Germany: SK Solingen (Nordrhein-Westfalen)¶

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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 14:32:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Solingen", weeks=5);
2023-03-07T14:33:05.660389 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 140 140 160 160 180 180 200 200 220 220 7-day incidence rate (per 100K people) 142.8 SK Solingen, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 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 Solingen cases daily growth factor Germany-SK Solingen cases daily growth factor (rolling mean) Germany-SK Solingen estimated R (using cases) 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 deaths) Germany-SK Solingen deaths daily growth factor Germany-SK Solingen deaths daily growth factor (rolling mean) Germany-SK Solingen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-SK Solingen doubling time cases (rolling mean) Germany-SK Solingen doubling time deaths (rolling mean) 0.0 31.8 63.6 95.4 daily change Germany-SK Solingen new cases (rolling 7d mean) Germany-SK Solingen new cases 0.000 0.795 1.590 2.384 daily change Germany-SK Solingen new deaths (rolling 7d mean) Germany-SK Solingen new deaths 0.0 198.4 396.8 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="SK Solingen");
2023-03-07T14:33:22.477809 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) 142.8 SK Solingen, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Solingen cases daily growth factor Germany-SK Solingen cases daily growth factor (rolling mean) Germany-SK Solingen estimated R (using cases) 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 deaths) Germany-SK Solingen deaths daily growth factor Germany-SK Solingen deaths daily growth factor (rolling mean) Germany-SK Solingen 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 Solingen doubling time cases (rolling mean) Germany-SK Solingen doubling time deaths (rolling mean) 0 318 636 954 daily change Germany-SK Solingen new cases (rolling 7d mean) Germany-SK Solingen new cases 0.000 3.179 6.358 daily change Germany-SK Solingen new deaths (rolling 7d mean) Germany-SK Solingen new deaths 0 90 180 270 360 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Solingen", dates="2020-03-15:");
2023-03-07T14:36:42.805207 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 Solingen SK Solingen 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 Solingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Solingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Solingen")
print(f'Population of country="Germany", subregion="SK Solingen": {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 Solingen": 158957 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 83661 71 396 0
2023-03-03 83590 42 396 0
2023-03-02 83548 29 396 1
2023-03-01 83519 21 395 0
2023-02-28 83498 64 395 1
2023-02-27 83434 81 394 0
2023-02-24 83353 42 394 0
2023-02-23 83311 66 394 0
2023-02-22 83245 41 394 1
2023-02-21 83204 97 393 0
2023-02-17 83107 47 393 0
2023-02-16 83060 65 393 0
2023-02-15 82995 59 393 0
2023-02-14 82936 70 393 1
2023-02-13 82866 83 392 1
2023-02-10 82783 39 391 0
2023-02-09 82744 41 391 3
2023-02-08 82703 44 388 0
2023-02-07 82659 90 388 0
2023-02-06 82569 80 388 1
2023-02-03 82489 54 387 0
2023-02-02 82435 57 387 0
2023-02-01 82378 77 387 0
2023-01-31 82301 69 387 1
2023-01-30 82232 86 386 0
2023-01-27 82146 32 386 1
2023-01-26 82114 60 385 1
2023-01-25 82054 56 384 1
2023-01-24 81998 95 383 2
2023-01-23 81903 73 381 0
2023-01-20 81830 31 381 0
2023-01-19 81799 33 381 0
2023-01-18 81766 42 381 0
2023-01-17 81724 28 381 0
2023-01-16 81696 69 381 1
2023-01-13 81627 38 380 1
2023-01-12 81589 46 379 0
2023-01-11 81543 30 379 0
2023-01-10 81513 56 379 0
2023-01-09 81457 100 379 0
2023-01-06 81357 57 379 0
2023-01-05 81300 46 379 0
2023-01-04 81254 67 379 0
2023-01-03 81187 128 379 0
2023-01-02 81059 126 379 2
2022-12-30 80933 92 377 1
2022-12-29 80841 94 376 0
2022-12-28 80747 120 376 0
2022-12-27 80627 314 376 2
2022-12-23 80313 103 374 0
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2022-12-09 78800 71 367 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: 0:04:05.131843