Germany: SK Lübeck (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 16:01:56 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Lübeck", weeks=5);
2023-03-07T16:02:13.879372 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 35 35 40 40 45 45 50 50 55 55 7-day incidence rate (per 100K people) 37.9 SK Lübeck, 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 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 Lübeck cases daily growth factor Germany-SK Lübeck cases daily growth factor (rolling mean) Germany-SK Lübeck 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 Lübeck deaths daily growth factor Germany-SK Lübeck deaths daily growth factor (rolling mean) Germany-SK Lübeck estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-SK Lübeck doubling time cases (rolling mean) 0.00 10.81 21.63 32.44 daily change Germany-SK Lübeck new cases (rolling 7d mean) Germany-SK Lübeck new cases 0.000 0.216 0.433 0.649 0.865 daily change Germany-SK Lübeck new deaths (rolling 7d mean) Germany-SK Lübeck new deaths 0.000 0.223 0.445 0.668 0.891
In [4]:
overview(country="Germany", subregion="SK Lübeck");
2023-03-07T16:02:28.158644 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) 37.9 SK Lübeck, 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 0.0 0.5 1.0 1.5 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 Lübeck cases daily growth factor Germany-SK Lübeck cases daily growth factor (rolling mean) Germany-SK Lübeck 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 Lübeck deaths daily growth factor Germany-SK Lübeck deaths daily growth factor (rolling mean) Germany-SK Lübeck 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-SK Lübeck doubling time cases (rolling mean) Germany-SK Lübeck doubling time deaths (rolling mean) 0 433 865 1298 daily change Germany-SK Lübeck new cases (rolling 7d mean) Germany-SK Lübeck new cases 0.000 1.081 2.163 3.244 daily change Germany-SK Lübeck new deaths (rolling 7d mean) Germany-SK Lübeck new deaths 0.0 27.2 54.3 81.5 108.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Lübeck", dates="2020-03-15:");
2023-03-07T16:04:43.657570 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 Lübeck SK Lübeck 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 Lübeck Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Lübeck")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Lübeck")
print(f'Population of country="Germany", subregion="SK Lübeck": {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 Lübeck": 216277 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 91762 19 167 0
2023-03-03 91743 17 167 0
2023-03-02 91726 2 167 0
2023-03-01 91724 18 167 0
2023-02-28 91706 26 167 0
2023-02-27 91680 20 167 0
2023-02-26 91660 5 167 0
2023-02-25 91655 1 167 0
2023-02-24 91654 23 167 0
2023-02-23 91631 12 167 0
2023-02-22 91619 18 167 0
2023-02-21 91601 23 167 0
2023-02-20 91578 40 167 0
2023-02-19 91538 4 167 0
2023-02-18 91534 1 167 0
2023-02-17 91533 11 167 0
2023-02-16 91522 12 167 0
2023-02-15 91510 9 167 0
2023-02-14 91501 28 167 1
2023-02-13 91473 19 166 0
2023-02-12 91454 10 166 0
2023-02-11 91444 2 166 0
2023-02-10 91442 8 166 0
2023-02-09 91434 11 166 0
2023-02-08 91423 15 166 0
2023-02-07 91408 18 166 0
2023-02-06 91390 27 166 0
2023-02-05 91363 4 166 0
2023-02-04 91359 4 166 0
2023-02-03 91355 16 166 0
2023-02-02 91339 14 166 0
2023-02-01 91325 18 166 0
2023-01-31 91307 16 166 0
2023-01-30 91291 17 166 0
2023-01-29 91274 9 166 0
2023-01-28 91265 2 166 0
2023-01-27 91263 15 166 0
2023-01-26 91248 7 166 0
2023-01-25 91241 11 166 0
2023-01-24 91230 22 166 1
2023-01-23 91208 19 165 0
2023-01-22 91189 2 165 0
2023-01-21 91187 2 165 0
2023-01-20 91185 12 165 0
2023-01-19 91173 35 165 0
2023-01-18 91138 16 165 0
2023-01-17 91122 8 165 0
2023-01-16 91114 10 165 0
2023-01-15 91104 2 165 0
2023-01-14 91102 4 165 0
2023-01-13 91098 4 165 0
2023-01-12 91094 11 165 0
2023-01-11 91083 21 165 0
2023-01-10 91062 13 165 0
2023-01-09 91049 43 165 0
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2023-01-07 90980 5 165 0
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2023-01-05 90943 24 165 0
2023-01-04 90919 43 165 0
2023-01-03 90876 40 165 0
2023-01-02 90836 72 165 0
2023-01-01 90764 26 165 0
2022-12-31 90738 6 165 0
2022-12-30 90732 88 165 0
2022-12-29 90644 40 165 0
2022-12-28 90604 124 165 0
2022-12-27 90480 114 165 0
2022-12-26 90366 8 165 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: 0:03:17.588917