In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding 'migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid, Future Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population counts that the grids are derived from are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.


Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):

- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019).

Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Ecologists are increasingly turning to historical abundance data to understand past changes in animal abundance and more broadly the ecosystems in which animals occur. However, developing reliable ecological or management interpretations from temporal abundance data can be difficult because most population counts are subject to measurement or estimation error.

There is now widespread recognition that counts of animal populations are often subject to detection bias. This recognition has led to the development of a general framework for abundance estimation that explicitly accounts for detection bias and its uncertainty, new methods for estimating detection bias, and calls for ecologists to estimate and account for bias and uncertainty when estimating animal abundance. While these methodological developments are now being increasingly accepted and used, there is a wealth of historical population count data in the literature that were collected before these developments. These historical abundance data may, in their original published form, have inherent unrecognised and therefore unaccounted biases and uncertainties that could confound reliable interpretation. Developing approaches to improve interpretation of historical data may therefore allow a more reliable assessment of extremely valuable long-term abundance data.

This dataset contains details of over 200 historical estimates of Adelie penguin breeding populations across the Australian Antarctic Territory (AAT) that have been published in the scientific literature. The details include attributes of the population count (date and year of count, count value, count object, count precision) and the published estimate of the breeding population derived from those attributes, expressed as the number of breeding pairs. In addition, the dataset contains revised population estimates that have been re-constructed using new estimation methods to account for detection bias as described in the associated publication. All population data used in this study were sourced from existing publications.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

• I've downloaded and wrangled the John Hopkins dataset on the number of reported COVID-19 cases by country.
• I joined this dataset with a bunch of additional metadata on country-level.

The data only covers the period Jan. 22 - March 23, but it should be a piece of cake to apply the metadata provided here on a larger range of data (just perform a join operation).

I used the dataset for an online lecture on data visualization --> https://www.youtube.com/watch?v=l85l1qmosEU

The additional variables provided here could shed some light on correlational relations between - for example - the share of government expenditure in the health care system and the growth rate of the virus in a given country.

• Data sources:

--> Reported COVID-19 cases by country by day: https://github.com/CSSEGISandData/COVID-19 --> Data on health expenditure comes from WHO: https://apps.who.int/nha/database/Select/Indicators/en (created my own table) --> Population data and other socio-demographic data: https://www.worldometers.info/world-population/population-by-country/ --> Countries divided by continent: https://www.worldometers.info/geography/7-continents/

• Methodology:

1. John Hopkins data was sometimes inconsistent in their level of aggregation; reporting country-level data in most cases, but sometimes opting for county/state level data (e.g. for the United States). I aggregated all data to country-level. Moreover, there were plenty of inconsistencies in country notation between the different datasets, which required some fuzzy matching techniques.
2. I melted the data so that one line corresponds with one day in a particular country. This is the default data scheme for anyone trying to do some data viz on the dataset at hand (I sacrifice data size for convenience here, I know...)

Some of the interactive dashboards created with this data:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2F2d8e73336e269038f06b43f81183fd87%2Fcovid19%20dashboard.JPG?generation=1597334049308430&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Ff08488ae7bded1f5850e730b87437782%2Fcovid19%20dashboard%202.JPG?generation=1597334070819173&alt=media" alt="">

Have fun!

Analysis of ‘Malaria in Africa’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lydia70/malaria-in-africa on 29 August 2021.

--- Dataset description provided by original source is as follows ---

Context

Africa, the world's second-largest continent, a continent with a wide array of vibrant cultures each with its own deep history, continent number 2 of largest population, and the continent is home to wonderful wildlife you can spot when you go on safari! Let's focus on Africa in this dataset.

Malaria is a common disease in Africa. The disease is transmitted to humans through infected mosquito bites. Although you can take preventive measures against malaria, it can be life-threatening. This dataset includes the malaria cases in African countries, the incidence at risk, and data on preventive treatments against malaria.

Content

This dataset includes data on all African countries from 2007 till 2017. Each country has a unique ISO-3 country code, and the dataset includes the latitude and longitude point of each country as well. The dataset includes the cases of malaria that have been reported in each country and each year, as well as data on preventive measures that have been taken to prevent malaria.

Acknowledgements

The data on the incidence of malaria, malaria cases reported, and preventive treatments against malaria have been retrieved from the world bank open data source.

Inspiration

Each country has a unique ISO-3 country code. You can use the ISO-3 code to create choropleth maps and in the geospatial analysis. In addition, the dataset includes latitude and longitude points for each country.

Drinking water safety and sanitation include a risk factor for malaria. Can improved drinking water facilities and preventive measures decrease the risk of malaria infection?

Check out my notebook submission, feel free to copy the kernel for your analysis: https://www.kaggle.com/lydia70/notebook-malaria-in-africa The notebook submission includes geospatial analysis with plotly.

--- Original source retains full ownership of the source dataset ---

The burden of animal disease is widespread globally and is especially severe for developing countries dependent on livestock production. Ethiopia has the largest livestock population in Africa and the second-largest human population on the continent. Ethiopia is one of the fastest-growing economies in Africa; however, much of the population still lives in extreme poverty, and most households depend on agriculture. Animal disease negatively affects domestic livestock production and limits growth potential across the domestic agricultural supply chain. This research investigates the economic effects of livestock disease burden in Ethiopia by employing a computable general equilibrium model in tandem with animal health loss estimates from a compartmental livestock population model. Two scenarios for disease burden are simulated to understand the effects of improved animal health on domestic production, prices, trade, gross domestic product (GDP), and economic welfare in Ethiopia. Results show that improved animal health may increase Ethiopian GDP by up to 3.6%, which improves national welfare by approximately $US 2.5 billion. This research illustrates the economic effects of improved livestock health, which is critical for Ethiopian households and the national economy.

The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

 This documentation describes the Latin American Population Database, a
 collaborative effort between the International Center for Tropical
 Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID,
 Sioux Falls) and the World Resources Institute (WRI). This work is
 intended to provide a population database that compliments previous
 work carried out for Asia and Africa. This data set is more detailed
 than the Africa and Asia data sets. Population estimates for 1960,
 1970, 1980, 1990 and 2000 are also provided. The work discussed in the
 following paragraphs is also related to NCGIA activities to produce a
 global database of subnational population estimates (Tobler et
 al. 1995), and an improved database for the Asian continent (Deichmann
 1996a).

A major goal of invasion biology is to understand global species flows between donor and recipient regions. Our current view of such flows assumes that species are moved directly from their native to their introduced range. However, if introduced populations serve as bridgehead population that generate additional introductions, tracing intercontinental flows between donor and recipient regions misrepresents the introduction history. Our aim was to assess to what extent bridgehead effects distort our view of global species flows. We separately mapped "flows" of 252 alien ant species established on one to six continents, representing a gradient of relatively certain to completely unreliable flows. In 83% of countries, more than 50% of alien ants were established on six continents, indicating that flows to these countries are unreliable. Flows of species established on a single continent were linked to global trade flows, while flows including cosmopolitan species were not linked to global trade. It is crucial to account for bridgehead effects when assessing the biogeography and intercontinental flows of alien species. This is urgent for improving our understanding of how species are moved around the planet.

Methods Species distributions and flows To determine the number of alien ant species that are established in each country, we used the geo-referenced database Antmaps (an authoritative database maintained and updated regularly by experts based on new records from the peer-reviewed scientific literature). The Antmaps database includes information on the native and alien ranges of 252 ant species (https://antmaps.org/?). We did not consider occurrence records that may be dubious (needing taxonomic verification). We kept both indoor and outdoor locations because all parts of the species’ distribution are the consequence of human-mediated dispersal. Populations that occurred at indoor locations were also a possible source of new invasions, for example if material such as potted plants and soil are moved from an indoor location to a different location. The aim of our analyses was not to distinguish between factors (climate, habitat) filtering out species at the establishment stage of the invasion process, but to understand what drives global species movements. As all species records are a reflection of global species flows, we kept all records for the analyses presented in the main part of the manuscript.

We delimited the countries and continents based on the administrative database GADM version 3.6. For mapping, we used the Mollweide projection. We defined a species “flow” as the number of species introduced from one region to another region. To calculate the species flows from donor to recipient regions, we defined the species’ native range as all countries containing native populations according to Antmaps. For species whose native range covers more than one continent, we weighted the flow from each of the continents by the number of political regions where the species is native (i.e., non-overlapping country or sub-country polygons, representing states, counties or islands and which are more homogenous in size than entire countries.

Countries In total, 173 countries worldwide host alien ant species. To compare species flows, we focused on the 41 countries which had both species exotic in only one continent and species exotic in several continents. In that way, we were able to compare the different species flows for all alien species (hereafter ALL species) or species exotic in one continent (Exo1) or two (Exo2), three (Exo3), four (Exo4), five (Exo5) or all continents except Antarctica (Exo6).

Interception data We have sourced previously published interception records for the United States and New Zealand from 1914-2013 (described in detail in Bertelsmeier et al. 2018, PNAS). In total, this dataset contains 69 alien ant species intercepted on cargo, goods, mail and baggage and has information on the country of origin for each interception, and therefore allows calculating the proportion of secondary interceptions for each species (i.e., the proportion of all interceptions of a species which come from a country where the species is not native).

Trade data Most biological invasions arise via human-mediated transport, allowing species to establish in new geographic regions. In particular, accidental transport with traded commodities is an important dispersal pathway for insects in general and especially ants. We used general import flows to represent global flows of potential transport vectors. To calculate import flows to all countries, we used cumulative import data from 1998 to 2017 extracted from the UN Comtrade Database (United Nations Commodity Trade Statistics Database, http://comtrade.un.org/db/ (accessed May 2019)). This dataset contains dyadic trade flows between pairs of countries, given in US dollars per year. Such comprehensive data is not available for earlier periods; as most imports over the last two centuries have occurred during this recent period of globalization, we expect these relatively recent imports to have left their footprint on the flows of ants. Because no import data was available for four previously defined administrative units (Puerto Rico, Christmas Island, Norfolk Island and Marshall Islands), they were excluded from this analysis. The flows to each of the remaining 37 countries were standardized by dividing the flows by the total imports to each country in order to study variations in the proportions of geographic origins of the flows (and not the absolute quantities).

Retiriment Notice: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofmaize harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by the International Food Policy Research Institute in 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing the Spatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of maize as a staple food see the Harvest Choice webpage. The source data for this layer are available here.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

Content

The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.

The datasets

COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

COVID-19 tests covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
4. Worldometer: https://www.worldometers.info/
5. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
6. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers