Data
New-Cases-of-COVID-19-In-World-Countries

New-Cases-of-COVID-19-In-World-Countries

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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  • Computer Systems Machine Learning
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Has the curve flattened? Countries around the world are working to flatten the curve of the coronavirus pandemic. Flattening the curve involves reducing the number of new COVID-19 cases from one day to the next. This helps prevent healthcare systems from becoming overwhelmed. When a country has fewer new COVID-19 cases emerging today than it did on a previous day, thats a sign that the country is flattening the curve. On a trend line of total cases, a flattened curve looks how it sounds: flat. On the charts on this page, which show new cases per day, a flattened curve will show a downward trend in the number of daily new cases. This analysis uses a 5-day moving average to visualize the number of new COVID-19 cases and calculate the rate of change. This is calculated for each day by averaging the values of that day, the two days before, and the two next days. This approach helps prevent major events (such as a change in reporting methods) from skewing the data. The interactive charts below show the daily number of new cases for the 10 most affected countries, based on the reported number of deaths by COVID-19. This datas were last updated on Saturday, April 25, 2020 at 11:51 PM EDT.

28 features

country_or_regionstring185 unique values
0 missing
province_or_statestring83 unique values
0 missing
state_and_countrystring264 unique values
0 missing
datestring94 unique values
0 missing
daily_new_casesnumeric1100 unique values
0 missing
running_total_casesnumeric2971 unique values
0 missing
running_total_cases_prev_daynumeric2899 unique values
0 missing
daily_new_deathsnumeric368 unique values
0 missing
running_total_deathsnumeric891 unique values
0 missing
running_total_deaths_prev_daynumeric865 unique values
0 missing
data_sourcestring1 unique values
0 missing
latnumeric254 unique values
0 missing
longnumeric257 unique values
0 missing
first_case_state_ranknumeric94 unique values
4206 missing
first_case_country_ranknumeric94 unique values
2152 missing
hundred_case_state_ranknumeric94 unique values
9287 missing
hundred_case_country_ranknumeric94 unique values
6764 missing
country_code_2string173 unique values
534 missing
country_code_3string174 unique values
492 missing
country_population_2018numeric171 unique values
597 missing
country_median_agenumeric130 unique values
618 missing
country_running_aggnumeric2835 unique values
0 missing
locationstring259 unique values
0 missing
country_or_region_day_numbernumeric3102 unique values
0 missing
province_or_state_day_numbernumeric9369 unique values
0 missing
state_and_country_day_numbernumeric94 unique values
0 missing
country_code_2_day_numbernumeric3102 unique values
0 missing
country_code_3_day_numbernumeric3102 unique values
0 missing

19 properties

16659
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
24650
Number of missing values in the dataset.
9532
Number of instances with at least one value missing.
20
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
71.43
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
57.22
Percentage of instances having missing values.
Average class difference between consecutive instances.
5.28
Percentage of missing values.

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