@@ -79,9 +79,7 @@ This is similar to `Cases_Per_Country`, but for districts.
#### Vaccinations_Per_Country
This table includes the `country_code` and data associated with vaccinations: `source_id`, `date_collected`, `first_vaccination_number`, `second_vaccination_number`, and `third_vaccination_number`. The vaccination numbers are basically the number of people who got their ith vaccination.
_Ivy, is this cumulative? or daily?_
This table includes the `country_code` and data associated with vaccinations: `source_id`, `date_collected`, `first_vaccination_number`, `second_vaccination_number`, and `third_vaccination_number`. The vaccination numbers are basically the number of people who have gotten their ith vaccination cumulatively till that date.
#### Vaccinations_Per_Region
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@@ -91,20 +89,6 @@ This is similar to `Vaccinations_Per_Country`, but for regions.
This is similar to `Vaccinations_Per_Country`, but for districts.
#### Population_Per_Country
This table includes the `country_code` and population of that country on a certain date: `date_collected` and `population_amount`.
The intention behind this table is to give users something to aggregate and manipulate data in other tables with. For instance, we number of deaths in the `Cases_Per_Country` table. But what if you wanted to know the percentage of deaths instead? This table would help with that. Unfortunately, this table is not exact and must be intentionally updated by a user. Population censuses are not taken often, so this data can't be used to determine exact percentages, only approximations. Regardless, we felt having a framework in place to help with such calculations would be useful.
#### Population_Per_Region
This is similar to `Population_Per_Country`, but for regions. Note that this population data will likely be more difficult to find than for countries.
#### Population_Per_District
This is similar to `Population_Per_Country`, but for districts. Note that this population data will likely be more difficult to find than for countries.
#### Age_Per_Country
This table is similar to `Cases_Per_Country` but has data divided based on ages as well. Also, unlike with `Cases_Per_Country`, different sources provided information in numbers or rates, so both versions are included in the table. The attributes include the `country_code` and the age-based COVID-19 data: `source_id`, `date_collected`, `age_group`, `death_number`, `case_number`, `recovery_number`, `hospitalization_number`, `death_rate`, `case_rate`, `recovery_rate`, and `hospitalization_rate`.
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@@ -119,6 +103,32 @@ This is similar to `Age_Per_Country`, but for regions.
This is similar to `Age_Per_Country`, but for districts.
#### Strains_Per_Country
This table includes the `country_code` and the breakdown of rates of cases for each strain: `source_id`, `date_collected`, `alpha_rate`, `beta_rate`, `gamma_rate`, `delta_rate`, `omicron_rate`, and `other_rate`.
#### Strains_Per_Region
This is similar to `Strains_Per_Country`, but for regions.
#### Strains_Per_District
This is similar to `Strains_Per_Country`, but for districts.
#### Population_Per_Country
This table includes the `country_code` and population of that country on a certain date: `date_collected` and `population_amount`.
The intention behind this table is to give users something to aggregate and manipulate data in other tables with. For instance, we number of deaths in the `Cases_Per_Country` table. But what if you wanted to know the percentage of deaths instead? This table would help with that. Unfortunately, this table is not exact and must be intentionally updated by a user. Population censuses are not taken often, so this data can't be used to determine exact percentages, only approximations. Regardless, we felt having a framework in place to help with such calculations would be useful.
#### Population_Per_Region
This is similar to `Population_Per_Country`, but for regions. Note that this population data will likely be more difficult to find than for countries.
#### Population_Per_District
This is similar to `Population_Per_Country`, but for districts. Note that this population data will likely be more difficult to find than for countries.
