Germany: LK Rheinisch-Bergischer Kreis (Nordrhein-Westfalen)¶

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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 14:02:27 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rheinisch-Bergischer Kreis", weeks=5);
2023-03-07T14:02:42.363584 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 67.7 LK Rheinisch-Bergischer Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Rheinisch-Bergischer Kreis new deaths (rolling 7d mean) Germany-LK Rheinisch-Bergischer Kreis new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Rheinisch-Bergischer Kreis cases daily growth factor Germany-LK Rheinisch-Bergischer Kreis cases daily growth factor (rolling mean) Germany-LK Rheinisch-Bergischer Kreis estimated R (using cases) 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 deaths) Germany-LK Rheinisch-Bergischer Kreis deaths daily growth factor Germany-LK Rheinisch-Bergischer Kreis deaths daily growth factor (rolling mean) Germany-LK Rheinisch-Bergischer Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Rheinisch-Bergischer Kreis doubling time cases (rolling mean) 0.0 56.7 113.4 daily change Germany-LK Rheinisch-Bergischer Kreis new cases (rolling 7d mean) Germany-LK Rheinisch-Bergischer Kreis new cases 0.000 0.386 0.771
In [4]:
overview(country="Germany", subregion="LK Rheinisch-Bergischer Kreis");
2023-03-07T14:03:04.157580 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 67.7 LK Rheinisch-Bergischer Kreis, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Rheinisch-Bergischer Kreis cases daily growth factor Germany-LK Rheinisch-Bergischer Kreis cases daily growth factor (rolling mean) Germany-LK Rheinisch-Bergischer Kreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 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 deaths) Germany-LK Rheinisch-Bergischer Kreis deaths daily growth factor Germany-LK Rheinisch-Bergischer Kreis deaths daily growth factor (rolling mean) Germany-LK Rheinisch-Bergischer Kreis estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Rheinisch-Bergischer Kreis doubling time cases (rolling mean) Germany-LK Rheinisch-Bergischer Kreis doubling time deaths (rolling mean) 0 283 567 850 1134 daily change Germany-LK Rheinisch-Bergischer Kreis new cases (rolling 7d mean) Germany-LK Rheinisch-Bergischer Kreis new cases 0.00 2.83 5.67 8.50 daily change Germany-LK Rheinisch-Bergischer Kreis new deaths (rolling 7d mean) Germany-LK Rheinisch-Bergischer Kreis new deaths 0.0 51.9 103.8 155.7 207.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rheinisch-Bergischer Kreis", dates="2020-03-15:");
2023-03-07T14:04:55.035065 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Rheinisch-Bergischer Kreis LK Rheinisch-Bergischer Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Rheinisch-Bergischer Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rheinisch-Bergischer Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rheinisch-Bergischer Kreis")
print(f'Population of country="Germany", subregion="LK Rheinisch-Bergischer Kreis": {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 country="Germany", subregion="LK Rheinisch-Bergischer Kreis": 283429 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 126340 1 283 0
2023-03-05 126339 1 283 0
2023-03-04 126338 6 283 0
2023-03-03 126332 21 283 0
2023-03-02 126311 30 283 0
... ... ... ... ...
2020-03-12 19 4 0 0
2020-03-11 15 4 0 0
2020-03-10 11 3 0 0
2020-03-09 8 5 0 0
2020-03-03 3 2 0 0

1020 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: 0:02:48.023462