Germany: LK Recklinghausen (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 14:01:03 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Recklinghausen", weeks=5);
2023-03-07T14:01:16.736548 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 7-day incidence rate (per 100K people) 104.1 LK Recklinghausen, Germany, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.05 0.10 0.15 daily change normalised per 100K 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 cases) Germany-LK Recklinghausen cases daily growth factor Germany-LK Recklinghausen cases daily growth factor (rolling mean) Germany-LK Recklinghausen estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Recklinghausen deaths daily growth factor Germany-LK Recklinghausen deaths daily growth factor (rolling mean) Germany-LK Recklinghausen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-LK Recklinghausen doubling time cases (rolling mean) 0.0 61.3 122.6 183.8 daily change Germany-LK Recklinghausen new cases (rolling 7d mean) Germany-LK Recklinghausen new cases 0.000 0.306 0.613 0.919 daily change Germany-LK Recklinghausen new deaths (rolling 7d mean) Germany-LK Recklinghausen new deaths 0.000 0.349 0.698
In [4]:
overview(country="Germany", subregion="LK Recklinghausen");
2023-03-07T14:01:34.670995 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 1000 1000 2000 2000 7-day incidence rate (per 100K people) 104.1 LK Recklinghausen, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Recklinghausen cases daily growth factor Germany-LK Recklinghausen cases daily growth factor (rolling mean) Germany-LK Recklinghausen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Recklinghausen deaths daily growth factor Germany-LK Recklinghausen deaths daily growth factor (rolling mean) Germany-LK Recklinghausen 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 Recklinghausen doubling time cases (rolling mean) Germany-LK Recklinghausen doubling time deaths (rolling mean) 0 613 1226 1838 2451 daily change Germany-LK Recklinghausen new cases (rolling 7d mean) Germany-LK Recklinghausen new cases 0.00 6.13 12.26 18.38 daily change Germany-LK Recklinghausen new deaths (rolling 7d mean) Germany-LK Recklinghausen new deaths 0 324 649 973 1297 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Recklinghausen", dates="2020-03-15:");
2023-03-07T14:04:31.403358 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 Recklinghausen LK Recklinghausen 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 Recklinghausen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Recklinghausen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Recklinghausen")
print(f'Population of country="Germany", subregion="LK Recklinghausen": {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 Recklinghausen": 612801 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 245987 34 1750 0
2023-03-05 245953 13 1750 0
2023-03-04 245940 71 1750 0
2023-03-03 245869 50 1750 0
2023-03-02 245819 116 1750 0
... ... ... ... ...
2020-03-16 44 28 0 0
2020-03-13 16 6 0 0
2020-03-12 10 2 0 0
2020-03-11 8 5 0 0
2020-03-10 3 2 0 0

1073 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: 0:03:59.739372