COVID19-Dataset-with-100-World-Countries

OpenML dataset with id 43457

Author name not available (Why is that?)

Full work available at URL: https://api.openml.org/data/v1/download/22102282/COVID19-Dataset-with-100-World-Countries.arff

Upload date: 23 March 2022

Dataset Characteristics

Number of features: 15 (numeric: 12, symbolic: 0 and in total binary: 0 )
Number of instances: 38,472
Number of instances with missing values: 9,603
Number of missing values: 11,759

Description

COVID19-Algeria-and-World-Dataset A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

Geography: some continents/areas may be more affected by the disease Climate: cold temperatures may promote the spread of the virus Healthcare: lack of hospital beds/doctors may lead to more human losses Economy: weak economies (GDP) have fewer means to fight the disease Demography: older populations may be at higher risk of the disease

The last column represents the number of daily tests performed and the total number of cases and deaths reported each day. Data description

Countries in the dataset by geographic coordinates

Europe: 33 countries Asia: 28 countries Africa: 21 countries North America: 11 countries South America: 8 countries Oceania: 3 countries

Statistical description of the data

Data distribution

Download The dataset is available in an encoded CSV form on GitHub. Python code The Python Jupyter Notebook to read and visualize the data is available on nbviewer. Data update The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021. Data construction The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

Continents: www.kaggle.com/statchaitya/country-to-continent Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Citation If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it. articlebelkacem_covid-19_2020,

   title = COVID-19 data analysis and forecasting: Algeria and the world,
   shorttitle = COVID-19 data analysis and forecasting,
   journal = arXiv preprint arXiv:2007.09755,
   author = Belkacem, Sami,
   year = 2020

Contact If you have any question or suggestion, please contact me at this email address: s.belkacemusthb.dz

This page was built for dataset: COVID19-Dataset-with-100-World-Countries