Germany: SK Herne (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 14:25:40 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Herne", weeks=5);
2023-03-07T14:25:54.608530 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 90.0 SK Herne, 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 40 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 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 Herne cases daily growth factor Germany-SK Herne cases daily growth factor (rolling mean) Germany-SK Herne 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 Herne deaths daily growth factor Germany-SK Herne deaths daily growth factor (rolling mean) Germany-SK Herne 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-SK Herne doubling time cases (rolling mean) 0.00 15.66 31.32 46.99 62.65 daily change Germany-SK Herne new cases (rolling 7d mean) Germany-SK Herne new cases 0.000 0.313 0.626 0.940 daily change Germany-SK Herne new deaths (rolling 7d mean) Germany-SK Herne new deaths 0.000 0.293 0.587 0.880
In [4]:
overview(country="Germany", subregion="SK Herne");
2023-03-07T14:26:13.988708 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) 90.0 SK Herne, 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 0.0 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-SK Herne cases daily growth factor Germany-SK Herne cases daily growth factor (rolling mean) Germany-SK Herne 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 Herne deaths daily growth factor Germany-SK Herne deaths daily growth factor (rolling mean) Germany-SK Herne estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 cases doubling time [days] Germany-SK Herne doubling time cases (rolling mean) Germany-SK Herne doubling time deaths (rolling mean) 0.0 313.2 626.5 daily change Germany-SK Herne new cases (rolling 7d mean) Germany-SK Herne new cases 0.00 3.92 7.83 11.75 15.66 daily change Germany-SK Herne new deaths (rolling 7d mean) Germany-SK Herne new deaths 0.0 147.1 294.3 441.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Herne", dates="2020-03-15:");
2023-03-07T14:28:11.202299 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 Herne SK Herne 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 Herne Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Herne")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Herne")
print(f'Population of country="Germany", subregion="SK Herne": {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 Herne": 156621 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 70968 15 485 0
2023-03-03 70953 17 485 0
2023-03-02 70936 16 485 0
2023-03-01 70920 46 485 0
2023-02-28 70874 47 485 0
2023-02-27 70827 50 485 0
2023-02-24 70777 27 485 0
2023-02-23 70750 53 485 0
2023-02-22 70697 57 485 0
2023-02-21 70640 67 485 0
2023-02-20 70573 61 485 0
2023-02-17 70512 32 485 0
2023-02-16 70480 46 485 1
2023-02-15 70434 50 484 0
2023-02-14 70384 72 484 0
2023-02-13 70312 37 484 0
2023-02-10 70275 39 484 0
2023-02-09 70236 29 484 0
2023-02-08 70207 40 484 0
2023-02-07 70167 41 484 0
2023-02-06 70126 56 484 0
2023-02-03 70070 28 484 1
2023-02-02 70042 34 483 0
2023-02-01 70008 47 483 0
2023-01-31 69961 46 483 1
2023-01-30 69915 40 482 0
2023-01-27 69875 24 482 0
2023-01-26 69851 30 482 1
2023-01-25 69821 37 481 0
2023-01-24 69784 36 481 0
2023-01-23 69748 26 481 0
2023-01-20 69722 14 481 0
2023-01-19 69708 22 481 0
2023-01-18 69686 21 481 0
2023-01-17 69665 29 481 0
2023-01-16 69636 49 481 1
2023-01-13 69587 34 480 0
2023-01-12 69553 23 480 0
2023-01-11 69530 39 480 0
2023-01-10 69491 41 480 0
2023-01-09 69450 66 480 0
2023-01-06 69384 36 480 0
2023-01-05 69348 46 480 0
2023-01-04 69302 61 480 0
2023-01-03 69241 95 480 1
2023-01-02 69146 110 479 0
2022-12-30 69036 83 479 1
2022-12-29 68953 94 478 1
2022-12-28 68859 152 477 2
2022-12-27 68707 158 475 5
2022-12-25 68549 1 470 0
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2022-12-22 68464 111 470 2
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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: 0:02:46.557595