Germany: LK Stormarn (Schleswig-Holstein)¶

  • 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 16:02:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Stormarn", weeks=5);
2023-03-07T16:02:19.526170 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 25 25 30 30 35 35 40 40 45 45 7-day incidence rate (per 100K people) 25.7 LK Stormarn, 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 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-LK Stormarn cases daily growth factor Germany-LK Stormarn cases daily growth factor (rolling mean) Germany-LK Stormarn 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-LK Stormarn deaths daily growth factor Germany-LK Stormarn deaths daily growth factor (rolling mean) Germany-LK Stormarn estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Stormarn doubling time cases (rolling mean) 0.00 12.27 24.54 daily change Germany-LK Stormarn new cases (rolling 7d mean) Germany-LK Stormarn new cases 0.000 0.245 0.491 0.736 0.982 daily change Germany-LK Stormarn new deaths (rolling 7d mean) Germany-LK Stormarn new deaths 0.000 0.225 0.449 0.674 0.898
In [4]:
overview(country="Germany", subregion="LK Stormarn");
2023-03-07T16:02:34.243403 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 25.7 LK Stormarn, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 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-LK Stormarn cases daily growth factor Germany-LK Stormarn cases daily growth factor (rolling mean) Germany-LK Stormarn 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-LK Stormarn deaths daily growth factor Germany-LK Stormarn deaths daily growth factor (rolling mean) Germany-LK Stormarn estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Stormarn doubling time cases (rolling mean) Germany-LK Stormarn doubling time deaths (rolling mean) 0 245 491 736 982 daily change Germany-LK Stormarn new cases (rolling 7d mean) Germany-LK Stormarn new cases 0.00 4.91 9.82 daily change Germany-LK Stormarn new deaths (rolling 7d mean) Germany-LK Stormarn new deaths 0.0 123.0 245.9 368.9 491.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Stormarn", dates="2020-03-15:");
2023-03-07T16:04:40.204974 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: LK Stormarn LK Stormarn 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 LK Stormarn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Stormarn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Stormarn")
print(f'Population of country="Germany", subregion="LK Stormarn": {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="LK Stormarn": 245406 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 89373 1 469 0
2023-03-05 89372 3 469 0
2023-03-04 89369 3 469 0
2023-03-03 89366 11 469 0
2023-03-02 89355 8 469 0
2023-03-01 89347 4 469 0
2023-02-28 89343 33 469 0
2023-02-27 89310 20 469 0
2023-02-26 89290 5 469 0
2023-02-25 89285 10 469 0
2023-02-24 89275 18 469 0
2023-02-23 89257 16 469 0
2023-02-22 89241 14 469 0
2023-02-21 89227 16 469 0
2023-02-20 89211 15 469 0
2023-02-19 89196 5 469 0
2023-02-18 89191 7 469 0
2023-02-17 89184 12 469 0
2023-02-16 89172 13 469 0
2023-02-15 89159 11 469 0
2023-02-14 89148 19 469 0
2023-02-13 89129 27 469 0
2023-02-12 89102 1 469 0
2023-02-11 89101 5 469 0
2023-02-10 89096 12 469 0
2023-02-09 89084 14 469 0
2023-02-08 89070 9 469 0
2023-02-07 89061 14 469 1
2023-02-06 89047 17 468 0
2023-02-05 89030 2 468 0
2023-02-04 89028 3 468 0
2023-02-03 89025 10 468 0
2023-02-02 89015 14 468 0
2023-02-01 89001 19 468 0
2023-01-31 88982 20 468 0
2023-01-30 88962 18 468 0
2023-01-29 88944 5 468 0
2023-01-28 88939 8 468 0
2023-01-27 88931 9 468 0
2023-01-26 88922 12 468 0
2023-01-25 88910 21 468 0
2023-01-24 88889 11 468 0
2023-01-23 88878 15 468 0
2023-01-22 88863 5 468 0
2023-01-21 88858 6 468 0
2023-01-20 88852 5 468 0
2023-01-19 88847 13 468 0
2023-01-18 88834 10 468 0
2023-01-17 88824 14 468 0
2023-01-16 88810 22 468 0
2023-01-15 88788 6 468 0
2023-01-14 88782 7 468 0
2023-01-13 88775 15 468 0
2023-01-12 88760 20 468 0
2023-01-11 88740 18 468 0
2023-01-10 88722 44 468 0
2023-01-09 88678 47 468 0
2023-01-08 88631 10 468 0
2023-01-07 88621 17 468 0
2023-01-06 88604 27 468 0
2023-01-05 88577 45 468 0
2023-01-04 88532 63 468 0
2023-01-03 88469 39 468 0
2023-01-02 88430 47 468 0
2023-01-01 88383 7 468 0
2022-12-31 88376 19 468 0
2022-12-30 88357 44 468 0
2022-12-29 88313 79 468 0
2022-12-28 88234 83 468 0
2022-12-27 88151 65 468 0
2022-12-26 88086 20 468 1
2022-12-25 88066 17 467 0
2022-12-24 88049 55 467 0
2022-12-23 87994 102 467 0
2022-12-22 87892 93 467 0
2022-12-21 87799 109 467 0
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2022-12-19 87560 102 467 1
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2022-12-09 86828 73 465 0
2022-12-08 86755 102 465 0
2022-12-07 86653 91 465 1
2022-12-06 86562 68 464 0
2022-12-05 86494 112 464 1
2022-12-04 86382 31 463 0
2022-12-03 86351 28 463 0
2022-12-02 86323 78 463 1
2022-12-01 86245 86 462 0
2022-11-30 86159 113 462 1
2022-11-29 86046 76 461 0
2022-11-28 85970 88 461 0
2022-11-27 85882 29 461 1
2022-11-26 85853 20 460 0
2022-11-25 85833 49 460 0
2022-11-24 85784 74 460 0
2022-11-23 85710 72 460 0
2022-11-22 85638 64 460 1
2022-11-21 85574 77 459 0
2022-11-20 85497 22 459 0
2022-11-19 85475 33 459 1
2022-11-18 85442 48 458 0
2022-11-17 85394 62 458 1
2022-11-16 85332 62 457 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: 0:02:59.417403