Germany: LK Lahn-Dill-Kreis (Hessen)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 12:36:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Lahn-Dill-Kreis", weeks=5);
2023-03-07T12:36:23.620884 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 7-day incidence rate (per 100K people) 200.9 LK Lahn-Dill-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 25 50 75 100 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Lahn-Dill-Kreis cases daily growth factor Germany-LK Lahn-Dill-Kreis cases daily growth factor (rolling mean) Germany-LK Lahn-Dill-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 Lahn-Dill-Kreis deaths daily growth factor Germany-LK Lahn-Dill-Kreis deaths daily growth factor (rolling mean) Germany-LK Lahn-Dill-Kreis 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 Lahn-Dill-Kreis doubling time cases (rolling mean) 0.0 63.3 126.7 190.0 253.4 daily change Germany-LK Lahn-Dill-Kreis new cases (rolling 7d mean) Germany-LK Lahn-Dill-Kreis new cases 0.000 0.253 0.507 0.760 daily change Germany-LK Lahn-Dill-Kreis new deaths (rolling 7d mean) Germany-LK Lahn-Dill-Kreis new deaths 0.000 0.209 0.417 0.626 0.834
In [4]:
overview(country="Germany", subregion="LK Lahn-Dill-Kreis");
2023-03-07T12:36:42.153226 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) 200.9 LK Lahn-Dill-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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Lahn-Dill-Kreis cases daily growth factor Germany-LK Lahn-Dill-Kreis cases daily growth factor (rolling mean) Germany-LK Lahn-Dill-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 Lahn-Dill-Kreis deaths daily growth factor Germany-LK Lahn-Dill-Kreis deaths daily growth factor (rolling mean) Germany-LK Lahn-Dill-Kreis estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 cases doubling time [days] Germany-LK Lahn-Dill-Kreis doubling time cases (rolling mean) Germany-LK Lahn-Dill-Kreis doubling time deaths (rolling mean) 0 253 507 760 1013 daily change Germany-LK Lahn-Dill-Kreis new cases (rolling 7d mean) Germany-LK Lahn-Dill-Kreis new cases 0.00 2.53 5.07 7.60 daily change Germany-LK Lahn-Dill-Kreis new deaths (rolling 7d mean) Germany-LK Lahn-Dill-Kreis new deaths 0.0 115.9 231.9 347.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Lahn-Dill-Kreis", dates="2020-03-15:");
2023-03-07T12:39:29.328291 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 Lahn-Dill-Kreis LK Lahn-Dill-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 Lahn-Dill-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Lahn-Dill-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Lahn-Dill-Kreis")
print(f'Population of country="Germany", subregion="LK Lahn-Dill-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 Lahn-Dill-Kreis": 253364 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 128693 7 416 0
2023-03-05 128686 5 416 0
2023-03-04 128681 9 416 0
2023-03-03 128672 28 416 1
2023-03-02 128644 313 415 0
2023-03-01 128331 75 415 0
2023-02-28 128256 72 415 0
2023-02-27 128184 19 415 0
2023-02-26 128165 19 415 0
2023-02-25 128146 22 415 0
2023-02-24 128124 82 415 0
2023-02-23 128042 137 415 0
2023-02-22 127905 39 415 0
2023-02-21 127866 31 415 0
2023-02-20 127835 25 415 0
2023-02-19 127810 6 415 0
2023-02-18 127804 8 415 0
2023-02-17 127796 64 415 0
2023-02-16 127732 39 415 0
2023-02-15 127693 58 415 0
2023-02-14 127635 58 415 0
2023-02-13 127577 16 415 0
2023-02-12 127561 9 415 0
2023-02-11 127552 10 415 0
2023-02-10 127542 62 415 0
2023-02-09 127480 37 415 0
2023-02-08 127443 56 415 0
2023-02-07 127387 55 415 0
2023-02-06 127332 17 415 0
2023-02-05 127315 1 415 0
2023-02-04 127314 17 415 0
2023-02-03 127297 60 415 0
2023-02-02 127237 16 415 0
2023-02-01 127221 38 415 0
2023-01-31 127183 52 415 0
2023-01-30 127131 5 415 0
2023-01-29 127126 21 415 0
2023-01-28 127105 17 415 0
2023-01-27 127088 80 415 0
2023-01-26 127008 64 415 1
2023-01-25 126944 55 414 0
2023-01-24 126889 56 414 1
2023-01-23 126833 13 413 0
2023-01-22 126820 5 413 0
2023-01-21 126815 15 413 0
2023-01-20 126800 47 413 0
2023-01-19 126753 27 413 0
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2023-01-16 126646 11 413 0
2023-01-15 126635 14 413 0
2023-01-14 126621 31 413 0
2023-01-13 126590 59 413 0
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2023-01-06 126280 114 413 1
2023-01-05 126166 93 412 0
2023-01-04 126073 127 412 1
2023-01-03 125946 109 411 0
2023-01-02 125837 19 411 0
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2022-12-30 125723 193 411 0
2022-12-29 125530 141 411 1
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2022-12-27 125205 24 409 0
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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: 0:03:57.363082