Germany: SK Kiel (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 15:53:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Kiel", weeks=5);
2023-03-07T15:54:09.153769 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 48.7 SK Kiel, 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 15 20 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 Kiel new deaths (rolling 7d mean) Germany-SK Kiel 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 Kiel cases daily growth factor Germany-SK Kiel cases daily growth factor (rolling mean) Germany-SK Kiel 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 Kiel deaths daily growth factor Germany-SK Kiel deaths daily growth factor (rolling mean) Germany-SK Kiel estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-SK Kiel doubling time cases (rolling mean) 0.00 12.31 24.62 36.94 49.25 daily change Germany-SK Kiel new cases (rolling 7d mean) Germany-SK Kiel new cases 0.000 0.292 0.584 0.875
In [4]:
overview(country="Germany", subregion="SK Kiel");
2023-03-07T15:54:25.568778 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 7-day incidence rate (per 100K people) 48.7 SK Kiel, 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 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 Kiel cases daily growth factor Germany-SK Kiel cases daily growth factor (rolling mean) Germany-SK Kiel 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 Kiel deaths daily growth factor Germany-SK Kiel deaths daily growth factor (rolling mean) Germany-SK Kiel 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 Kiel doubling time cases (rolling mean) Germany-SK Kiel doubling time deaths (rolling mean) 0 492 985 1477 daily change Germany-SK Kiel new cases (rolling 7d mean) Germany-SK Kiel new cases 0.000 2.462 4.925 daily change Germany-SK Kiel new deaths (rolling 7d mean) Germany-SK Kiel new deaths 0.0 96.3 192.6 288.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Kiel", dates="2020-03-15:");
2023-03-07T15:56:21.795873 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 Kiel SK Kiel 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 Kiel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Kiel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Kiel")
print(f'Population of country="Germany", subregion="SK Kiel": {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 Kiel": 246243 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 102781 22 322 0
2023-03-03 102759 18 322 0
2023-03-02 102741 9 322 0
2023-03-01 102732 31 322 0
2023-02-28 102701 40 322 0
2023-02-27 102661 54 322 0
2023-02-24 102607 21 322 0
2023-02-23 102586 43 322 0
2023-02-22 102543 42 322 0
2023-02-21 102501 49 322 0
2023-02-20 102452 43 322 0
2023-02-17 102409 15 322 0
2023-02-16 102394 20 322 0
2023-02-15 102374 37 322 0
2023-02-14 102337 55 322 0
2023-02-13 102282 59 322 0
2023-02-10 102223 31 322 0
2023-02-09 102192 24 322 0
2023-02-08 102168 26 322 0
2023-02-07 102142 29 322 0
2023-02-06 102113 37 322 0
2023-02-03 102076 20 322 0
2023-02-02 102056 19 322 0
2023-02-01 102037 27 322 0
2023-01-31 102010 35 322 0
2023-01-30 101975 31 322 0
2023-01-27 101944 22 322 0
2023-01-26 101922 25 322 0
2023-01-25 101897 17 322 0
2023-01-24 101880 35 322 0
2023-01-23 101845 36 322 0
2023-01-20 101809 14 322 0
2023-01-19 101795 23 322 0
2023-01-18 101772 33 322 0
2023-01-17 101739 34 322 0
2023-01-16 101705 58 322 0
2023-01-13 101647 21 322 0
2023-01-12 101626 26 322 0
2023-01-11 101600 39 322 0
2023-01-10 101561 40 322 0
2023-01-09 101521 69 322 0
2023-01-06 101452 42 322 0
2023-01-05 101410 59 322 0
2023-01-04 101351 70 322 0
2023-01-03 101281 83 322 0
2023-01-02 101198 153 322 0
2022-12-30 101045 82 322 0
2022-12-29 100963 98 322 0
2022-12-28 100865 156 322 0
2022-12-27 100709 264 322 0
2022-12-23 100445 143 322 0
2022-12-22 100302 170 322 0
2022-12-21 100132 131 322 0
2022-12-20 100001 141 322 0
2022-12-19 99860 192 322 0
2022-12-16 99668 186 322 0
2022-12-15 99482 70 322 0
2022-12-14 99412 112 322 0
2022-12-13 99300 152 322 0
2022-12-12 99148 189 322 0
2022-12-09 98959 107 322 0
2022-12-08 98852 103 322 0
2022-12-07 98749 117 322 2
2022-12-06 98632 142 320 0
2022-12-05 98490 158 320 4
2022-12-02 98332 62 316 0
2022-12-01 98270 118 316 0
2022-11-30 98152 94 316 0
2022-11-29 98058 149 316 0
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2022-11-23 97609 69 316 1
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2022-11-01 96071 141 308 1
2022-10-31 95930 19 307 0
2022-10-30 95911 37 307 1
2022-10-29 95874 62 306 0
2022-10-28 95812 131 306 1
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2022-10-24 95203 154 303 0
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2022-10-20 94691 179 303 2
2022-10-19 94512 143 301 1
2022-10-18 94369 266 300 1
2022-10-17 94103 298 299 5
2022-10-16 93805 27 294 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: 0:02:41.983998