Germany: LK Steinburg (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 15:53:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Steinburg", weeks=5);
2023-03-07T15:53:47.887216 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 58.1 LK Steinburg, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Steinburg new deaths (rolling 7d mean) Germany-LK Steinburg 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 Steinburg cases daily growth factor Germany-LK Steinburg cases daily growth factor (rolling mean) Germany-LK Steinburg 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 Steinburg deaths daily growth factor Germany-LK Steinburg deaths daily growth factor (rolling mean) Germany-LK Steinburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Steinburg doubling time cases (rolling mean) 0.00 13.08 26.17 daily change Germany-LK Steinburg new cases (rolling 7d mean) Germany-LK Steinburg new cases 0.000 0.238 0.477 0.715 0.954
In [4]:
overview(country="Germany", subregion="LK Steinburg");
2023-03-07T15:54:06.875721 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) 58.1 LK Steinburg, 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 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 Steinburg cases daily growth factor Germany-LK Steinburg cases daily growth factor (rolling mean) Germany-LK Steinburg 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 Steinburg deaths daily growth factor Germany-LK Steinburg deaths daily growth factor (rolling mean) Germany-LK Steinburg 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 cases doubling time [days] Germany-LK Steinburg doubling time cases (rolling mean) Germany-LK Steinburg doubling time deaths (rolling mean) 0 262 523 785 daily change Germany-LK Steinburg new cases (rolling 7d mean) Germany-LK Steinburg new cases 0.000 1.308 2.617 3.925 5.234 daily change Germany-LK Steinburg new deaths (rolling 7d mean) Germany-LK Steinburg new deaths 0.00 15.66 31.33 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Steinburg", dates="2020-03-15:");
2023-03-07T15:56:19.548274 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 Steinburg LK Steinburg 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 Steinburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Steinburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Steinburg")
print(f'Population of country="Germany", subregion="LK Steinburg": {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 Steinburg": 130843 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 52623 11 77 0
2023-03-03 52612 10 77 0
2023-03-02 52602 16 77 0
2023-03-01 52586 17 77 0
2023-02-28 52569 22 77 0
2023-02-27 52547 30 77 0
2023-02-24 52517 9 77 0
2023-02-23 52508 10 77 0
2023-02-22 52498 18 77 0
2023-02-21 52480 20 77 0
2023-02-20 52460 34 77 0
2023-02-17 52426 19 77 0
2023-02-16 52407 22 77 0
2023-02-15 52385 23 77 0
2023-02-14 52362 27 77 0
2023-02-13 52335 30 77 0
2023-02-10 52305 16 77 0
2023-02-09 52289 22 77 0
2023-02-08 52267 19 77 0
2023-02-07 52248 16 77 0
2023-02-06 52232 33 77 0
2023-02-03 52199 11 77 0
2023-02-02 52188 11 77 0
2023-02-01 52177 14 77 0
2023-01-31 52163 19 77 0
2023-01-30 52144 37 77 0
2023-01-27 52107 5 77 0
2023-01-26 52102 10 77 0
2023-01-25 52092 12 77 0
2023-01-24 52080 15 77 0
2023-01-23 52065 24 77 0
2023-01-20 52041 11 77 0
2023-01-19 52030 16 77 0
2023-01-18 52014 23 77 0
2023-01-17 51991 21 77 0
2023-01-16 51970 36 77 0
2023-01-13 51934 12 77 0
2023-01-12 51922 16 77 0
2023-01-11 51906 26 77 0
2023-01-10 51880 20 77 0
2023-01-09 51860 56 77 0
2023-01-06 51804 16 77 0
2023-01-05 51788 20 77 0
2023-01-04 51768 31 77 0
2023-01-03 51737 54 77 0
2023-01-02 51683 81 77 0
2022-12-30 51602 38 77 1
2022-12-29 51564 71 76 0
2022-12-28 51493 97 76 0
2022-12-27 51396 174 76 1
2022-12-23 51222 111 75 0
2022-12-22 51111 81 75 0
2022-12-21 51030 86 75 0
2022-12-20 50944 122 75 0
2022-12-19 50822 184 75 0
2022-12-16 50638 64 75 0
2022-12-15 50574 85 75 0
2022-12-14 50489 85 75 0
2022-12-13 50404 147 75 0
2022-12-12 50257 181 75 1
2022-12-09 50076 90 74 0
2022-12-08 49986 103 74 0
2022-12-07 49883 89 74 0
2022-12-06 49794 131 74 0
2022-12-05 49663 171 74 1
2022-12-02 49492 71 73 1
2022-12-01 49421 64 72 0
2022-11-30 49357 91 72 1
2022-11-29 49266 88 71 0
2022-11-28 49178 67 71 0
2022-11-27 49111 36 71 0
2022-11-26 49075 2 71 0
2022-11-25 49073 49 71 0
2022-11-24 49024 55 71 0
2022-11-23 48969 56 71 1
2022-11-22 48913 60 70 0
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2022-11-20 48775 14 70 0
2022-11-19 48761 2 70 0
2022-11-18 48759 60 70 0
2022-11-17 48699 49 70 0
2022-11-16 48650 43 70 0
2022-11-15 48607 82 70 0
2022-11-14 48525 99 70 0
2022-11-13 48426 22 70 0
2022-11-12 48404 1 70 0
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2022-11-10 48323 84 70 0
2022-11-09 48239 47 70 0
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2022-11-07 48084 46 70 0
2022-11-06 48038 34 70 0
2022-11-05 48004 36 70 0
2022-11-04 47968 37 70 0
2022-11-03 47931 64 70 1
2022-11-02 47867 112 69 0
2022-11-01 47755 75 69 0
2022-10-31 47680 55 69 0
2022-10-30 47625 40 69 0
2022-10-29 47585 47 69 0
2022-10-28 47538 61 69 0
2022-10-27 47477 92 69 0
2022-10-26 47385 135 69 0
2022-10-25 47250 108 69 0
2022-10-24 47142 103 69 0
2022-10-23 47039 11 69 0
2022-10-22 47028 111 69 0
2022-10-21 46917 66 69 0
2022-10-20 46851 228 69 0
2022-10-19 46623 90 69 0
2022-10-18 46533 199 69 0
2022-10-17 46334 272 69 0
2022-10-16 46062 3 69 0
2022-10-14 46059 149 69 0
2022-10-13 45910 178 69 0
2022-10-12 45732 321 69 0
2022-10-11 45411 252 69 0
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2022-10-08 45036 1 69 0
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2022-10-06 44875 206 69 0
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2022-10-04 44539 179 68 0
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2021-01-03 881 4 21 0
2021-01-02 877 10 21 1
2021-01-01 867 9 20 0
2020-12-31 858 2 20 0
2020-12-30 856 27 20 0
2020-12-29 829 10 20 0
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2020-12-27 812 11 19 0
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2020-12-15 622 61 14 6
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2020-12-13 551 6 8 2
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2020-05-06 164 2 3 0
2020-05-05 162 30 3 0
2020-05-03 132 48 3 0
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2020-04-04 54 1 2 0
2020-04-03 53 1 2 0
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2020-04-01 50 7 2 1
2020-03-31 43 3 1 0
2020-03-30 40 4 1 1
2020-03-27 36 8 0 0
2020-03-26 28 5 0 0
2020-03-25 23 3 0 0
2020-03-24 20 1 0 0
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2020-03-20 12 5 0 0
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2020-03-14 3 1 0 0

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: 0:03:02.832879