Germany: LK Nordfriesland (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 15:53:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Nordfriesland", weeks=5);
2023-03-07T15:54:14.691177 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) 48.3 LK Nordfriesland, 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 Nordfriesland new deaths (rolling 7d mean) Germany-LK Nordfriesland 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Nordfriesland cases daily growth factor Germany-LK Nordfriesland cases daily growth factor (rolling mean) Germany-LK Nordfriesland 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 Nordfriesland deaths daily growth factor Germany-LK Nordfriesland deaths daily growth factor (rolling mean) Germany-LK Nordfriesland 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 Nordfriesland doubling time cases (rolling mean) 0.00 16.76 33.51 daily change Germany-LK Nordfriesland new cases (rolling 7d mean) Germany-LK Nordfriesland new cases 0.000 0.305 0.611 0.916
In [4]:
overview(country="Germany", subregion="LK Nordfriesland");
2023-03-07T15:54:32.764012 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) 48.3 LK Nordfriesland, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Nordfriesland cases daily growth factor Germany-LK Nordfriesland cases daily growth factor (rolling mean) Germany-LK Nordfriesland 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 Nordfriesland deaths daily growth factor Germany-LK Nordfriesland deaths daily growth factor (rolling mean) Germany-LK Nordfriesland 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 Nordfriesland doubling time cases (rolling mean) Germany-LK Nordfriesland doubling time deaths (rolling mean) 0 335 670 daily change Germany-LK Nordfriesland new cases (rolling 7d mean) Germany-LK Nordfriesland new cases 0.00 3.35 6.70 10.05 daily change Germany-LK Nordfriesland new deaths (rolling 7d mean) Germany-LK Nordfriesland new deaths 0.0 46.6 93.2 139.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Nordfriesland", dates="2020-03-15:");
2023-03-07T15:56:28.875770 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 Nordfriesland LK Nordfriesland 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 Nordfriesland Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Nordfriesland")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Nordfriesland")
print(f'Population of country="Germany", subregion="LK Nordfriesland": {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 Nordfriesland": 167560 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 72636 25 213 0
2023-03-03 72611 8 213 0
2023-03-02 72603 9 213 0
2023-03-01 72594 16 213 0
2023-02-28 72578 23 213 0
2023-02-27 72555 36 213 0
2023-02-24 72519 15 213 0
2023-02-23 72504 8 213 0
2023-02-22 72496 18 213 0
2023-02-21 72478 33 213 0
2023-02-20 72445 44 213 0
2023-02-17 72401 12 213 0
2023-02-16 72389 23 213 0
2023-02-15 72366 26 213 0
2023-02-14 72340 37 213 0
2023-02-13 72303 44 213 0
2023-02-10 72259 19 213 0
2023-02-09 72240 12 213 0
2023-02-08 72228 28 213 0
2023-02-07 72200 23 213 0
2023-02-06 72177 16 213 0
2023-02-03 72161 10 213 0
2023-02-02 72151 11 213 0
2023-02-01 72140 28 213 0
2023-01-31 72112 26 213 0
2023-01-30 72086 30 213 0
2023-01-27 72056 14 213 0
2023-01-26 72042 17 213 0
2023-01-25 72025 49 213 0
2023-01-23 71976 34 213 0
2023-01-20 71942 10 213 0
2023-01-19 71932 15 213 0
2023-01-18 71917 27 213 0
2023-01-17 71890 35 213 0
2023-01-16 71855 38 213 0
2023-01-13 71817 18 213 0
2023-01-12 71799 13 213 0
2023-01-11 71786 30 213 0
2023-01-10 71756 40 213 0
2023-01-09 71716 49 213 1
2023-01-06 71667 20 212 0
2023-01-05 71647 35 212 1
2023-01-04 71612 41 211 0
2023-01-03 71571 62 211 1
2023-01-02 71509 85 210 0
2022-12-30 71424 62 210 0
2022-12-29 71362 88 210 0
2022-12-28 71274 120 210 0
2022-12-27 71154 143 210 2
2022-12-23 71011 47 208 0
2022-12-22 70964 69 208 0
2022-12-21 70895 112 208 0
2022-12-20 70783 149 208 0
2022-12-19 70634 125 208 0
2022-12-18 70509 25 208 0
2022-12-17 70484 54 208 0
2022-12-16 70430 122 208 0
2022-12-15 70308 91 208 0
2022-12-14 70217 89 208 0
2022-12-13 70128 198 208 0
2022-12-12 69930 80 208 1
2022-12-11 69850 39 207 1
2022-12-10 69811 61 206 1
2022-12-09 69750 97 205 0
2022-12-08 69653 75 205 0
2022-12-07 69578 148 205 0
2022-12-06 69430 105 205 1
2022-12-05 69325 113 204 0
2022-12-04 69212 25 204 0
2022-12-03 69187 52 204 1
2022-12-02 69135 124 203 1
2022-12-01 69011 49 202 1
2022-11-30 68962 93 201 0
2022-11-29 68869 84 201 1
2022-11-28 68785 112 200 0
2022-11-27 68673 24 200 0
2022-11-26 68649 42 200 0
2022-11-25 68607 71 200 0
2022-11-24 68536 76 200 2
2022-11-23 68460 61 198 1
2022-11-22 68399 61 197 0
2022-11-21 68338 109 197 2
2022-11-20 68229 5 195 0
2022-11-19 68224 18 195 0
2022-11-18 68206 47 195 0
2022-11-17 68159 85 195 0
2022-11-16 68074 67 195 0
2022-11-15 68007 86 195 0
2022-11-14 67921 113 195 0
2022-11-12 67808 6 195 0
2022-11-11 67802 29 195 1
2022-11-10 67773 60 194 0
2022-11-09 67713 84 194 1
2022-11-08 67629 62 193 0
2022-11-07 67567 113 193 0
2022-11-06 67454 32 193 0
2022-11-05 67422 45 193 0
2022-11-04 67377 84 193 0
2022-11-03 67293 57 193 0
2022-11-02 67236 116 193 0
2022-11-01 67120 165 193 1
2022-10-31 66955 24 192 0
2022-10-30 66931 32 192 0
2022-10-29 66899 23 192 0
2022-10-28 66876 115 192 0
2022-10-27 66761 100 192 0
2022-10-26 66661 144 192 1
2022-10-25 66517 184 191 0
2022-10-24 66333 173 191 0
2022-10-23 66160 37 191 0
2022-10-22 66123 84 191 0
2022-10-21 66039 152 191 0
2022-10-20 65887 150 191 0
2022-10-19 65737 169 191 0
2022-10-18 65568 230 191 0
2022-10-17 65338 199 191 1
2022-10-16 65139 81 190 0
2022-10-15 65058 78 190 0
2022-10-14 64980 125 190 0
2022-10-13 64855 204 190 0
2022-10-12 64651 194 190 0
2022-10-11 64457 250 190 2
2022-10-10 64207 229 188 1
2022-10-09 63978 77 187 0
2022-10-08 63901 131 187 0
2022-10-07 63770 253 187 0
2022-10-06 63517 212 187 0
2022-10-05 63305 284 187 0
2022-10-04 63021 281 187 0
2022-10-03 62740 113 187 1
2022-10-02 62627 75 186 0
2022-10-01 62552 88 186 0
2022-09-30 62464 166 186 0
2022-09-29 62298 147 186 1
2022-09-28 62151 138 185 0
2022-09-27 62013 184 185 1
2022-09-26 61829 162 184 1
2022-09-25 61667 30 183 0
2022-09-24 61637 59 183 0
2022-09-23 61578 122 183 1
2022-09-22 61456 94 182 0
2022-09-21 61362 94 182 2
2022-09-20 61268 112 180 1
2022-09-19 61156 89 179 0
2022-09-18 61067 18 179 0
2022-09-17 61049 29 179 0
2022-09-16 61020 92 179 0
2022-09-15 60928 47 179 0
2022-09-14 60881 71 179 0
2022-09-13 60810 87 179 1
2022-09-12 60723 109 178 0
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2022-09-10 60596 28 178 0
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2022-09-08 60506 57 178 0
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2021-01-01 960 5 28 0
2020-12-31 955 7 28 0
2020-12-30 948 16 28 3
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2020-09-26 122 1 2 0
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2020-09-17 112 1 2 0
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2020-03-22 18 1 1 0
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2020-03-17 9 8 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: 0:02:38.212446