Korea, South¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")

Notebook executed on: 07/03/2023 09:35:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Korea, South", weeks=5);
2023-03-07T09:35:24.577834 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 300 300 7-day incidence rate (per 100K people) 132.2 Korea, South, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.02 0.04 0.06 0.08 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Korea, South cases daily growth factor Korea, South cases daily growth factor (rolling mean) Korea, South estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Korea, South deaths daily growth factor Korea, South deaths daily growth factor (rolling mean) Korea, South estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Korea, South doubling time cases (rolling mean) Korea, South doubling time deaths (rolling mean) 0 5127 10254 15381 20508 daily change Korea, South new cases (rolling 7d mean) Korea, South new cases 0.00 10.25 20.51 30.76 41.02 daily change Korea, South new deaths (rolling 7d mean) Korea, South new deaths 0 1057 2114 3171 deaths doubling time [days]
In [4]:
overview("Korea, South");
2023-03-07T09:35:33.544832 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 2000 2000 4000 4000 7-day incidence rate (per 100K people) 132.2 Korea, South, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 250 500 750 1000 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.2 0.4 0.6 0.8 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Korea, South cases daily growth factor Korea, South cases daily growth factor (rolling mean) Korea, South estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Korea, South deaths daily growth factor Korea, South deaths daily growth factor (rolling mean) Korea, South estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 1000 2000 3000 cases doubling time [days] Korea, South doubling time cases (rolling mean) Korea, South doubling time deaths (rolling mean) 0 128173 256346 384519 512692 daily change Korea, South new cases (rolling 7d mean) Korea, South new cases 0.0 102.5 205.1 307.6 410.2 daily change Korea, South new deaths (rolling 7d mean) Korea, South new deaths 0 1155 2310 3465 deaths doubling time [days]
In [5]:
compare_plot("Korea, South", normalise=True);
2023-03-07T09:35:37.950547 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.001 0.001 0.1 0.1 10 10 1000 1000 daily new cases per 100K people (rolling 7-day mean) Daily cases (top) and deaths (below) for Korea, South Korea, South Germany Australia Poland Belarus Switzerland US 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.0001 0.0001 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths per 100K people (rolling 7-day mean) Korea, South Germany Australia Poland Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Korea, South")

# get population of the region for future normalisation:
inhabitants = population("Korea, South")
print(f'Population of "Korea, South": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table

Population of "Korea, South": 51269183 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 30581499 12284 34049 15
2023-03-05 30569215 14113 34034 14
2023-03-04 30555102 0 34020 0
2023-03-03 30555102 11121 34020 6
2023-03-02 30543981 10408 34014 11
... ... ... ... ...
2020-01-27 4 1 0 0
2020-01-26 3 1 0 0
2020-01-25 2 0 0 0
2020-01-24 2 1 0 0
2020-01-23 1 0 0 0

1139 rows × 4 columns

Explore the data in your web browser¶

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Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)
In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")

Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")

Notebook execution took: 0:00:17.600593