Germany: LK Heidenheim (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 09:54:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Heidenheim", weeks=5);
2023-03-07T09:54:56.702791 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 59.4 LK Heidenheim, 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 0.2 0.4 0.6 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 R & growth factor (based on cases) Germany-LK Heidenheim cases daily growth factor Germany-LK Heidenheim cases daily growth factor (rolling mean) Germany-LK Heidenheim estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Heidenheim deaths daily growth factor Germany-LK Heidenheim deaths daily growth factor (rolling mean) Germany-LK Heidenheim 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 Heidenheim doubling time cases (rolling mean) 0.00 13.30 26.59 39.89 53.18 daily change Germany-LK Heidenheim new cases (rolling 7d mean) Germany-LK Heidenheim new cases 0.000 0.266 0.532 0.798 daily change Germany-LK Heidenheim new deaths (rolling 7d mean) Germany-LK Heidenheim new deaths 0.000 0.321 0.642 0.964
In [4]:
overview(country="Germany", subregion="LK Heidenheim");
2023-03-07T09:55:13.794866 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) 59.4 LK Heidenheim, 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 2 4 6 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 R & growth factor (based on cases) Germany-LK Heidenheim cases daily growth factor Germany-LK Heidenheim cases daily growth factor (rolling mean) Germany-LK Heidenheim estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Heidenheim deaths daily growth factor Germany-LK Heidenheim deaths daily growth factor (rolling mean) Germany-LK Heidenheim 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 Heidenheim doubling time cases (rolling mean) Germany-LK Heidenheim doubling time deaths (rolling mean) 0.0 265.9 531.8 daily change Germany-LK Heidenheim new cases (rolling 7d mean) Germany-LK Heidenheim new cases 0.00 2.66 5.32 7.98 daily change Germany-LK Heidenheim new deaths (rolling 7d mean) Germany-LK Heidenheim new deaths 0.0 120.3 240.7 361.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Heidenheim", dates="2020-03-15:");
2023-03-07T09:57:42.773839 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 Heidenheim LK Heidenheim 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 Heidenheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Heidenheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Heidenheim")
print(f'Population of country="Germany", subregion="LK Heidenheim": {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 Heidenheim": 132958 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 63635 7 383 0
2023-03-02 63628 15 383 0
2023-03-01 63613 25 383 0
2023-02-28 63588 32 383 0
2023-02-27 63556 61 383 0
2023-02-24 63495 16 383 0
2023-02-23 63479 21 383 0
2023-02-22 63458 26 383 0
2023-02-21 63432 40 383 0
2023-02-20 63392 48 383 0
2023-02-17 63344 23 383 1
2023-02-16 63321 20 382 0
2023-02-15 63301 38 382 0
2023-02-14 63263 25 382 1
2023-02-13 63238 66 381 1
2023-02-10 63172 30 380 0
2023-02-09 63142 12 380 0
2023-02-08 63130 28 380 0
2023-02-07 63102 24 380 0
2023-02-06 63078 45 380 0
2023-02-03 63033 15 380 0
2023-02-02 63018 22 380 0
2023-02-01 62996 24 380 0
2023-01-31 62972 20 380 1
2023-01-30 62952 30 379 0
2023-01-27 62922 12 379 0
2023-01-26 62910 12 379 0
2023-01-25 62898 25 379 0
2023-01-24 62873 14 379 0
2023-01-23 62859 33 379 0
2023-01-20 62826 9 379 0
2023-01-19 62817 26 379 1
2023-01-18 62791 25 378 0
2023-01-17 62766 23 378 0
2023-01-16 62743 32 378 0
2023-01-13 62711 16 378 0
2023-01-12 62695 23 378 1
2023-01-11 62672 29 377 0
2023-01-10 62643 55 377 0
2023-01-09 62588 38 377 0
2023-01-05 62550 26 377 0
2023-01-04 62524 19 377 1
2023-01-03 62505 27 376 0
2023-01-02 62478 50 376 0
2022-12-30 62428 14 376 0
2022-12-29 62414 20 376 0
2022-12-28 62394 26 376 0
2022-12-27 62368 79 376 1
2022-12-23 62289 25 375 0
2022-12-22 62264 22 375 0
2022-12-21 62242 46 375 1
2022-12-20 62196 21 374 0
2022-12-19 62175 74 374 2
2022-12-16 62101 78 372 0
2022-12-13 62023 11 372 0
2022-12-12 62012 39 372 1
2022-12-11 61973 18 371 0
2022-12-10 61955 9 371 0
2022-12-09 61946 8 371 0
2022-12-08 61938 31 371 1
2022-12-07 61907 22 370 1
2022-12-06 61885 106 369 0
2022-12-05 61779 27 369 0
2022-12-04 61752 12 369 3
2022-12-03 61740 10 366 0
2022-12-02 61730 22 366 1
2022-12-01 61708 18 365 0
2022-11-30 61690 25 365 1
2022-11-29 61665 35 364 0
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2022-11-03 60941 30 355 0
2022-11-02 60911 40 355 0
2022-11-01 60871 32 355 1
2022-10-31 60839 54 354 1
2022-10-30 60785 6 353 0
2022-10-29 60779 16 353 0
2022-10-28 60763 27 353 1
2022-10-27 60736 53 352 0
2022-10-26 60683 70 352 0
2022-10-25 60613 162 352 1
2022-10-24 60451 68 351 0
2022-10-23 60383 19 351 2
2022-10-22 60364 65 349 1
2022-10-21 60299 116 348 0
2022-10-20 60183 100 348 3
2022-10-19 60083 134 345 0
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2022-10-17 59686 171 345 2
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2022-10-08 58379 153 339 1
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2022-10-02 57332 27 334 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: 0:03:14.590730