Dataset: COVID-19 Country Data

About this Dataset

COVID-19 Country Data

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

Monthly temperature and precipitation from Worldbank
Latitude and longitude
Population, density, gender and age
Airport traffic from Worldbank
COVID-19 date of first case and number of cases and deaths as of March 26, 2020
2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
Property affordability index and Health care index from Numbeo
Number of hospital beds and ICU beds from Wikipedia
Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
School closures due to COVID-19
Number of COVID-19 tests done
Number of COVID-19 genetic strains
US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
DHL Global Connectedness Index 2018 (People Breadth scores)
Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

Tables

Covid19 Data 2009 Flu Pandemic

@kaggle.bitsnpieces_covid19_country_data.covid19_data_2009_flu_pandemic

9.48 KB
199 rows
7 columns


CREATE TABLE covid19_data_2009_flu_pandemic (
  "country" VARCHAR,
  "geographic_spread" VARCHAR,
  "intensity" VARCHAR,
  "impact_on_healthcare_services" VARCHAR,
  "cases_underestimate" BIGINT,
  "cases_confirmed_clean" BIGINT,
  "deaths_confirmed_clean" BIGINT
);

Covid19 Data Age

@kaggle.bitsnpieces_covid19_country_data.covid19_data_age

7.76 KB
191 rows
4 columns


CREATE TABLE covid19_data_age (
  "country" VARCHAR,
  "age_0_to_14_years" VARCHAR,
  "age_15_to_64_years" VARCHAR,
  "age_over_65_years" VARCHAR
);

Covid19 Data Airport Traffic

@kaggle.bitsnpieces_covid19_country_data.covid19_data_airport_traffic

9.11 KB
50 rows
7 columns


CREATE TABLE covid19_data_airport_traffic (
  "rank" BIGINT,
  "airport" VARCHAR,
  "location" VARCHAR,
  "country" VARCHAR,
  "code_iata_icao" VARCHAR,
  "total_passengers" BIGINT,
  "pct_change" VARCHAR
);

Covid19 Data Airport Traffic World

@kaggle.bitsnpieces_covid19_country_data.covid19_data_airport_traffic_world

9.08 KB
264 rows
3 columns


CREATE TABLE covid19_data_airport_traffic_world (
  "country_name" VARCHAR,
  "airport_traffic_2018_thousands" DOUBLE,
  "country_code" VARCHAR
);

Covid19 Data Cost Of Living

@kaggle.bitsnpieces_covid19_country_data.covid19_data_cost_of_living

13.58 KB
132 rows
8 columns


CREATE TABLE covid19_data_cost_of_living (
  "rank" BIGINT,
  "country" VARCHAR,
  "cost_of_living_index" DOUBLE,
  "rent_index" DOUBLE,
  "cost_of_living_plus_rent_index" DOUBLE,
  "groceries_index" DOUBLE,
  "restaurant_price_index" DOUBLE,
  "local_purchasing_power_index" DOUBLE
);

Covid19 Data Covid19 Strains

@kaggle.bitsnpieces_covid19_country_data.covid19_data_covid19_strains

97.43 KB
2649 rows
18 columns


CREATE TABLE covid19_data_covid19_strains (
  "strain" VARCHAR,
  "age" DOUBLE,
  "clade" VARCHAR,
  "country" VARCHAR,
  "admin_division" VARCHAR,
  "gisaid_epi_isl" VARCHAR,
  "host" VARCHAR,
  "originating_lab" VARCHAR,
  "submission_date" VARCHAR,
  "region" VARCHAR,
  "sex" VARCHAR,
  "submitting_lab" VARCHAR,
  "url" VARCHAR,
  "collection_data" TIMESTAMP,
  "author" VARCHAR,
  "genbank_accession" VARCHAR,
  "location" VARCHAR,
  "exposure_history" VARCHAR
);

Covid19 Data Covid Tests

@kaggle.bitsnpieces_covid19_country_data.covid19_data_covid_tests

5.1 KB
77 rows
4 columns


CREATE TABLE covid19_data_covid_tests (
  "entity" VARCHAR,
  "code" VARCHAR,
  "date" VARCHAR,
  "total_covid_19_tests" BIGINT
);

Covid19 Data Data Sources

@kaggle.bitsnpieces_covid19_country_data.covid19_data_data_sources

10.5 KB
21 rows
9 columns


CREATE TABLE covid19_data_data_sources (
  "name" VARCHAR,
  "source" VARCHAR,
  "notes" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

Covid19 Data Flu Pneumonia Death

@kaggle.bitsnpieces_covid19_country_data.covid19_data_flu_pneumonia_death

6.67 KB
183 rows
3 columns


CREATE TABLE covid19_data_flu_pneumonia_death (
  "rank" BIGINT,
  "country" VARCHAR,
  "rate_per_100000" DOUBLE
);

Covid19 Data Gdp

@kaggle.bitsnpieces_covid19_country_data.covid19_data_gdp

6.99 KB
192 rows
3 columns


CREATE TABLE covid19_data_gdp (
  "rank" BIGINT,
  "country_territory" VARCHAR,
  "gdp_usd_million" BIGINT
);

Covid19 Data Health

@kaggle.bitsnpieces_covid19_country_data.covid19_data_health

4.82 KB
93 rows
3 columns


CREATE TABLE covid19_data_health (
  "rank" BIGINT,
  "country" VARCHAR,
  "health_care_index" DOUBLE
);

Covid19 Data Hospital Beds

@kaggle.bitsnpieces_covid19_country_data.covid19_data_hospital_beds

11.21 KB
41 rows
11 columns


CREATE TABLE covid19_data_hospital_beds (
  "rank" BIGINT,
  "country_territory" VARCHAR,
  "continent" VARCHAR,
  "hosp_beds_per_1000_2013" DOUBLE,
  "hosp_beds_per_1000_2014" DOUBLE,
  "hosp_beds_per_1000_2015" DOUBLE,
  "hosp_beds_per_1000_2016" DOUBLE,
  "hosp_beds_per_1000_2017" DOUBLE,
  "occupancy_percent" DOUBLE,
  "icu_ccb_beds_per_100000" VARCHAR,
  "ventilators" VARCHAR
);

Covid19 Data Lat Long

@kaggle.bitsnpieces_covid19_country_data.covid19_data_lat_long

11.71 KB
245 rows
4 columns


CREATE TABLE covid19_data_lat_long (
  "country_code" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "country_name" VARCHAR
);

Covid19 Data Population

@kaggle.bitsnpieces_covid19_country_data.covid19_data_population

21.06 KB
235 rows
12 columns


CREATE TABLE covid19_data_population (
  "rank" BIGINT,
  "country" VARCHAR,
  "population_2020" BIGINT,
  "yearly_change" VARCHAR,
  "net_change" BIGINT,
  "density_km2m" BIGINT,
  "land_area_km2" BIGINT,
  "migrants" DOUBLE,
  "fertility_rate" VARCHAR,
  "median_age" VARCHAR,
  "urban_pop_pct" VARCHAR,
  "world_share" VARCHAR
);

Covid19 Data Property Prices

@kaggle.bitsnpieces_covid19_country_data.covid19_data_property_prices

13.68 KB
104 rows
9 columns


CREATE TABLE covid19_data_property_prices (
  "rank" BIGINT,
  "country" VARCHAR,
  "price_to_income_ratio" DOUBLE,
  "gross_rental_yield_city_centre" DOUBLE,
  "gross_rental_yield_outside_of_centre" DOUBLE,
  "price_to_rent_ratio_city_centre" DOUBLE,
  "price_to_rent_ratio_outside_of_city_centre" DOUBLE,
  "mortgage_as_a_percentage_of_income" DOUBLE,
  "affordability_index" DOUBLE
);

Covid19 Data Quality Of Life

@kaggle.bitsnpieces_covid19_country_data.covid19_data_quality_of_life

14.6 KB
80 rows
11 columns


CREATE TABLE covid19_data_quality_of_life (
  "rank" BIGINT,
  "country" VARCHAR,
  "quality_of_life_index" DOUBLE,
  "purchasing_power_index" DOUBLE,
  "safety_index" DOUBLE,
  "health_care_index" DOUBLE,
  "cost_of_living_index" DOUBLE,
  "property_price_to_income_ratio" DOUBLE,
  "traffic_commute_time_index" DOUBLE,
  "pollution_index" DOUBLE,
  "climate_index" DOUBLE
);

Covid19 Data School Closures

@kaggle.bitsnpieces_covid19_country_data.covid19_data_school_closures

10.87 KB
4042 rows
5 columns


CREATE TABLE covid19_data_school_closures (
  "date" TIMESTAMP,
  "iso" VARCHAR,
  "country" VARCHAR,
  "scale" VARCHAR,
  "note" VARCHAR
);

Covid19 Data Sex

@kaggle.bitsnpieces_covid19_country_data.covid19_data_sex

11.54 KB
230 rows
8 columns


CREATE TABLE covid19_data_sex (
  "country_region" VARCHAR,
  "at_birth_cia_estimate_2020" DOUBLE,
  "n_0_14_years" DOUBLE,
  "n_15_24_years" DOUBLE,
  "n_25_54_years" DOUBLE,
  "n_55_64_years" DOUBLE,
  "over_65" DOUBLE,
  "total" VARCHAR
);

Covid19 Merged

@kaggle.bitsnpieces_covid19_country_data.covid19_merged

106.97 KB
173 rows
65 columns


CREATE TABLE covid19_merged (
  "unnamed_0" BIGINT,
  "country" VARCHAR,
  "covid_confirmed_4_28_20" BIGINT,
  "covid_deaths_4_28_20" BIGINT,
  "covid_recovered_4_28_20" BIGINT,
  "covid19_first_date" TIMESTAMP,
  "flu_pneumonia_death_rate_per_100000" DOUBLE,
  "h1n1_geographic_spread" VARCHAR,
  "h1n1_intensity" VARCHAR,
  "h1n1_impact_on_healthcare_services" VARCHAR,
  "h1n1_cases_underestimate" DOUBLE,
  "h1n1_cases_confirmed" DOUBLE,
  "h1n1_deaths_confirmed" DOUBLE,
  "first_school_closure_date" TIMESTAMP,
  "code_2digit_x" VARCHAR,
  "code_3digit_x" VARCHAR,
  "jan_temp" DOUBLE,
  "feb_temp" DOUBLE,
  "mar_temp" DOUBLE,
  "apr_temp" DOUBLE,
  "may_temp" DOUBLE,
  "jun_temp" DOUBLE,
  "july_temp" DOUBLE,
  "aug_temp" DOUBLE,
  "sept_temp" DOUBLE,
  "oct_temp" DOUBLE,
  "nov_temp" DOUBLE,
  "dec_temp" DOUBLE,
  "annual_temp" DOUBLE,
  "jan_precip" DOUBLE,
  "feb_precip" DOUBLE,
  "mar_precip" DOUBLE,
  "apr_precip" DOUBLE,
  "may_precip" DOUBLE,
  "jun_precip" DOUBLE,
  "july_precip" DOUBLE,
  "aug_precip" DOUBLE,
  "sept_precip" DOUBLE,
  "oct_precip" DOUBLE,
  "nov_precip" DOUBLE,
  "dec_precip" DOUBLE,
  "annual_precip" DOUBLE,
  "airport_traffic_2018_thousands" DOUBLE,
  "property_affordability_index" DOUBLE,
  "health_care_index" DOUBLE,
  "hosp_beds_per_1000_2017" DOUBLE,
  "icu_ccb_beds_per_100000" DOUBLE,
  "population_2020" DOUBLE,
  "density_km2m" DOUBLE,
  "fertility_rate" VARCHAR,
  "median_age" VARCHAR,
  "urban_pop_pct" VARCHAR,
  "gdp_usd_million" DOUBLE,
  "age_0_to_14_years_percent" DOUBLE,
  "age_15_to_64_years_percent" DOUBLE,
  "age_over_65_years_percent" DOUBLE,
  "sex_male_to_female_at_birth_cia_estimate_2020" DOUBLE,
  "sex_male_to_female_0_14_years" DOUBLE,
  "sex_male_to_female_15_24_years" DOUBLE,
  "sex_male_to_female_25_54_years" DOUBLE,
  "sex_male_to_female_55_64_years" DOUBLE,
  "sex_male_to_female_over_65" DOUBLE,
  "sex_male_to_female_total" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE
);

Dhl People Breadth

@kaggle.bitsnpieces_covid19_country_data.dhl_people_breadth

3.56 KB
102 rows
2 columns


CREATE TABLE dhl_people_breadth (
  "country" VARCHAR,
  "breadth_score" BIGINT
);