters)

_merge_tables_daily_split(df_adults, df_teens, df_doses)

[deprecated

_read_daily_data() → DataFrame

Read daily data (latest) from HTML page.

_read_daily_data_age_split() → DataFrame

[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)

_read_daily_data_doses(df)

_read_daily_data_people(df)

_read_weekly_data() → DataFrame

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

Read weekly data for number of vaccinations administered.

_read_weekly_data_people(dfs) → DataFrame

Read weekly data for number of vaccinated people.

export()

Generalized.

merge_with_current_booster_data(output_path, df)

pipe_add_boosters(df: DataFrame)

pipe_columns(df: DataFrame) → DataFrame

pipe_out_columns(df: DataFrame)

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

cowidev.vax.batch.sweden.main()

cowidev.vax.batch.switzerland

class cowidev.vax.batch.switzerland.Switzerland

Bases: CountryVaxBase

_get_file_url() → str

_parse_age_link(soup)

_parse_data(doses_url, people_url, manufacturer_url)

export()

location: str = 'Switzerland'

pipe_age_checks(df)

pipe_age_date(df)

pipe_age_filter_region(df, geo_region)

pipe_age_groups(df)

pipe_age_location(df, location)

pipe_age_pivot(df)

pipe_age_rename_columns(df)

pipe_age_select_cols(df)

pipe_filter_country(df: DataFrame, country_code: str) → DataFrame

pipe_fix_metrics(df: DataFrame) → DataFrame

pipe_location(df: DataFrame, location: str) → DataFrame

pipe_pivot(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_source(df: DataFrame, country_code: str) → DataFrame

pipe_unique_rows(df: DataFrame)

pipeline(df: DataFrame, location: str) → DataFrame

pipeline_age(df, location)

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

read_age()

save_vaccine_timeline(df_manuf: DataFrame) → DataFrame

cowidev.vax.batch.switzerland._get_geo_region(location)

cowidev.vax.batch.switzerland.main()

cowidev.vax.batch.trinidad_and_tobago

class cowidev.vax.batch.trinidad_and_tobago.TrinidadTobago

Bases: CountryVaxBase

_parse_data(data: dict) → int

export()

location: str = 'Trinidad and Tobago'

pipe_checks(df: DataFrame) → DataFrame

pipe_data_correction(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_filter_dp(df: DataFrame) → DataFrame

pipe_legacy(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_out_columns(df: DataFrame) → DataFrame

pipe_source(df: DataFrame) → DataFrame

pipe_vaccine_name(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()

cowidev.vax.batch.ukraine

class cowidev.vax.batch.ukraine.Ukraine

Bases: CountryVaxBase

_load_dose_data(dose_param, colname)

export()

location: str = 'Ukraine'

pipe_date(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

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()

cowidev.vax.batch.united_kingdom

class cowidev.vax.batch.united_kingdom.UnitedKingdom

Bases: CountryVaxBase

_filter_location(df: DataFrame, location: str) → DataFrame

_read_metrics(filters)

export()

location: str = 'United Kingdom'

pipe_select_output_cols(df: DataFrame) → DataFrame

pipe_source_url(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

source_url = 'https://coronavirus.data.gov.uk/details/vaccinations'

cowidev.vax.batch.united_kingdom.main()

cowidev.vax.batch.united_states

class cowidev.vax.batch.united_states.UnitedStates

Bases: CountryVaxBase

export()

pipe_add_source(df: DataFrame) → DataFrame

pipe_add_vaccines(df: DataFrame) → DataFrame

pipe_clean_data(df: DataFrame) → DataFrame

pipe_filter_rows(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read() → DataFrame

read_manufacturer() → DataFrame

cowidev.vax.batch.united_states.main()

cowidev.vax.batch.uruguay

class cowidev.vax.batch.uruguay.Uruguay

Bases: CountryVaxBase

export()

pipe_age_checks(df: DataFrame) → DataFrame

pipe_age_fix_age_max(df: DataFrame) → DataFrame

pipe_age_melt_pivot(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_age(df: DataFrame) → DataFrame

pipeline_manufacturer(df: DataFrame) → DataFrame

read()

cowidev.vax.batch.uruguay.main()

cowidev.vax.batch.zimbabwe

class cowidev.vax.batch.zimbabwe.Zimbabwe

Bases: CountryVaxBase

columns_rename: dict = {'date_reported': 'date', 'first_doses': 'people_vaccinated', 'second_doses': 'people_fully_vaccinated', 'third_doses': 'total_boosters'}

export()

location: str = 'Zimbabwe'

pipe_columns(df: DataFrame) → DataFrame

pipe_date(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read() → DataFrame

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()