Germany: LK Ostholstein (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 15:53:36 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ostholstein", weeks=5);
2023-03-07T15:53:50.658714 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 35 35 40 40 45 45 50 50 7-day incidence rate (per 100K people) 41.6 LK Ostholstein, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Ostholstein new deaths (rolling 7d mean) Germany-LK Ostholstein 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 R & growth factor (based on cases) Germany-LK Ostholstein cases daily growth factor Germany-LK Ostholstein cases daily growth factor (rolling mean) Germany-LK Ostholstein 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-LK Ostholstein deaths daily growth factor Germany-LK Ostholstein deaths daily growth factor (rolling mean) Germany-LK Ostholstein 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-LK Ostholstein doubling time cases (rolling mean) 0.0 10.1 20.2 daily change Germany-LK Ostholstein new cases (rolling 7d mean) Germany-LK Ostholstein new cases 0.000 0.217 0.433 0.650 0.866
In [4]:
overview(country="Germany", subregion="LK Ostholstein");
2023-03-07T15:54:13.352859 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 2000 2000 7-day incidence rate (per 100K people) 41.6 LK Ostholstein, 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 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 R & growth factor (based on cases) Germany-LK Ostholstein cases daily growth factor Germany-LK Ostholstein cases daily growth factor (rolling mean) Germany-LK Ostholstein 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-LK Ostholstein deaths daily growth factor Germany-LK Ostholstein deaths daily growth factor (rolling mean) Germany-LK Ostholstein 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 8000 cases doubling time [days] Germany-LK Ostholstein doubling time cases (rolling mean) Germany-LK Ostholstein doubling time deaths (rolling mean) 0 202 404 606 808 daily change Germany-LK Ostholstein new cases (rolling 7d mean) Germany-LK Ostholstein new cases 0.00 2.02 4.04 daily change Germany-LK Ostholstein new deaths (rolling 7d mean) Germany-LK Ostholstein new deaths 0.0 21.0 42.0 63.1 84.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ostholstein", dates="2020-03-15:");
2023-03-07T15:56:17.090644 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 Ostholstein LK Ostholstein 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 Ostholstein Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ostholstein")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ostholstein")
print(f'Population of country="Germany", subregion="LK Ostholstein": {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 Ostholstein": 202014 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 73488 3 138 0
2023-03-05 73485 4 138 0
2023-03-04 73481 5 138 0
2023-03-03 73476 7 138 0
2023-03-02 73469 11 138 0
2023-03-01 73458 24 138 0
2023-02-28 73434 30 138 0
2023-02-27 73404 17 138 0
2023-02-24 73387 15 138 0
2023-02-23 73372 14 138 0
2023-02-22 73358 13 138 0
2023-02-21 73345 18 138 0
2023-02-20 73327 6 138 0
2023-02-19 73321 16 138 0
2023-02-17 73305 13 138 0
2023-02-16 73292 8 138 0
2023-02-15 73284 15 138 0
2023-02-14 73269 21 138 0
2023-02-13 73248 4 138 0
2023-02-12 73244 1 138 0
2023-02-11 73243 7 138 0
2023-02-10 73236 17 138 0
2023-02-09 73219 10 138 0
2023-02-08 73209 10 138 0
2023-02-07 73199 15 138 0
2023-02-06 73184 6 138 0
2023-02-05 73178 5 138 0
2023-02-04 73173 5 138 0
2023-02-03 73168 6 138 0
2023-02-02 73162 14 138 0
2023-02-01 73148 12 138 0
2023-01-31 73136 22 138 0
2023-01-30 73114 4 138 0
2023-01-29 73110 7 138 0
2023-01-28 73103 10 138 0
2023-01-27 73093 6 138 0
2023-01-26 73087 5 138 0
2023-01-25 73082 7 138 0
2023-01-24 73075 19 138 0
2023-01-23 73056 27 138 0
2023-01-20 73029 9 138 0
2023-01-19 73020 23 138 0
2023-01-18 72997 18 138 0
2023-01-17 72979 19 138 0
2023-01-16 72960 5 138 0
2023-01-15 72955 3 138 0
2023-01-14 72952 10 138 0
2023-01-13 72942 15 138 0
2023-01-12 72927 18 138 0
2023-01-11 72909 27 138 0
2023-01-10 72882 41 138 0
2023-01-09 72841 50 138 1
2023-01-06 72791 39 137 0
2023-01-05 72752 54 137 0
2023-01-04 72698 60 137 0
2023-01-03 72638 69 137 0
2023-01-02 72569 78 137 0
2022-12-30 72491 52 137 0
2022-12-29 72439 102 137 0
2022-12-28 72337 153 137 0
2022-12-27 72184 73 137 0
2022-12-24 72111 95 137 0
2022-12-23 72016 80 137 0
2022-12-22 71936 94 137 0
2022-12-21 71842 106 137 0
2022-12-20 71736 112 137 0
2022-12-19 71624 83 137 0
2022-12-18 71541 79 137 0
2022-12-16 71462 99 137 0
2022-12-15 71363 78 137 0
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2022-12-13 71184 151 137 1
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2022-11-21 69665 103 135 0
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2022-11-11 69110 74 135 0
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2022-11-03 68332 179 134 0
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2022-10-31 67771 2 134 0
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2020-12-31 1110 20 19 2
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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: 0:02:49.430929