Germany: LK Herzogtum Lauenburg (Schleswig-Holstein)¶

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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 15:46:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Herzogtum Lauenburg", weeks=5);
2023-03-07T15:46:23.966649 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 35 35 40 40 45 45 50 50 7-day incidence rate (per 100K people) 30.4 LK Herzogtum Lauenburg, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 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 Herzogtum Lauenburg cases daily growth factor Germany-LK Herzogtum Lauenburg cases daily growth factor (rolling mean) Germany-LK Herzogtum Lauenburg 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 Herzogtum Lauenburg deaths daily growth factor Germany-LK Herzogtum Lauenburg deaths daily growth factor (rolling mean) Germany-LK Herzogtum Lauenburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Herzogtum Lauenburg doubling time cases (rolling mean) 0.00 10.04 20.08 daily change Germany-LK Herzogtum Lauenburg new cases (rolling 7d mean) Germany-LK Herzogtum Lauenburg new cases 0.000 0.201 0.402 0.602 0.803 daily change Germany-LK Herzogtum Lauenburg new deaths (rolling 7d mean) Germany-LK Herzogtum Lauenburg new deaths 0.000 0.218 0.436 0.654 0.872
In [4]:
overview(country="Germany", subregion="LK Herzogtum Lauenburg");
2023-03-07T15:46:36.272710 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 30.4 LK Herzogtum Lauenburg, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.5 1.0 1.5 2.0 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 Herzogtum Lauenburg cases daily growth factor Germany-LK Herzogtum Lauenburg cases daily growth factor (rolling mean) Germany-LK Herzogtum Lauenburg 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 Herzogtum Lauenburg deaths daily growth factor Germany-LK Herzogtum Lauenburg deaths daily growth factor (rolling mean) Germany-LK Herzogtum Lauenburg estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Herzogtum Lauenburg doubling time cases (rolling mean) Germany-LK Herzogtum Lauenburg doubling time deaths (rolling mean) 0 201 402 602 803 daily change Germany-LK Herzogtum Lauenburg new cases (rolling 7d mean) Germany-LK Herzogtum Lauenburg new cases 0.000 1.004 2.008 3.012 4.016 daily change Germany-LK Herzogtum Lauenburg new deaths (rolling 7d mean) Germany-LK Herzogtum Lauenburg new deaths 0.0 38.0 75.9 113.9 151.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Herzogtum Lauenburg", dates="2020-03-15:");
2023-03-07T15:49:08.879075 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 Herzogtum Lauenburg LK Herzogtum Lauenburg 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 Herzogtum Lauenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Herzogtum Lauenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Herzogtum Lauenburg")
print(f'Population of country="Germany", subregion="LK Herzogtum Lauenburg": {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 Herzogtum Lauenburg": 200819 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 76464 7 216 0
2023-03-03 76457 6 216 0
2023-03-02 76451 7 216 0
2023-03-01 76444 18 216 0
2023-02-28 76426 23 216 0
2023-02-27 76403 14 216 0
2023-02-24 76389 14 216 0
2023-02-23 76375 16 216 0
2023-02-22 76359 15 216 0
2023-02-21 76344 23 216 0
2023-02-20 76321 21 216 1
2023-02-17 76300 6 215 0
2023-02-16 76294 12 215 0
2023-02-15 76282 14 215 0
2023-02-14 76268 27 215 0
2023-02-13 76241 20 215 0
2023-02-10 76221 12 215 0
2023-02-09 76209 9 215 0
2023-02-08 76200 10 215 0
2023-02-07 76190 24 215 0
2023-02-06 76166 23 215 0
2023-02-03 76143 8 215 0
2023-02-02 76135 27 215 0
2023-02-01 76108 20 215 0
2023-01-31 76088 16 215 0
2023-01-30 76072 12 215 0
2023-01-27 76060 7 215 0
2023-01-26 76053 12 215 0
2023-01-25 76041 14 215 1
2023-01-24 76027 21 214 0
2023-01-23 76006 22 214 0
2023-01-20 75984 10 214 0
2023-01-19 75974 26 214 0
2023-01-18 75948 6 214 0
2023-01-17 75942 20 214 0
2023-01-16 75922 27 214 0
2023-01-14 75895 1 214 1
2023-01-13 75894 9 213 0
2023-01-12 75885 19 213 0
2023-01-11 75866 21 213 0
2023-01-10 75845 25 213 0
2023-01-09 75820 49 213 0
2023-01-06 75771 24 213 0
2023-01-05 75747 27 213 0
2023-01-04 75720 37 213 0
2023-01-03 75683 64 213 1
2023-01-02 75619 74 212 0
2022-12-30 75545 50 212 0
2022-12-29 75495 80 212 0
2022-12-28 75415 102 212 1
2022-12-27 75313 121 211 0
2022-12-23 75192 63 211 0
2022-12-22 75129 70 211 1
2022-12-21 75059 74 210 1
2022-12-20 74985 97 209 1
2022-12-19 74888 126 208 0
2022-12-17 74762 1 208 0
2022-12-16 74761 60 208 0
2022-12-15 74701 72 208 0
2022-12-14 74629 93 208 0
2022-12-13 74536 115 208 0
2022-12-12 74421 107 208 0
2022-12-09 74314 25 208 0
2022-12-08 74289 97 208 0
2022-12-07 74192 72 208 0
2022-12-06 74120 99 208 0
2022-12-05 74021 62 208 0
2022-12-04 73959 23 208 0
2022-12-03 73936 21 208 0
2022-12-02 73915 77 208 0
2022-12-01 73838 61 208 0
2022-11-30 73777 63 208 0
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2022-11-28 73619 76 208 1
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2022-11-16 72966 24 207 0
2022-11-15 72942 101 207 0
2022-11-14 72841 109 207 0
2022-11-13 72732 9 207 0
2022-11-12 72723 22 207 0
2022-11-11 72701 81 207 0
2022-11-10 72620 102 207 0
2022-11-09 72518 98 207 0
2022-11-08 72420 122 207 0
2022-11-07 72298 101 207 0
2022-11-06 72197 14 207 0
2022-11-05 72183 23 207 0
2022-11-04 72160 95 207 1
2022-11-03 72065 117 206 0
2022-11-02 71948 198 206 0
2022-11-01 71750 135 206 1
2022-10-31 71615 30 205 0
2022-10-30 71585 41 205 0
2022-10-29 71544 49 205 1
2022-10-28 71495 129 204 0
2022-10-27 71366 74 204 0
2022-10-26 71292 107 204 2
2022-10-25 71185 167 202 0
2022-10-24 71018 162 202 1
2022-10-23 70856 24 201 0
2022-10-22 70832 59 201 0
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2022-10-20 70652 118 201 1
2022-10-19 70534 167 200 0
2022-10-18 70367 233 200 1
2022-10-17 70134 170 199 0
2022-10-16 69964 30 199 0
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2022-10-13 69684 153 199 2
2022-10-12 69531 172 197 0
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2022-10-10 69121 203 196 2
2022-10-09 68918 66 194 0
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2022-10-07 68734 174 194 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: 0:03:07.979340