Germany: LK Lörrach (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 10:01:33 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Lörrach", weeks=5);
2023-03-07T10:01:51.259343 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 63.6 LK Lörrach, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Lörrach cases daily growth factor Germany-LK Lörrach cases daily growth factor (rolling mean) Germany-LK Lörrach estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Lörrach deaths daily growth factor Germany-LK Lörrach deaths daily growth factor (rolling mean) Germany-LK Lörrach 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 Lörrach doubling time cases (rolling mean) 0.00 22.94 45.89 daily change Germany-LK Lörrach new cases (rolling 7d mean) Germany-LK Lörrach new cases 0.000 0.229 0.459 0.688 0.918 daily change Germany-LK Lörrach new deaths (rolling 7d mean) Germany-LK Lörrach new deaths 0.000 0.322 0.644 0.966
In [4]:
overview(country="Germany", subregion="LK Lörrach");
2023-03-07T10:02:54.694098 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) 63.6 LK Lörrach, 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 2 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 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Lörrach cases daily growth factor Germany-LK Lörrach cases daily growth factor (rolling mean) Germany-LK Lörrach estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Lörrach deaths daily growth factor Germany-LK Lörrach deaths daily growth factor (rolling mean) Germany-LK Lörrach estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 10000 20000 30000 cases doubling time [days] Germany-LK Lörrach doubling time cases (rolling mean) Germany-LK Lörrach doubling time deaths (rolling mean) 0 459 918 1377 daily change Germany-LK Lörrach new cases (rolling 7d mean) Germany-LK Lörrach new cases 0.00 4.59 9.18 daily change Germany-LK Lörrach new deaths (rolling 7d mean) Germany-LK Lörrach new deaths 0.0 162.8 325.7 488.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Lörrach", dates="2020-03-15:");
2023-03-07T10:04:54.714964 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 Lörrach LK Lörrach 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 Lörrach Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Lörrach")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Lörrach")
print(f'Population of country="Germany", subregion="LK Lörrach": {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 Lörrach": 229445 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 94618 17 480 0
2023-03-03 94601 11 480 0
2023-03-02 94590 26 480 0
2023-03-01 94564 44 480 0
2023-02-28 94520 48 480 0
2023-02-27 94472 66 480 1
2023-02-24 94406 18 479 0
2023-02-23 94388 32 479 0
2023-02-22 94356 35 479 0
2023-02-21 94321 45 479 0
2023-02-20 94276 42 479 0
2023-02-17 94234 26 479 1
2023-02-16 94208 27 478 0
2023-02-15 94181 34 478 0
2023-02-14 94147 31 478 0
2023-02-13 94116 38 478 0
2023-02-10 94078 18 478 0
2023-02-09 94060 33 478 0
2023-02-08 94027 25 478 0
2023-02-07 94002 17 478 0
2023-02-06 93985 31 478 0
2023-02-03 93954 21 478 0
2023-02-02 93933 13 478 0
2023-02-01 93920 30 478 0
2023-01-31 93890 34 478 0
2023-01-30 93856 31 478 1
2023-01-27 93825 18 477 0
2023-01-26 93807 21 477 0
2023-01-25 93786 20 477 0
2023-01-24 93766 28 477 0
2023-01-23 93738 18 477 0
2023-01-20 93720 14 477 0
2023-01-19 93706 11 477 0
2023-01-18 93695 32 477 0
2023-01-17 93663 51 477 0
2023-01-16 93612 35 477 0
2023-01-13 93577 25 477 0
2023-01-12 93552 28 477 0
2023-01-11 93524 41 477 1
2023-01-10 93483 124 476 2
2023-01-09 93359 153 474 1
2022-12-30 93206 15 473 0
2022-12-29 93191 71 473 0
2022-12-28 93120 108 473 1
2022-12-27 93012 113 472 0
2022-12-23 92899 55 472 2
2022-12-22 92844 153 470 0
2022-12-21 92691 55 470 0
2022-12-20 92636 84 470 1
2022-12-19 92552 116 469 0
2022-12-16 92436 53 469 0
2022-12-15 92383 88 469 0
2022-12-14 92295 78 469 0
2022-12-13 92217 104 469 1
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2022-12-09 91996 58 466 0
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2022-12-07 91877 85 466 0
2022-12-06 91792 105 466 1
2022-12-05 91687 122 465 0
2022-12-02 91565 80 465 2
2022-12-01 91485 88 463 0
2022-11-30 91397 65 463 0
2022-11-29 91332 91 463 0
2022-11-28 91241 87 463 0
2022-11-25 91154 62 463 0
2022-11-24 91092 62 463 0
2022-11-23 91030 87 463 1
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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: 0:05:54.535818