Germany: LK Garmisch-Partenkirchen (Bayern)¶

  • 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 10:48:00 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Garmisch-Partenkirchen", weeks=5);
2023-03-07T10:48:14.355000 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 74.8 LK Garmisch-Partenkirchen, 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 0.00 0.25 0.50 0.75 1.00 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 Garmisch-Partenkirchen cases daily growth factor Germany-LK Garmisch-Partenkirchen cases daily growth factor (rolling mean) Germany-LK Garmisch-Partenkirchen 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 Garmisch-Partenkirchen deaths daily growth factor Germany-LK Garmisch-Partenkirchen deaths daily growth factor (rolling mean) Germany-LK Garmisch-Partenkirchen 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 Garmisch-Partenkirchen doubling time cases (rolling mean) 0.00 8.82 17.65 26.47 daily change Germany-LK Garmisch-Partenkirchen new cases (rolling 7d mean) Germany-LK Garmisch-Partenkirchen new cases 0.000 0.221 0.441 0.662 0.882 daily change Germany-LK Garmisch-Partenkirchen new deaths (rolling 7d mean) Germany-LK Garmisch-Partenkirchen new deaths 0.000 0.294 0.587 0.881
In [4]:
overview(country="Germany", subregion="LK Garmisch-Partenkirchen");
2023-03-07T10:48:29.387611 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 3000 3000 7-day incidence rate (per 100K people) 74.8 LK Garmisch-Partenkirchen, 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 0 200 400 600 800 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 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 Garmisch-Partenkirchen cases daily growth factor Germany-LK Garmisch-Partenkirchen cases daily growth factor (rolling mean) Germany-LK Garmisch-Partenkirchen 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 Garmisch-Partenkirchen deaths daily growth factor Germany-LK Garmisch-Partenkirchen deaths daily growth factor (rolling mean) Germany-LK Garmisch-Partenkirchen 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 Garmisch-Partenkirchen doubling time cases (rolling mean) Germany-LK Garmisch-Partenkirchen doubling time deaths (rolling mean) 0.0 176.5 352.9 529.4 705.9 daily change Germany-LK Garmisch-Partenkirchen new cases (rolling 7d mean) Germany-LK Garmisch-Partenkirchen new cases 0.00 2.21 4.41 6.62 8.82 daily change Germany-LK Garmisch-Partenkirchen new deaths (rolling 7d mean) Germany-LK Garmisch-Partenkirchen new deaths 0.0 24.2 48.4 72.6 96.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Garmisch-Partenkirchen", dates="2020-03-15:");
2023-03-07T10:50:53.944029 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 Garmisch-Partenkirchen LK Garmisch-Partenkirchen 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 Garmisch-Partenkirchen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Garmisch-Partenkirchen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Garmisch-Partenkirchen")
print(f'Population of country="Germany", subregion="LK Garmisch-Partenkirchen": {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 Garmisch-Partenkirchen": 88232 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 40314 8 172 0
2023-03-03 40306 16 172 0
2023-03-02 40290 3 172 0
2023-03-01 40287 11 172 0
2023-02-28 40276 28 172 0
2023-02-27 40248 19 172 0
2023-02-24 40229 10 172 0
2023-02-23 40219 18 172 0
2023-02-22 40201 4 172 0
2023-02-21 40197 8 172 0
2023-02-20 40189 26 172 0
2023-02-17 40163 10 172 0
2023-02-16 40153 19 172 0
2023-02-15 40134 20 172 0
2023-02-14 40114 14 172 0
2023-02-13 40100 21 172 1
2023-02-10 40079 7 171 0
2023-02-09 40072 10 171 0
2023-02-08 40062 25 171 0
2023-02-06 40037 20 171 0
2023-02-03 40017 4 171 0
2023-02-02 40013 12 171 0
2023-02-01 40001 9 171 0
2023-01-31 39992 3 171 0
2023-01-30 39989 12 171 0
2023-01-27 39977 2 171 0
2023-01-26 39975 8 171 0
2023-01-25 39967 5 171 0
2023-01-24 39962 9 171 0
2023-01-23 39953 9 171 0
2023-01-20 39944 8 171 0
2023-01-19 39936 3 171 0
2023-01-18 39933 11 171 0
2023-01-17 39922 11 171 1
2023-01-16 39911 13 170 0
2023-01-13 39898 8 170 0
2023-01-12 39890 6 170 0
2023-01-11 39884 14 170 0
2023-01-10 39870 4 170 0
2023-01-09 39866 19 170 0
2023-01-05 39847 28 170 0
2023-01-04 39819 1 170 0
2023-01-03 39818 22 170 0
2023-01-02 39796 18 170 0
2022-12-30 39778 8 170 0
2022-12-29 39770 6 170 0
2022-12-28 39764 19 170 0
2022-12-27 39745 32 170 0
2022-12-23 39713 15 170 0
2022-12-22 39698 7 170 0
2022-12-21 39691 32 170 0
2022-12-20 39659 8 170 0
2022-12-19 39651 23 170 0
2022-12-16 39628 6 170 0
2022-12-15 39622 19 170 0
2022-12-14 39603 55 170 0
2022-12-12 39548 35 170 0
2022-12-09 39513 20 170 0
2022-12-08 39493 7 170 0
2022-12-07 39486 30 170 0
2022-12-06 39456 13 170 0
2022-12-05 39443 25 170 0
2022-12-02 39418 11 170 0
2022-12-01 39407 10 170 0
2022-11-30 39397 15 170 0
2022-11-29 39382 10 170 0
2022-11-28 39372 14 170 0
2022-11-25 39358 9 170 0
2022-11-24 39349 8 170 0
2022-11-23 39341 7 170 0
2022-11-22 39334 11 170 0
2022-11-21 39323 10 170 0
2022-11-18 39313 14 170 0
2022-11-17 39299 8 170 0
2022-11-16 39291 21 170 0
2022-11-15 39270 27 170 0
2022-11-14 39243 30 170 0
2022-11-11 39213 21 170 0
2022-11-10 39192 16 170 0
2022-11-09 39176 11 170 0
2022-11-08 39165 20 170 0
2022-11-07 39145 17 170 0
2022-11-04 39128 19 170 0
2022-11-03 39109 29 170 0
2022-11-02 39080 27 170 1
2022-10-31 39053 43 169 0
2022-10-28 39010 34 169 0
2022-10-27 38976 19 169 1
2022-10-26 38957 42 168 0
2022-10-25 38915 54 168 0
2022-10-24 38861 68 168 0
2022-10-22 38793 29 168 0
2022-10-21 38764 48 168 0
2022-10-20 38716 57 168 0
2022-10-19 38659 87 168 0
2022-10-18 38572 85 168 1
2022-10-17 38487 105 167 1
2022-10-15 38382 31 166 0
2022-10-14 38351 89 166 0
2022-10-13 38262 82 166 0
2022-10-12 38180 190 166 1
2022-10-11 37990 159 165 0
2022-10-10 37831 219 165 0
2022-10-08 37612 100 165 0
2022-10-07 37512 229 165 0
2022-10-06 37283 175 165 0
2022-10-05 37108 173 165 1
2022-10-04 36935 151 164 0
2022-10-03 36784 84 164 0
2022-09-30 36700 113 164 2
2022-09-29 36587 121 162 1
2022-09-28 36466 158 161 0
2022-09-27 36308 143 161 1
2022-09-26 36165 115 160 0
2022-09-23 36050 65 160 0
2022-09-22 35985 48 160 0
2022-09-21 35937 52 160 1
2022-09-20 35885 68 159 1
2022-09-19 35817 62 158 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: 0:03:06.284536