Germany: SK Kassel (Hessen)¶

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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 13:07:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Kassel", weeks=5);
2023-03-07T13:07:34.126634 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 7-day incidence rate (per 100K people) 84.8 SK Kassel, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 0.4 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 Kassel cases daily growth factor Germany-SK Kassel cases daily growth factor (rolling mean) Germany-SK Kassel estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Kassel deaths daily growth factor Germany-SK Kassel deaths daily growth factor (rolling mean) Germany-SK Kassel 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 Kassel doubling time cases (rolling mean) 0.00 20.04 40.08 60.12 daily change Germany-SK Kassel new cases (rolling 7d mean) Germany-SK Kassel new cases 0.000 0.200 0.401 0.601 0.802 daily change Germany-SK Kassel new deaths (rolling 7d mean) Germany-SK Kassel new deaths 0.000 0.285 0.570 0.855
In [4]:
overview(country="Germany", subregion="SK Kassel");
2023-03-07T13:07:56.720527 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) 84.8 SK Kassel, 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 3 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 Kassel cases daily growth factor Germany-SK Kassel cases daily growth factor (rolling mean) Germany-SK Kassel estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Kassel deaths daily growth factor Germany-SK Kassel deaths daily growth factor (rolling mean) Germany-SK Kassel 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 Kassel doubling time cases (rolling mean) Germany-SK Kassel doubling time deaths (rolling mean) 0 401 802 1202 daily change Germany-SK Kassel new cases (rolling 7d mean) Germany-SK Kassel new cases 0.000 2.004 4.008 6.012 daily change Germany-SK Kassel new deaths (rolling 7d mean) Germany-SK Kassel new deaths 0.0 101.6 203.3 304.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Kassel", dates="2020-03-15:");
2023-03-07T13:10:15.438548 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 Kassel SK Kassel 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 Kassel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Kassel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Kassel")
print(f'Population of country="Germany", subregion="SK Kassel": {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 Kassel": 200406 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 79542 33 350 0
2023-03-03 79509 56 350 0
2023-03-02 79453 25 350 0
2023-03-01 79428 24 350 0
2023-02-28 79404 32 350 0
2023-02-27 79372 55 350 0
2023-02-24 79317 17 350 0
2023-02-23 79300 50 350 0
2023-02-22 79250 26 350 0
2023-02-21 79224 40 350 0
2023-02-20 79184 76 350 0
2023-02-17 79108 16 350 0
2023-02-16 79092 33 350 1
2023-02-15 79059 46 349 0
2023-02-14 79013 41 349 0
2023-02-13 78972 39 349 0
2023-02-10 78933 20 349 0
2023-02-09 78913 47 349 0
2023-02-08 78866 39 349 1
2023-02-07 78827 42 348 0
2023-02-06 78785 71 348 1
2023-02-03 78714 30 347 0
2023-02-02 78684 24 347 0
2023-02-01 78660 26 347 0
2023-01-31 78634 43 347 0
2023-01-30 78591 37 347 0
2023-01-27 78554 13 347 0
2023-01-26 78541 16 347 0
2023-01-25 78525 19 347 1
2023-01-24 78506 28 346 0
2023-01-23 78478 37 346 1
2023-01-20 78441 11 345 0
2023-01-19 78430 15 345 1
2023-01-18 78415 26 344 0
2023-01-17 78389 21 344 0
2023-01-16 78368 43 344 0
2023-01-13 78325 26 344 0
2023-01-12 78299 24 344 0
2023-01-11 78275 28 344 0
2023-01-10 78247 49 344 0
2023-01-09 78198 73 344 1
2023-01-08 78125 1 343 0
2023-01-06 78124 34 343 1
2023-01-05 78090 53 342 0
2023-01-04 78037 34 342 0
2023-01-03 78003 63 342 0
2023-01-02 77940 75 342 1
2023-01-01 77865 1 341 1
2022-12-30 77864 36 340 0
2022-12-29 77828 69 340 0
2022-12-28 77759 61 340 0
2022-12-27 77698 159 340 0
2022-12-23 77539 53 340 1
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2022-12-12 76883 123 337 2
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2022-12-08 76719 40 335 1
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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: 0:03:06.462393