Germany: LK Kleve (Nordrhein-Westfalen)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
  • Execute this Jupyter Notebook using myBinder
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 13:53:26 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Kleve", weeks=5);
2023-03-07T13:53:40.107898 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) 74.4 LK Kleve, 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 0.0 0.1 0.2 0.3 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Kleve cases daily growth factor Germany-LK Kleve cases daily growth factor (rolling mean) Germany-LK Kleve estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Kleve deaths daily growth factor Germany-LK Kleve deaths daily growth factor (rolling mean) Germany-LK Kleve estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Kleve doubling time cases (rolling mean) 0.0 62.9 125.9 daily change Germany-LK Kleve new cases (rolling 7d mean) Germany-LK Kleve new cases 0.000 0.315 0.629 0.944 daily change Germany-LK Kleve new deaths (rolling 7d mean) Germany-LK Kleve new deaths 0.000 0.230 0.461 0.691 0.922
In [4]:
overview(country="Germany", subregion="LK Kleve");
2023-03-07T13:53:56.097929 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 7-day incidence rate (per 100K people) 74.4 LK Kleve, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Kleve cases daily growth factor Germany-LK Kleve cases daily growth factor (rolling mean) Germany-LK Kleve estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Kleve deaths daily growth factor Germany-LK Kleve deaths daily growth factor (rolling mean) Germany-LK Kleve 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 cases doubling time [days] Germany-LK Kleve doubling time cases (rolling mean) Germany-LK Kleve doubling time deaths (rolling mean) 0 315 629 944 1259 daily change Germany-LK Kleve new cases (rolling 7d mean) Germany-LK Kleve new cases 0.000 3.147 6.294 daily change Germany-LK Kleve new deaths (rolling 7d mean) Germany-LK Kleve new deaths 0.0 144.8 289.6 434.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Kleve", dates="2020-03-15:");
2023-03-07T13:56:15.927056 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 Kleve LK Kleve 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 Kleve Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Kleve")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Kleve")
print(f'Population of country="Germany", subregion="LK Kleve": {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 Kleve": 314676 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-04 124111 2 473 0
2023-03-03 124109 24 473 0
2023-03-02 124085 21 473 0
2023-03-01 124064 54 473 0
2023-02-28 124010 133 473 0
... ... ... ... ...
2020-03-16 41 19 0 0
2020-03-14 22 7 0 0
2020-03-13 15 8 0 0
2020-03-12 7 3 0 0
2020-03-11 4 2 0 0

1039 rows × 4 columns

Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

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: 0:03:10.680779