Germany: LK Segeberg (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 15:53:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Segeberg", weeks=5);
2023-03-07T15:53:51.459087 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 90 90 7-day incidence rate (per 100K people) 46.4 LK Segeberg, 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 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Segeberg cases daily growth factor Germany-LK Segeberg cases daily growth factor (rolling mean) Germany-LK Segeberg 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Segeberg deaths daily growth factor Germany-LK Segeberg deaths daily growth factor (rolling mean) Germany-LK Segeberg 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-LK Segeberg doubling time cases (rolling mean) 0.00 28.04 56.08 daily change Germany-LK Segeberg new cases (rolling 7d mean) Germany-LK Segeberg new cases 0.000 0.280 0.561 0.841 daily change Germany-LK Segeberg new deaths (rolling 7d mean) Germany-LK Segeberg new deaths 0.000 0.256 0.512 0.768
In [4]:
overview(country="Germany", subregion="LK Segeberg");
2023-03-07T15:54:13.595705 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) 46.4 LK Segeberg, 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 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Segeberg cases daily growth factor Germany-LK Segeberg cases daily growth factor (rolling mean) Germany-LK Segeberg 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Segeberg deaths daily growth factor Germany-LK Segeberg deaths daily growth factor (rolling mean) Germany-LK Segeberg 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-LK Segeberg doubling time cases (rolling mean) Germany-LK Segeberg doubling time deaths (rolling mean) 0 280 561 841 1122 daily change Germany-LK Segeberg new cases (rolling 7d mean) Germany-LK Segeberg new cases 0.00 2.80 5.61 8.41 daily change Germany-LK Segeberg new deaths (rolling 7d mean) Germany-LK Segeberg new deaths 0.0 111.9 223.8 335.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Segeberg", dates="2020-03-15:");
2023-03-07T15:56:22.719149 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 Segeberg LK Segeberg 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 Segeberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Segeberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Segeberg")
print(f'Population of country="Germany", subregion="LK Segeberg": {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 Segeberg": 280400 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 124025 15 417 0
2023-03-03 124010 8 417 0
2023-03-02 124002 20 417 0
2023-03-01 123982 28 417 0
2023-02-28 123954 59 417 0
2023-02-27 123895 53 417 0
2023-02-24 123842 39 417 0
2023-02-23 123803 48 417 0
2023-02-22 123755 47 417 0
2023-02-21 123708 61 417 0
2023-02-20 123647 53 417 0
2023-02-17 123594 37 417 0
2023-02-16 123557 33 417 0
2023-02-15 123524 43 417 1
2023-02-14 123481 21 416 0
2023-02-13 123460 82 416 0
2023-02-10 123378 30 416 0
2023-02-09 123348 13 416 0
2023-02-08 123335 44 416 0
2023-02-07 123291 56 416 0
2023-02-06 123235 40 416 1
2023-02-03 123195 28 415 0
2023-02-02 123167 44 415 0
2023-02-01 123123 36 415 0
2023-01-31 123087 45 415 0
2023-01-30 123042 40 415 0
2023-01-27 123002 19 415 0
2023-01-26 122983 38 415 0
2023-01-25 122945 36 415 0
2023-01-24 122909 70 415 0
2023-01-23 122839 50 415 0
2023-01-22 122789 1 415 0
2023-01-20 122788 35 415 0
2023-01-19 122753 52 415 0
2023-01-18 122701 30 415 0
2023-01-17 122671 5 415 0
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2023-01-14 122631 2 415 0
2023-01-13 122629 25 415 1
2023-01-12 122604 38 414 0
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2023-01-09 122418 89 414 1
2023-01-08 122329 1 413 0
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2023-01-05 122269 85 413 0
2023-01-04 122184 105 413 0
2023-01-03 122079 110 413 1
2023-01-02 121969 165 412 2
2023-01-01 121804 1 410 0
2022-12-31 121803 1 410 0
2022-12-30 121802 119 410 0
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2022-12-28 121543 534 409 2
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2022-12-22 120845 192 405 1
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2022-12-20 120390 249 403 2
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2022-12-18 119847 1 399 0
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2022-12-16 119845 162 399 3
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2022-12-12 118981 333 395 4
2022-12-11 118648 1 391 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: 0:03:42.007638