Germany: LK Schleswig-Flensburg (Schleswig-Holstein)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 Schleswig-Flensburg", weeks=5);
2023-03-07T15:53:47.389245 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 7-day incidence rate (per 100K people) 46.1 LK Schleswig-Flensburg, 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 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 Schleswig-Flensburg cases daily growth factor Germany-LK Schleswig-Flensburg cases daily growth factor (rolling mean) Germany-LK Schleswig-Flensburg 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-LK Schleswig-Flensburg deaths daily growth factor Germany-LK Schleswig-Flensburg deaths daily growth factor (rolling mean) Germany-LK Schleswig-Flensburg 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 Schleswig-Flensburg doubling time cases (rolling mean) 0.00 20.38 40.76 daily change Germany-LK Schleswig-Flensburg new cases (rolling 7d mean) Germany-LK Schleswig-Flensburg new cases 0.000 0.204 0.408 0.611 0.815 daily change Germany-LK Schleswig-Flensburg new deaths (rolling 7d mean) Germany-LK Schleswig-Flensburg new deaths 0.00 0.29 0.58 0.87
In [4]:
overview(country="Germany", subregion="LK Schleswig-Flensburg");
2023-03-07T15:54:06.926291 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) 46.1 LK Schleswig-Flensburg, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Schleswig-Flensburg cases daily growth factor Germany-LK Schleswig-Flensburg cases daily growth factor (rolling mean) Germany-LK Schleswig-Flensburg 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-LK Schleswig-Flensburg deaths daily growth factor Germany-LK Schleswig-Flensburg deaths daily growth factor (rolling mean) Germany-LK Schleswig-Flensburg 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 Schleswig-Flensburg doubling time cases (rolling mean) Germany-LK Schleswig-Flensburg doubling time deaths (rolling mean) 0.0 203.8 407.6 611.4 daily change Germany-LK Schleswig-Flensburg new cases (rolling 7d mean) Germany-LK Schleswig-Flensburg new cases 0.000 2.038 4.076 6.114 daily change Germany-LK Schleswig-Flensburg new deaths (rolling 7d mean) Germany-LK Schleswig-Flensburg new deaths 0.0 61.5 123.0 184.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schleswig-Flensburg", dates="2020-03-15:");
2023-03-07T15:56:17.282296 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 Schleswig-Flensburg LK Schleswig-Flensburg 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 Schleswig-Flensburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schleswig-Flensburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schleswig-Flensburg")
print(f'Population of country="Germany", subregion="LK Schleswig-Flensburg": {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 Schleswig-Flensburg": 203799 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 79729 12 187 0
2023-03-03 79717 8 187 0
2023-03-02 79709 10 187 0
2023-03-01 79699 26 187 0
2023-02-28 79673 38 187 0
2023-02-27 79635 29 187 0
2023-02-24 79606 16 187 0
2023-02-23 79590 22 187 0
2023-02-22 79568 30 187 0
2023-02-21 79538 24 187 0
2023-02-20 79514 29 187 0
2023-02-17 79485 21 187 0
2023-02-16 79464 21 187 0
2023-02-15 79443 18 187 0
2023-02-14 79425 55 187 1
2023-02-13 79370 43 186 0
2023-02-10 79327 18 186 0
2023-02-09 79309 14 186 0
2023-02-08 79295 26 186 0
2023-02-07 79269 28 186 0
2023-02-06 79241 35 186 0
2023-02-03 79206 23 186 0
2023-02-02 79183 14 186 0
2023-02-01 79169 21 186 0
2023-01-31 79148 19 186 0
2023-01-30 79129 40 186 2
2023-01-27 79089 23 184 0
2023-01-26 79066 31 184 0
2023-01-25 79035 14 184 0
2023-01-24 79021 37 184 0
2023-01-23 78984 6 184 0
2023-01-20 78978 14 184 0
2023-01-19 78964 15 184 0
2023-01-18 78949 20 184 0
2023-01-17 78929 35 184 0
2023-01-16 78894 43 184 1
2023-01-13 78851 11 183 0
2023-01-12 78840 19 183 0
2023-01-11 78821 44 183 0
2023-01-10 78777 32 183 0
2023-01-09 78745 53 183 1
2023-01-06 78692 44 182 0
2023-01-05 78648 34 182 0
2023-01-04 78614 39 182 1
2023-01-03 78575 73 181 0
2023-01-02 78502 92 181 4
2022-12-30 78410 60 177 2
2022-12-29 78350 81 175 1
2022-12-28 78269 103 174 2
2022-12-27 78166 180 172 1
2022-12-24 77986 5 171 0
2022-12-23 77981 124 171 1
2022-12-22 77857 172 170 0
2022-12-21 77685 142 170 0
2022-12-20 77543 127 170 0
2022-12-19 77416 211 170 3
2022-12-16 77205 97 167 2
2022-12-15 77108 141 165 0
2022-12-14 76967 140 165 2
2022-12-13 76827 176 163 1
2022-12-12 76651 188 162 1
2022-12-09 76463 87 161 0
2022-12-08 76376 165 161 0
2022-12-07 76211 136 161 0
2022-12-06 76075 107 161 1
2022-12-05 75968 169 160 0
2022-12-02 75799 76 160 0
2022-12-01 75723 98 160 2
2022-11-30 75625 114 158 0
2022-11-29 75511 95 158 1
2022-11-28 75416 138 157 1
2022-11-25 75278 71 156 0
2022-11-24 75207 68 156 0
2022-11-23 75139 67 156 0
2022-11-22 75072 55 156 0
2022-11-21 75017 106 156 0
2022-11-18 74911 54 156 1
2022-11-17 74857 53 155 0
2022-11-16 74804 125 155 0
2022-11-15 74679 106 155 0
2022-11-14 74573 142 155 0
2022-11-11 74431 47 155 0
2022-11-10 74384 97 155 0
2022-11-09 74287 150 155 0
2022-11-08 74137 96 155 0
2022-11-07 74041 212 155 0
2022-11-04 73829 86 155 0
2022-11-03 73743 160 155 0
2022-11-02 73583 158 155 0
2022-11-01 73425 248 155 0
2022-10-28 73177 102 155 0
2022-10-27 73075 121 155 0
2022-10-26 72954 149 155 0
2022-10-25 72805 212 155 0
2022-10-24 72593 295 155 0
2022-10-21 72298 142 155 0
2022-10-20 72156 162 155 1
2022-10-19 71994 185 154 0
2022-10-18 71809 301 154 0
2022-10-17 71508 299 154 1
2022-10-14 71209 159 153 0
2022-10-13 71050 226 153 1
2022-10-12 70824 200 152 2
2022-10-11 70624 289 150 0
2022-10-10 70335 382 150 0
2022-10-07 69953 197 150 0
2022-10-06 69756 302 150 2
2022-10-05 69454 247 148 0
2022-10-04 69207 343 148 2
2022-09-30 68864 154 146 0
2022-09-29 68710 148 146 0
2022-09-28 68562 148 146 0
2022-09-27 68414 171 146 0
2022-09-26 68243 156 146 0
2022-09-23 68087 98 146 0
2022-09-22 67989 103 146 0
2022-09-21 67886 99 146 0
2022-09-20 67787 114 146 0
2022-09-19 67673 163 146 0
2022-09-16 67510 59 146 0
2022-09-15 67451 108 146 0
2022-09-14 67343 84 146 0
2022-09-13 67259 82 146 0
2022-09-12 67177 110 146 0
2022-09-11 67067 1 146 0
2022-09-09 67066 61 146 0
2022-09-08 67005 78 146 0
2022-09-07 66927 93 146 0
2022-09-06 66834 113 146 0
2022-09-05 66721 119 146 0
2022-09-02 66602 57 146 0
2022-09-01 66545 79 146 0
2022-08-31 66466 96 146 0
2022-08-30 66370 95 146 0
2022-08-29 66275 152 146 0
2022-08-26 66123 61 146 1
2022-08-25 66062 97 145 0
2022-08-24 65965 108 145 0
2022-08-23 65857 145 145 0
2022-08-22 65712 189 145 0
2022-08-19 65523 66 145 0
2022-08-18 65457 107 145 0
2022-08-17 65350 145 145 0
2022-08-16 65205 174 145 0
2022-08-15 65031 296 145 0
2022-08-12 64735 145 145 0
2022-08-11 64590 182 145 0
2022-08-10 64408 203 145 0
2022-08-09 64205 193 145 0
2022-08-08 64012 236 145 0
2022-08-05 63776 157 145 0
2022-08-04 63619 119 145 0
2022-08-03 63500 173 145 0
2022-08-02 63327 161 145 0
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2022-07-06 58689 499 138 2
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2021-01-02 931 14 31 2
2021-01-01 917 9 29 0
2020-12-31 908 16 29 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: 0:02:52.940795