Germany: SK Leipzig (Sachsen)¶

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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 15:27:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Leipzig", weeks=5);
2023-03-07T15:27:40.088648 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 90 90 100 100 110 110 7-day incidence rate (per 100K people) 86.4 SK Leipzig, 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 0.05 0.10 0.15 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-SK Leipzig cases daily growth factor Germany-SK Leipzig cases daily growth factor (rolling mean) Germany-SK Leipzig 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-SK Leipzig deaths daily growth factor Germany-SK Leipzig deaths daily growth factor (rolling mean) Germany-SK Leipzig estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Leipzig doubling time cases (rolling mean) 0.0 60.2 120.4 180.6 daily change Germany-SK Leipzig new cases (rolling 7d mean) Germany-SK Leipzig new cases 0.000 0.301 0.602 0.903 daily change Germany-SK Leipzig new deaths (rolling 7d mean) Germany-SK Leipzig new deaths 0.000 0.228 0.456 0.684 0.911
In [4]:
overview(country="Germany", subregion="SK Leipzig");
2023-03-07T15:27:57.397303 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) 86.4 SK Leipzig, 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 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-SK Leipzig cases daily growth factor Germany-SK Leipzig cases daily growth factor (rolling mean) Germany-SK Leipzig 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-SK Leipzig deaths daily growth factor Germany-SK Leipzig deaths daily growth factor (rolling mean) Germany-SK Leipzig 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-SK Leipzig doubling time cases (rolling mean) Germany-SK Leipzig doubling time deaths (rolling mean) 0 1204 2407 daily change Germany-SK Leipzig new cases (rolling 7d mean) Germany-SK Leipzig new cases 0.00 6.02 12.04 daily change Germany-SK Leipzig new deaths (rolling 7d mean) Germany-SK Leipzig new deaths 0 239 479 718 958 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Leipzig", dates="2020-03-15:");
2023-03-07T15:30:41.228367 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: SK Leipzig SK Leipzig 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 SK Leipzig Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Leipzig")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Leipzig")
print(f'Population of country="Germany", subregion="SK Leipzig": {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="SK Leipzig": 601866 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 278893 66 893 0
2023-03-03 278827 81 893 0
2023-03-02 278746 53 893 0
2023-03-01 278693 129 893 0
2023-02-28 278564 191 893 0
2023-02-27 278373 168 893 0
2023-02-24 278205 78 893 0
2023-02-23 278127 94 893 0
2023-02-22 278033 100 893 0
2023-02-21 277933 153 893 0
2023-02-20 277780 39 893 0
2023-02-19 277741 63 893 0
2023-02-17 277678 85 893 0
2023-02-16 277593 151 893 1
2023-02-15 277442 83 892 0
2023-02-14 277359 158 892 0
2023-02-13 277201 64 892 0
2023-02-12 277137 57 892 0
2023-02-10 277080 84 892 0
2023-02-09 276996 46 892 0
2023-02-08 276950 85 892 0
2023-02-07 276865 148 892 0
2023-02-06 276717 88 892 0
2023-02-03 276629 107 892 0
2023-02-02 276522 76 892 0
2023-02-01 276446 94 892 0
2023-01-31 276352 168 892 0
2023-01-30 276184 97 892 0
2023-01-27 276087 59 892 0
2023-01-26 276028 74 892 0
2023-01-25 275954 73 892 0
2023-01-24 275881 140 892 0
2023-01-23 275741 64 892 0
2023-01-20 275677 49 892 0
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2023-01-13 275309 58 892 0
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2023-01-11 275207 100 892 0
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2023-01-09 274930 144 892 0
2023-01-06 274786 110 892 0
2023-01-05 274676 91 892 0
2023-01-04 274585 203 892 0
2023-01-03 274382 383 892 2
2023-01-02 273999 153 890 0
2022-12-30 273846 63 890 0
2022-12-29 273783 157 890 1
2022-12-28 273626 191 889 0
2022-12-27 273435 245 889 1
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2022-12-22 273038 190 887 0
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2022-12-19 272300 366 887 2
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2022-12-13 271231 435 885 1
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2022-12-08 270196 200 883 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: 0:04:35.167607