In the case of Uruguay, you can take the information from the following repository: https://github.com/3dgiordano/covid-19-uy-vacc-data/blob/main/data/Uruguay.csv

The same repository that is used in OWID for its data. I added columns to reflect the sub-national data in the last few weeks.

If you want to migrate, the actual column format is total_[sub_id] where [sub_id] is the identification of the ISO format region in lowercase. Example: total_ar is total_vaccinations of UY-AR

In the next week I hope to resolve the data for people_vaccinated and people_fully_vaccinated. I will notify you when it is available.

Same dates are occurring twice for states such as IN-BR, IN-GJ and IN-KA in API for India. (not sure about other countries)

Example

{
            "region_iso": "IN-KA",
            "region_name": "Karnataka",
            "data": [
                {
                    "date": "2021-01-16",
                    "total_vaccinations": 13594,
                    "total_vaccinations_per_100": 0.02
                },
                {
                    "date": "2021-01-16",
                    "total_vaccinations": 13594,
                    "total_vaccinations_per_100": 0.02
                },
                {
                    "date": "2021-01-17",
                    "total_vaccinations": 29504,
                    "total_vaccinations_per_100": 0.05
                },
                {
                    "date": "2021-01-17",
                    "total_vaccinations": 29504,
                    "total_vaccinations_per_100": 0.05
                },
                {
                    "date": "2021-01-18",
                    "total_vaccinations": 66392,
                    "total_vaccinations_per_100": 0.11
                },
                {
                    "date": "2021-01-18",
                    "total_vaccinations": 66392,
                    "total_vaccinations_per_100": 0.11
                }
            ]
}

Due to upstream changes, we had to change our data structure a bit: https://github.com/mathiasbynens/covid-19-vaccinations-germany/blob/main/data/data.csv

• All columns with the first prefix have been renamed to initial, e.g. firstDosesCumulative → initialDosesCumulative.
• All columns with the second prefix have been renamed to final, e.g. secondDosesCumulative → finalDosesCumulative
• New columns are added for J&J vaccinations (but I think this part doesn’t affect your project).

The initial source is discontinued and current data is found here: https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1/#_

Specifically:

• 1st dose: https://www.data.gouv.fr/fr/datasets/r/735b0df8-51b4-4dd2-8a2d-8e46d77d60d8
• 2nd dose: https://www.data.gouv.fr/fr/datasets/r/96db2c1a-8c0c-413c-9a07-f6f62f3d4daf

Watch out for the column names: they are the same, but the descriptions of the files mention 1st and 2nd.

Current sourcehas been discontinued otherwise.

Hi - Taking Canada as an example in the shot below: the timeseries is not continuously increasing as one would expect from a cumulative timeseries. It does not correspond neither to a daily (or "new") number as the latest figures are pretty close to the total vaccinations (just below 1 million people).

I observed this for a lot of countries: USA, Italy, Czechia, ...

I am building out a GraphQL Server with StepZen and the query https://sociepy.org/covid19-vaccination-subnational/data/api/v1/latest/country_by_iso/FR.json breaks due to a NaN value rather than null value in total_vaccinations_per_100.

// Response from RestAPI

        {
            "region_name": "Corse",
            "region_iso": "FR-COR",
            "date": "2021-02-22",
            "total_vaccinations": 27059,
            "total_vaccinations_per_100": NaN
        },

GraphQL Query

  vaccines(countryISO: "FR") {
  country
  }

Response Error

{
  "data": {
    "vaccines": null
  },
  "errors": [
    {
      "message": "Invalid json content.: invalid character 'N' looking for beginning of value",
      "locations": [
        {
          "line": 58,
          "column": 3
        }
      ],
      "path": [
        "vaccines"
      ]
    }
  ]
}

Excellent

Shouldnt we try to use same as instead of just text strings

See how Wikidata can help table Table_of_administrative_divisions_by_country

• Introduction to Wikidata

I think it will be much easier if we have not just text strings but same as Wikidata

We changed the country reporting to using ISO 3 lettercode

Thanks for making that project!

There's an official live dashboard for the Ukrainian vaccination process: https://health-security.rnbo.gov.ua/vaccination

It uses data from an open API endpoint: https://health-security.rnbo.gov.ua/api/vaccination/process/map?distributionBy=vaccine&vaccines=&dose=1&date=2021-04-27

I'd gladly help writing a scrapper by myself, but I'm not particularly strong in Python. Nevertheless, I can help by answering possible questions.

I'll ask some folks on Twitter for help with writing a scrapper for that API.

I have made the changes as mentioned here. You can make the changes to the project w.r.t this.

I have made both state_timeline.json and state_timeline.csv as per the new structure, you can use whatever best suits.

the variable names in the source are changed as per 26 April.

Currently in the repo the data is till 24 April and the website is providing data for 26 April, data for 25th April is missing. I tried looking for it, but was not able to find any older datasets on the website. If you are free you can look into it !

source: https://es.wikipedia.org/wiki/Departamentos_del_Per%C3%BA

Map department ids (https://es.wikipedia.org/wiki/Departamentos_del_Per%C3%BA) to ISO-3166-2 codes.

The RKI reports vaccinations that are administered by the Bundeswehr: https://en.wikipedia.org/wiki/Bundeswehr This is not a German state! But it’s still important to count these vaccinations towards the national total.

The upstream CSV now includes entries with state == 'Bundeswehr'. (Previously this was stored as a separate file, but this increasingly made less and less sense since RKI is reporting it as if it was a state.)

Importantly, Bundeswehr entries lack any percentage metrics, since there is no known total population. Only absolute numbers are included.

I’m not sure if you want to include these entries in your data. If you do, we should probably add a new entry to https://github.com/sociepy/covid19-vaccination-subnational/blob/56cd475dd43a34a331ede1328683d7923bb7cc58/scripts/create_iso_db.py#L20.

I will not be able to maintain the source.

This seems to be best fit https://github.com/covid19india/api/blob/gh-pages/csv/latest/cowin_vaccine_data_statewise.csv for replacement.

The current source for Switzerland is not updated any more after 6th April (as mentioned in the readme).

You can use the primary source : https://www.covid19.admin.ch/en/epidemiologic/vacc-doses instead.

Official data source: https://emvolio.gov.gr/vaccinationtracker Script example: https://github.com/balikasg/covid19-vaccine-greece/blob/main/export_csv.py (another project)