Germany: SK Münster (Nordrhein-Westfalen)¶

  • Homepage of project: https://oscovida.github.io
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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 14:32:24 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Münster", weeks=5);
2023-03-07T14:32:46.595066 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 119.9 SK Münster, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Münster new deaths (rolling 7d mean) Germany-SK Münster new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-SK Münster cases daily growth factor Germany-SK Münster cases daily growth factor (rolling mean) Germany-SK Münster estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Münster deaths daily growth factor Germany-SK Münster deaths daily growth factor (rolling mean) Germany-SK Münster 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 Münster doubling time cases (rolling mean) 0.0 63.5 127.1 190.6 254.2 daily change Germany-SK Münster new cases (rolling 7d mean) Germany-SK Münster new cases 0.000 0.323 0.647 0.970
In [4]:
overview(country="Germany", subregion="SK Münster");
2023-03-07T14:33:06.738539 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) 119.9 SK Münster, 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Münster cases daily growth factor Germany-SK Münster cases daily growth factor (rolling mean) Germany-SK Münster estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Münster deaths daily growth factor Germany-SK Münster deaths daily growth factor (rolling mean) Germany-SK Münster 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 cases doubling time [days] Germany-SK Münster doubling time cases (rolling mean) Germany-SK Münster doubling time deaths (rolling mean) 0 635 1271 daily change Germany-SK Münster new cases (rolling 7d mean) Germany-SK Münster new cases 0.000 3.177 6.354 daily change Germany-SK Münster new deaths (rolling 7d mean) Germany-SK Münster new deaths 0.0 50.4 100.9 151.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Münster", dates="2020-03-15:");
2023-03-07T14:35:18.496674 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 Münster SK Münster 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 Münster Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Münster")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Münster")
print(f'Population of country="Germany", subregion="SK Münster": {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 Münster": 317713 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 147096 38 261 0
2023-03-03 147058 36 261 0
2023-03-02 147022 50 261 0
2023-03-01 146972 178 261 0
2023-02-28 146794 79 261 0
2023-02-27 146715 145 261 0
2023-02-24 146570 122 261 0
2023-02-23 146448 90 261 0
2023-02-22 146358 96 261 0
2023-02-21 146262 276 261 0
2023-02-17 145986 47 261 0
2023-02-16 145939 81 261 0
2023-02-15 145858 77 261 0
2023-02-14 145781 94 261 0
2023-02-13 145687 96 261 0
2023-02-10 145591 55 261 0
2023-02-09 145536 62 261 0
2023-02-08 145474 51 261 0
2023-02-07 145423 71 261 0
2023-02-06 145352 66 261 0
2023-02-03 145286 34 261 0
2023-02-02 145252 57 261 0
2023-02-01 145195 58 261 0
2023-01-31 145137 98 261 0
2023-01-30 145039 104 261 0
2023-01-27 144935 35 261 0
2023-01-26 144900 59 261 0
2023-01-25 144841 77 261 0
2023-01-24 144764 68 261 0
2023-01-23 144696 68 261 0
2023-01-20 144628 39 261 0
2023-01-19 144589 48 261 0
2023-01-18 144541 55 261 1
2023-01-17 144486 63 260 0
2023-01-16 144423 72 260 0
2023-01-13 144351 28 260 0
2023-01-12 144323 54 260 0
2023-01-11 144269 96 260 0
2023-01-10 144173 69 260 1
2023-01-09 144104 166 259 1
2023-01-06 143938 85 258 0
2023-01-05 143853 103 258 0
2023-01-04 143750 149 258 0
2023-01-03 143601 143 258 1
2023-01-02 143458 260 257 0
2022-12-30 143198 88 257 0
2022-12-29 143110 229 257 0
2022-12-28 142881 187 257 0
2022-12-27 142694 462 257 1
2022-12-23 142232 226 256 3
2022-12-22 142006 204 253 0
2022-12-21 141802 225 253 0
2022-12-20 141577 229 253 0
2022-12-19 141348 338 253 0
2022-12-16 141010 160 253 0
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2022-12-08 139820 184 251 1
2022-12-07 139636 136 250 0
2022-12-06 139500 147 250 1
2022-12-05 139353 243 249 0
2022-12-02 139110 108 249 2
2022-12-01 139002 145 247 0
2022-11-30 138857 178 247 1
2022-11-29 138679 122 246 0
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2022-10-19 131840 476 242 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: 0:03:07.442919