Germany: LK Düren (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 13:45:32 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Düren", weeks=5);
2023-03-07T13:45:43.360452 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 200 200 300 300 400 400 7-day incidence rate (per 100K people) 193.4 LK Düren, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Düren cases daily growth factor Germany-LK Düren cases daily growth factor (rolling mean) Germany-LK Düren 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 Düren deaths daily growth factor Germany-LK Düren deaths daily growth factor (rolling mean) Germany-LK Düren estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] Germany-LK Düren doubling time cases (rolling mean) 0.0 133.4 266.8 daily change Germany-LK Düren new cases (rolling 7d mean) Germany-LK Düren new cases 0.000 0.534 1.067 1.601 daily change Germany-LK Düren new deaths (rolling 7d mean) Germany-LK Düren new deaths 0.000 0.218 0.435 0.653 0.870
In [4]:
overview(country="Germany", subregion="LK Düren");
2023-03-07T13:46:02.908991 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) 193.4 LK Düren, 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 0 200 400 600 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Düren cases daily growth factor Germany-LK Düren cases daily growth factor (rolling mean) Germany-LK Düren 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 Düren deaths daily growth factor Germany-LK Düren deaths daily growth factor (rolling mean) Germany-LK Düren 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 Düren doubling time cases (rolling mean) Germany-LK Düren doubling time deaths (rolling mean) 0 534 1067 1601 daily change Germany-LK Düren new cases (rolling 7d mean) Germany-LK Düren new cases 0.00 2.67 5.34 8.00 daily change Germany-LK Düren new deaths (rolling 7d mean) Germany-LK Düren new deaths 0.0 102.3 204.6 306.9 409.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Düren", dates="2020-03-15:");
2023-03-07T13:48:03.803355 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 Düren LK Düren 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 Düren Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Düren")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Düren")
print(f'Population of country="Germany", subregion="LK Düren": {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 Düren": 266771 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 134297 41 500 0
2023-03-03 134256 53 500 0
2023-03-02 134203 65 500 0
2023-03-01 134138 151 500 0
2023-02-28 133987 206 500 0
2023-02-27 133781 351 500 0
2023-02-24 133430 241 500 0
2023-02-23 133189 214 500 0
2023-02-22 132975 146 500 0
2023-02-21 132829 205 500 1
2023-02-17 132624 135 499 0
2023-02-16 132489 21 499 0
2023-02-15 132468 107 499 0
2023-02-14 132361 145 499 2
2023-02-13 132216 132 497 0
2023-02-10 132084 86 497 0
2023-02-09 131998 69 497 0
2023-02-08 131929 79 497 1
2023-02-07 131850 129 496 0
2023-02-06 131721 115 496 1
2023-02-03 131606 81 495 0
2023-02-02 131525 91 495 0
2023-02-01 131434 139 495 0
2023-01-31 131295 203 495 0
2023-01-30 131092 166 495 0
2023-01-27 130926 99 495 0
2023-01-26 130827 113 495 0
2023-01-25 130714 129 495 0
2023-01-24 130585 153 495 0
2023-01-23 130432 118 495 1
2023-01-20 130314 85 494 0
2023-01-19 130229 76 494 0
2023-01-18 130153 78 494 0
2023-01-17 130075 92 494 0
2023-01-16 129983 135 494 1
2023-01-13 129848 49 493 0
2023-01-12 129799 63 493 0
2023-01-11 129736 80 493 0
2023-01-10 129656 132 493 0
2023-01-09 129524 143 493 0
2023-01-06 129381 82 493 0
2023-01-05 129299 106 493 0
2023-01-04 129193 145 493 0
2023-01-03 129048 178 493 1
2023-01-02 128870 255 492 0
2022-12-30 128615 170 492 0
2022-12-29 128445 259 492 3
2022-12-28 128186 187 489 0
2022-12-27 127999 351 489 0
2022-12-23 127648 215 489 1
2022-12-22 127433 249 488 1
2022-12-21 127184 258 487 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: 0:03:46.418052