#Python create sqlite database iso
#Python create sqlite database series

new_vaccinations_smoothed: New COVID-19 vaccination doses administered (7-day smoothed).new_vaccinations: New COVID-19 vaccination doses administered (only calculated for consecutive days).people_fully_vaccinated: Total number of people who received all doses prescribed by the vaccination protocol.people_vaccinated: Total number of people who received at least one vaccine dose.total_vaccinations: Total number of COVID-19 vaccination doses administered.tests_units: Units used by the location to report its testing data.tests_per_case: Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate).

positive_rate: The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case).

new_tests_smoothed_per_thousand: New tests for COVID-19 (7-day smoothed) per 1,000 people.

#Python create sqlite database series

This produces a complete series of daily figures, which is then averaged over a rolling 7-day window For countries that donвЂ™t report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported.

new_tests_smoothed: New tests for COVID-19 (7-day smoothed).

new_tests_per_thousand: New tests for COVID-19 per 1,000 people.

total_tests_per_thousand: Total tests for COVID-19 per 1,000 people.

new_tests: New tests for COVID-19 (only calculated for consecutive days).

weekly_hosp_admissions_per_million: Number of COVID-19 patients newly admitted to hospitals in a given week per 1,000,000 people.

weekly_hosp_admissions: Number of COVID-19 patients newly admitted to hospitals in a given week.

weekly_icu_admissions_per_million: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week per 1,000,000 people.

weekly_icu_admissions: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week.

hosp_patients_per_million: Number of COVID-19 patients in hospital on a given day per 1,000,000 people.

hosp_patients: Number of COVID-19 patients in hospital on a given day.

icu_patients_per_million: Number of COVID-19 patients in intensive care units (ICUs) on a given day per 1,000,000 people.

icu_patients: Number of COVID-19 patients in intensive care units (ICUs) on a given day.

reproduction_rate: Real-time estimate of the effective reproduction rate (R) of COVID-19.

new_deaths_smoothed_per_million: New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people.

new_deaths_per_million: New deaths attributed to COVID-19 per 1,000,000 people.

total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people.

new_cases_smoothed_per_million: New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people.

new_cases_per_million: New confirmed cases of COVID-19 per 1,000,000 people.

total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people.

new_deaths_smoothed: New deaths attributed to COVID-19 (7-day smoothed).

new_deaths: New deaths attributed to COVID-19.

total_deaths: Total deaths attributed to COVID-19.

new_cases_smoothed: New confirmed cases of COVID-19 (7-day smoothed).

new_cases: New confirmed cases of COVID-19.

total_cases: Total confirmed cases of COVID-19.

continent: Continent of the geographical location.

#Python create sqlite database iso

iso_code: ISO 3166-1 alpha-3 вЂ“ three-letter country codes.When creating the SQLite database, we will create a table for each of these categories: place, covid19_case, covid19_test, covid19_vaccination, government_response, and demography. We have arranged all variables into different categories based on the nature of variables. The variablesвЂ™ description, borrowed directly from the codebook, can be found below. Reviewing the codebook provided on the host of this database is highly recommended for this purpose. First, we should spend some time to become familiar with all the variables in the dataset. The dataset consists of 59 variables (each column represents one variable).