Germany: LK Biberach (Baden-Württemberg)¶

  • Homepage of project: https://oscovida.github.io
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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 09:44:28 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Biberach", weeks=5);
2023-03-07T09:44:46.899189 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 7-day incidence rate (per 100K people) 72.3 LK Biberach, 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 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 Biberach cases daily growth factor Germany-LK Biberach cases daily growth factor (rolling mean) Germany-LK Biberach 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 Biberach deaths daily growth factor Germany-LK Biberach deaths daily growth factor (rolling mean) Germany-LK Biberach estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Biberach doubling time cases (rolling mean) 0.00 20.32 40.65 60.97 daily change Germany-LK Biberach new cases (rolling 7d mean) Germany-LK Biberach new cases 0.000 0.203 0.406 0.610 0.813 daily change Germany-LK Biberach new deaths (rolling 7d mean) Germany-LK Biberach new deaths 0.000 0.392 0.783
In [4]:
overview(country="Germany", subregion="LK Biberach");
2023-03-07T09:45:06.585342 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) 72.3 LK Biberach, 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 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 Biberach cases daily growth factor Germany-LK Biberach cases daily growth factor (rolling mean) Germany-LK Biberach 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 Biberach deaths daily growth factor Germany-LK Biberach deaths daily growth factor (rolling mean) Germany-LK Biberach 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 cases doubling time [days] Germany-LK Biberach doubling time cases (rolling mean) Germany-LK Biberach doubling time deaths (rolling mean) 0 406 813 daily change Germany-LK Biberach new cases (rolling 7d mean) Germany-LK Biberach new cases 0.000 2.032 4.065 6.097 daily change Germany-LK Biberach new deaths (rolling 7d mean) Germany-LK Biberach new deaths 0.0 131.1 262.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Biberach", dates="2020-03-15:");
2023-03-07T09:50:02.404802 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 Biberach LK Biberach 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 Biberach Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Biberach")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Biberach")
print(f'Population of country="Germany", subregion="LK Biberach": {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 Biberach": 203244 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 99324 26 336 0
2023-03-03 99298 14 336 0
2023-03-02 99284 20 336 0
2023-03-01 99264 41 336 0
2023-02-28 99223 46 336 0
2023-02-27 99177 61 336 0
2023-02-24 99116 26 336 0
2023-02-23 99090 52 336 0
2023-02-22 99038 63 336 0
2023-02-21 98975 40 336 1
2023-02-20 98935 62 335 0
2023-02-17 98873 41 335 0
2023-02-16 98832 35 335 0
2023-02-15 98797 38 335 0
2023-02-14 98759 52 335 0
2023-02-13 98707 59 335 0
2023-02-10 98648 31 335 0
2023-02-09 98617 37 335 0
2023-02-08 98580 43 335 0
2023-02-07 98537 64 335 0
2023-02-06 98473 61 335 1
2023-02-03 98412 40 334 0
2023-02-02 98372 21 334 0
2023-02-01 98351 64 334 0
2023-01-31 98287 48 334 0
2023-01-30 98239 38 334 1
2023-01-27 98201 18 333 0
2023-01-26 98183 32 333 0
2023-01-25 98151 34 333 0
2023-01-24 98117 44 333 0
2023-01-23 98073 51 333 0
2023-01-20 98022 21 333 0
2023-01-19 98001 12 333 0
2023-01-18 97989 21 333 0
2023-01-17 97968 38 333 0
2023-01-16 97930 29 333 0
2023-01-15 97901 3 333 0
2023-01-13 97898 13 333 0
2023-01-12 97885 9 333 0
2023-01-11 97876 35 333 0
2023-01-10 97841 50 333 0
2023-01-09 97791 43 333 0
2023-01-08 97748 1 333 0
2023-01-07 97747 10 333 0
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2023-01-04 97720 32 333 0
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2022-12-31 97618 2 333 0
2022-12-30 97616 19 333 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: 0:06:22.142148