Germany: LK Rhein-Hunsrück-Kreis (Rheinland-Pfalz)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 14:56:38 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rhein-Hunsrück-Kreis", weeks=5);
2023-03-07T14:56:50.726886 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 200 200 300 300 7-day incidence rate (per 100K people) 109.9 LK Rhein-Hunsrück-Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 80 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Rhein-Hunsrück-Kreis cases daily growth factor Germany-LK Rhein-Hunsrück-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Hunsrück-Kreis 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 Rhein-Hunsrück-Kreis deaths daily growth factor Germany-LK Rhein-Hunsrück-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Hunsrück-Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Rhein-Hunsrück-Kreis doubling time cases (rolling mean) 0.0 20.8 41.5 62.3 83.0 daily change Germany-LK Rhein-Hunsrück-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Hunsrück-Kreis new cases 0.000 0.208 0.415 0.623 0.830 daily change Germany-LK Rhein-Hunsrück-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Hunsrück-Kreis new deaths 0.000 0.286 0.573 0.859
In [4]:
overview(country="Germany", subregion="LK Rhein-Hunsrück-Kreis");
2023-03-07T14:57:04.466892 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 4000 4000 7-day incidence rate (per 100K people) 109.9 LK Rhein-Hunsrück-Kreis, 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 0 500 1000 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 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Rhein-Hunsrück-Kreis cases daily growth factor Germany-LK Rhein-Hunsrück-Kreis cases daily growth factor (rolling mean) Germany-LK Rhein-Hunsrück-Kreis 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 Rhein-Hunsrück-Kreis deaths daily growth factor Germany-LK Rhein-Hunsrück-Kreis deaths daily growth factor (rolling mean) Germany-LK Rhein-Hunsrück-Kreis 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 cases doubling time [days] Germany-LK Rhein-Hunsrück-Kreis doubling time cases (rolling mean) Germany-LK Rhein-Hunsrück-Kreis doubling time deaths (rolling mean) 0 519 1038 daily change Germany-LK Rhein-Hunsrück-Kreis new cases (rolling 7d mean) Germany-LK Rhein-Hunsrück-Kreis new cases 0.000 1.038 2.075 3.113 4.151 daily change Germany-LK Rhein-Hunsrück-Kreis new deaths (rolling 7d mean) Germany-LK Rhein-Hunsrück-Kreis new deaths 0.0 57.4 114.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rhein-Hunsrück-Kreis", dates="2020-03-15:");
2023-03-07T14:59:46.969735 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 1000 1000 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Rhein-Hunsrück-Kreis LK Rhein-Hunsrück-Kreis 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 Rhein-Hunsrück-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rhein-Hunsrück-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rhein-Hunsrück-Kreis")
print(f'Population of country="Germany", subregion="LK Rhein-Hunsrück-Kreis": {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 Rhein-Hunsrück-Kreis": 103767 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 48694 11 174 0
2023-03-03 48683 5 174 0
2023-03-02 48678 13 174 0
2023-03-01 48665 37 174 0
2023-02-28 48628 48 174 0
2023-02-27 48580 94 174 0
2023-02-24 48486 65 174 0
2023-02-23 48421 63 174 0
2023-02-22 48358 84 174 0
2023-02-21 48274 67 174 0
2023-02-17 48207 28 174 0
2023-02-16 48179 34 174 0
2023-02-15 48145 33 174 0
2023-02-14 48112 35 174 1
2023-02-13 48077 48 173 0
2023-02-10 48029 29 173 0
2023-02-09 48000 33 173 0
2023-02-08 47967 29 173 0
2023-02-07 47938 34 173 0
2023-02-06 47904 37 173 0
2023-02-03 47867 18 173 0
2023-02-02 47849 12 173 0
2023-02-01 47837 21 173 0
2023-01-31 47816 19 173 0
2023-01-30 47797 30 173 0
2023-01-27 47767 11 173 0
2023-01-26 47756 13 173 0
2023-01-25 47743 10 173 0
2023-01-24 47733 18 173 0
2023-01-23 47715 28 173 0
2023-01-20 47687 8 173 0
2023-01-19 47679 16 173 0
2023-01-18 47663 15 173 0
2023-01-17 47648 14 173 0
2023-01-16 47634 29 173 0
2023-01-12 47605 15 173 0
2023-01-11 47590 10 173 0
2023-01-10 47580 20 173 1
2023-01-09 47560 25 172 0
2023-01-06 47535 22 172 2
2023-01-05 47513 20 170 1
2023-01-04 47493 24 169 0
2023-01-03 47469 37 169 0
2023-01-02 47432 43 169 1
2022-12-30 47389 23 168 0
2022-12-29 47366 37 168 1
2022-12-28 47329 45 167 0
2022-12-27 47284 91 167 1
2022-12-23 47193 28 166 0
2022-12-22 47165 37 166 0
2022-12-21 47128 37 166 0
2022-12-20 47091 38 166 0
2022-12-19 47053 76 166 0
2022-12-16 46977 26 166 0
2022-12-15 46951 25 166 0
2022-12-14 46926 52 166 1
2022-12-13 46874 47 165 0
2022-12-12 46827 57 165 0
2022-12-09 46770 25 165 0
2022-12-08 46745 33 165 0
2022-12-07 46712 35 165 1
2022-12-06 46677 37 164 0
2022-12-05 46640 41 164 1
2022-12-02 46599 15 163 0
2022-12-01 46584 23 163 0
2022-11-30 46561 26 163 0
2022-11-29 46535 29 163 0
2022-11-28 46506 57 163 1
2022-11-25 46449 31 162 0
2022-11-24 46418 32 162 0
2022-11-23 46386 23 162 1
2022-11-22 46363 41 161 0
2022-11-21 46322 70 161 2
2022-11-18 46252 30 159 2
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2022-10-10 42835 339 147 0
2022-10-07 42496 204 147 0
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2021-01-03 1572 20 36 0
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2021-01-01 1537 15 36 1
2020-12-31 1522 25 35 1
2020-12-30 1497 11 34 0
2020-12-29 1486 26 34 4
2020-12-28 1460 14 30 1
2020-12-25 1446 30 29 1
2020-12-24 1416 39 28 1
2020-12-23 1377 15 27 0
2020-12-22 1362 20 27 2
2020-12-21 1342 36 25 4
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2020-12-18 1260 25 20 2
2020-12-17 1235 37 18 1
2020-12-16 1198 9 17 0
2020-12-15 1189 20 17 0
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2020-12-06 1009 6 12 0
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2020-12-02 941 16 12 0
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2020-08-26 200 1 5 0
2020-08-25 199 6 5 0
2020-08-23 193 3 5 0
2020-08-21 190 1 5 0
2020-08-20 189 4 5 0
2020-08-19 185 1 5 0
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2020-08-16 181 1 5 0
2020-08-14 180 2 5 0
2020-08-12 178 2 5 0
2020-08-11 176 1 5 0
2020-08-10 175 1 5 0
2020-08-08 174 3 5 0
2020-08-05 171 2 5 0
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2020-07-15 168 1 5 0
2020-07-09 167 1 5 0
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2020-05-29 162 1 5 0
2020-05-05 161 1 5 0
2020-04-29 160 1 5 0
2020-04-23 159 1 5 1
2020-04-22 158 3 4 1
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2020-04-12 137 1 2 1
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2020-04-09 133 3 1 1
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2020-03-27 89 10 0 0
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2020-03-16 25 3 0 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}")