Germany: LK Main-Taunus-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 12:44:24 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Main-Taunus-Kreis", weeks=5);
2023-03-07T12:44:42.671398 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) 86.9 LK Main-Taunus-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 10 20 30 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 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Main-Taunus-Kreis cases daily growth factor Germany-LK Main-Taunus-Kreis cases daily growth factor (rolling mean) Germany-LK Main-Taunus-Kreis 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 Main-Taunus-Kreis deaths daily growth factor Germany-LK Main-Taunus-Kreis deaths daily growth factor (rolling mean) Germany-LK Main-Taunus-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 Main-Taunus-Kreis doubling time cases (rolling mean) 0.0 23.9 47.9 71.8 95.7 daily change Germany-LK Main-Taunus-Kreis new cases (rolling 7d mean) Germany-LK Main-Taunus-Kreis new cases 0.000 0.239 0.479 0.718 0.957 daily change Germany-LK Main-Taunus-Kreis new deaths (rolling 7d mean) Germany-LK Main-Taunus-Kreis new deaths 0.00 0.24 0.48 0.72 0.96
In [4]:
overview(country="Germany", subregion="LK Main-Taunus-Kreis");
2023-03-07T12:45:00.486899 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) 86.9 LK Main-Taunus-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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Main-Taunus-Kreis cases daily growth factor Germany-LK Main-Taunus-Kreis cases daily growth factor (rolling mean) Germany-LK Main-Taunus-Kreis 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 Main-Taunus-Kreis deaths daily growth factor Germany-LK Main-Taunus-Kreis deaths daily growth factor (rolling mean) Germany-LK Main-Taunus-Kreis estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Main-Taunus-Kreis doubling time cases (rolling mean) Germany-LK Main-Taunus-Kreis doubling time deaths (rolling mean) 0.0 239.3 478.6 717.8 daily change Germany-LK Main-Taunus-Kreis new cases (rolling 7d mean) Germany-LK Main-Taunus-Kreis new cases 0.000 2.393 4.786 7.178 daily change Germany-LK Main-Taunus-Kreis new deaths (rolling 7d mean) Germany-LK Main-Taunus-Kreis new deaths 0.0 114.1 228.2 342.3 456.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Main-Taunus-Kreis", dates="2020-03-15:");
2023-03-07T12:47:11.461241 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 Main-Taunus-Kreis LK Main-Taunus-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 Main-Taunus-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Main-Taunus-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Main-Taunus-Kreis")
print(f'Population of country="Germany", subregion="LK Main-Taunus-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 Main-Taunus-Kreis": 239276 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 110078 6 395 0
2023-03-05 110072 2 395 0
2023-03-04 110070 8 395 0
2023-03-03 110062 37 395 0
2023-03-02 110025 15 395 0
2023-03-01 110010 40 395 0
2023-02-28 109970 100 395 0
2023-02-27 109870 72 395 0
2023-02-26 109798 16 395 0
2023-02-25 109782 25 395 0
2023-02-24 109757 103 395 0
2023-02-23 109654 54 395 0
2023-02-22 109600 97 395 0
2023-02-21 109503 87 395 0
2023-02-20 109416 90 395 0
2023-02-19 109326 16 395 0
2023-02-18 109310 35 395 0
2023-02-17 109275 82 395 0
2023-02-16 109193 78 395 0
2023-02-15 109115 89 395 0
2023-02-14 109026 72 395 0
2023-02-13 108954 97 395 1
2023-02-12 108857 8 394 0
2023-02-11 108849 17 394 0
2023-02-10 108832 66 394 0
2023-02-09 108766 54 394 0
2023-02-08 108712 84 394 0
2023-02-07 108628 62 394 0
2023-02-06 108566 81 394 0
2023-02-05 108485 7 394 0
2023-02-04 108478 17 394 0
2023-02-03 108461 36 394 0
2023-02-02 108425 47 394 0
2023-02-01 108378 43 394 0
2023-01-31 108335 75 394 0
2023-01-30 108260 59 394 0
2023-01-29 108201 16 394 0
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2023-01-25 108083 54 394 0
2023-01-24 108029 59 394 1
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2023-01-05 107297 73 393 1
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2023-01-03 107185 83 391 0
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2022-12-31 107016 45 391 1
2022-12-30 106971 68 390 0
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2022-12-28 106836 89 390 2
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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: 0:03:08.080846