Germany: LK Schwäbisch Hall (Baden-Württemberg)¶

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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 10:11:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schwäbisch Hall", weeks=5);
2023-03-07T10:11:55.699071 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 40 40 50 50 60 60 7-day incidence rate (per 100K people) 50.7 LK Schwäbisch Hall, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 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 cases) Germany-LK Schwäbisch Hall cases daily growth factor Germany-LK Schwäbisch Hall cases daily growth factor (rolling mean) Germany-LK Schwäbisch Hall 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 Schwäbisch Hall deaths daily growth factor Germany-LK Schwäbisch Hall deaths daily growth factor (rolling mean) Germany-LK Schwäbisch Hall estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Schwäbisch Hall doubling time cases (rolling mean) 0.00 9.97 19.94 29.91 39.88 daily change Germany-LK Schwäbisch Hall new cases (rolling 7d mean) Germany-LK Schwäbisch Hall new cases 0.000 0.399 0.798 daily change Germany-LK Schwäbisch Hall new deaths (rolling 7d mean) Germany-LK Schwäbisch Hall new deaths 0.00 0.28 0.56 0.84
In [4]:
overview(country="Germany", subregion="LK Schwäbisch Hall");
2023-03-07T10:12:13.173064 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) 50.7 LK Schwäbisch Hall, 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 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 4 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-LK Schwäbisch Hall cases daily growth factor Germany-LK Schwäbisch Hall cases daily growth factor (rolling mean) Germany-LK Schwäbisch Hall 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 Schwäbisch Hall deaths daily growth factor Germany-LK Schwäbisch Hall deaths daily growth factor (rolling mean) Germany-LK Schwäbisch Hall 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 20000 cases doubling time [days] Germany-LK Schwäbisch Hall doubling time cases (rolling mean) Germany-LK Schwäbisch Hall doubling time deaths (rolling mean) 0 399 798 daily change Germany-LK Schwäbisch Hall new cases (rolling 7d mean) Germany-LK Schwäbisch Hall new cases 0.000 1.994 3.988 5.982 7.976 daily change Germany-LK Schwäbisch Hall new deaths (rolling 7d mean) Germany-LK Schwäbisch Hall new deaths 0.0 77.4 154.7 232.1 309.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schwäbisch Hall", dates="2020-03-15:");
2023-03-07T10:14:28.843045 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 Schwäbisch Hall LK Schwäbisch Hall 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 Schwäbisch Hall Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schwäbisch Hall")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schwäbisch Hall")
print(f'Population of country="Germany", subregion="LK Schwäbisch Hall": {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 Schwäbisch Hall": 199398 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 86176 5 378 0
2023-03-04 86171 12 378 0
2023-03-03 86159 9 378 0
2023-03-02 86150 15 378 0
2023-03-01 86135 19 378 0
2023-02-28 86116 41 378 0
2023-02-27 86075 12 378 0
2023-02-25 86063 19 378 0
2023-02-24 86044 12 378 0
2023-02-23 86032 22 378 0
2023-02-22 86010 18 378 0
2023-02-21 85992 24 378 0
2023-02-20 85968 22 378 0
2023-02-18 85946 18 378 0
2023-02-17 85928 15 378 0
2023-02-16 85913 24 378 0
2023-02-15 85889 15 378 0
2023-02-14 85874 22 378 0
2023-02-13 85852 14 378 0
2023-02-11 85838 11 378 0
2023-02-10 85827 18 378 1
2023-02-09 85809 19 377 0
2023-02-08 85790 23 377 0
2023-02-07 85767 16 377 0
2023-02-06 85751 17 377 0
2023-02-04 85734 14 377 0
2023-02-03 85720 15 377 0
2023-02-02 85705 17 377 0
2023-02-01 85688 10 377 0
2023-01-31 85678 16 377 0
2023-01-30 85662 6 377 0
2023-01-28 85656 6 377 0
2023-01-27 85650 7 377 0
2023-01-26 85643 7 377 0
2023-01-25 85636 8 377 0
2023-01-24 85628 6 377 0
2023-01-23 85622 5 377 0
2023-01-21 85617 6 377 0
2023-01-20 85611 1 377 0
2023-01-19 85610 4 377 0
2023-01-18 85606 5 377 0
2023-01-17 85601 7 377 0
2023-01-16 85594 3 377 0
2023-01-14 85591 7 377 0
2023-01-13 85584 8 377 0
2023-01-12 85576 6 377 0
2023-01-11 85570 15 377 0
2023-01-10 85555 12 377 0
2023-01-09 85543 10 377 0
2023-01-07 85533 13 377 0
2023-01-05 85520 13 377 0
2023-01-04 85507 19 377 0
2023-01-03 85488 21 377 0
2023-01-02 85467 10 377 1
2022-12-31 85457 13 376 0
2022-12-30 85444 13 376 0
2022-12-29 85431 15 376 0
2022-12-28 85416 15 376 0
2022-12-27 85401 26 376 0
2022-12-24 85375 30 376 0
2022-12-23 85345 20 376 0
2022-12-22 85325 26 376 0
2022-12-21 85299 24 376 0
2022-12-20 85275 33 376 0
2022-12-19 85242 16 376 0
2022-12-17 85226 23 376 1
2022-12-16 85203 21 375 0
2022-12-15 85182 15 375 0
2022-12-14 85167 23 375 1
2022-12-13 85144 34 374 0
2022-12-12 85110 19 374 0
2022-12-10 85091 26 374 0
2022-12-09 85065 21 374 0
2022-12-08 85044 20 374 0
2022-12-07 85024 29 374 1
2022-12-06 84995 30 373 0
2022-12-05 84965 16 373 0
2022-12-03 84949 13 373 0
2022-12-02 84936 19 373 0
2022-12-01 84917 15 373 0
2022-11-30 84902 25 373 0
2022-11-29 84877 36 373 0
2022-11-28 84841 13 373 0
2022-11-26 84828 18 373 0
2022-11-25 84810 18 373 0
2022-11-24 84792 28 373 0
2022-11-23 84764 15 373 0
2022-11-22 84749 17 373 0
2022-11-21 84732 21 373 0
2022-11-19 84711 25 373 0
2022-11-18 84686 14 373 0
2022-11-17 84672 26 373 0
2022-11-16 84646 26 373 0
2022-11-15 84620 39 373 1
2022-11-14 84581 20 372 0
2022-11-12 84561 18 372 0
2022-11-11 84543 32 372 0
2022-11-10 84511 39 372 1
2022-11-09 84472 42 371 0
2022-11-08 84430 58 371 0
2022-11-07 84372 30 371 0
2022-11-05 84342 27 371 0
2022-11-04 84315 63 371 0
2022-11-03 84252 43 371 0
2022-11-02 84209 33 371 0
2022-11-01 84176 41 371 0
2022-10-31 84135 25 371 0
2022-10-29 84110 56 371 0
2022-10-28 84054 79 371 0
2022-10-27 83975 86 371 0
2022-10-26 83889 147 371 0
2022-10-25 83742 124 371 0
2022-10-24 83618 56 371 1
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2022-10-21 83426 181 370 1
2022-10-20 83245 181 369 0
2022-10-19 83064 375 369 1
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2022-10-12 81634 238 366 0
2022-10-11 81396 192 366 0
2022-10-10 81204 160 366 3
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2022-10-06 80526 327 363 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: 0:03:09.979039