Germany: LK Friesland (Niedersachsen)¶

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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 13:07:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Friesland", weeks=5);
2023-03-07T13:07:51.235230 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 37.4 LK Friesland, 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 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 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 Friesland cases daily growth factor Germany-LK Friesland cases daily growth factor (rolling mean) Germany-LK Friesland 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 R & growth factor (based on deaths) Germany-LK Friesland deaths daily growth factor Germany-LK Friesland deaths daily growth factor (rolling mean) Germany-LK Friesland 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 Friesland doubling time cases (rolling mean) 0.00 9.90 19.79 29.69 39.59 daily change Germany-LK Friesland new cases (rolling 7d mean) Germany-LK Friesland new cases 0.000 0.247 0.495 0.742 0.990 daily change Germany-LK Friesland new deaths (rolling 7d mean) Germany-LK Friesland new deaths 0.000 0.225 0.450 0.675 0.899
In [4]:
overview(country="Germany", subregion="LK Friesland");
2023-03-07T13:08:05.088680 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) 37.4 LK Friesland, 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 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 Friesland cases daily growth factor Germany-LK Friesland cases daily growth factor (rolling mean) Germany-LK Friesland 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 R & growth factor (based on deaths) Germany-LK Friesland deaths daily growth factor Germany-LK Friesland deaths daily growth factor (rolling mean) Germany-LK Friesland 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 Friesland doubling time cases (rolling mean) Germany-LK Friesland doubling time deaths (rolling mean) 0.0 197.9 395.9 daily change Germany-LK Friesland new cases (rolling 7d mean) Germany-LK Friesland new cases 0.000 0.990 1.979 2.969 3.959 daily change Germany-LK Friesland new deaths (rolling 7d mean) Germany-LK Friesland new deaths 0.00 9.73 19.46 29.18 38.91 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Friesland", dates="2020-03-15:");
2023-03-07T13:10:21.753464 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 Friesland LK Friesland 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 Friesland Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Friesland")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Friesland")
print(f'Population of country="Germany", subregion="LK Friesland": {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 Friesland": 98971 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 42430 2 86 0
2023-03-03 42428 6 86 0
2023-03-02 42422 4 86 0
2023-03-01 42418 11 86 0
2023-02-28 42407 14 86 0
2023-02-27 42393 15 86 0
2023-02-26 42378 5 86 0
2023-02-25 42373 1 86 0
2023-02-24 42372 8 86 0
2023-02-23 42364 9 86 0
2023-02-22 42355 16 86 0
2023-02-21 42339 35 86 0
2023-02-20 42304 6 86 0
2023-02-19 42298 7 86 0
2023-02-18 42291 3 86 0
2023-02-17 42288 14 86 0
2023-02-16 42274 8 86 0
2023-02-15 42266 13 86 0
2023-02-14 42253 27 86 0
2023-02-13 42226 9 86 0
2023-02-12 42217 1 86 0
2023-02-11 42216 6 86 0
2023-02-10 42210 7 86 0
2023-02-09 42203 13 86 1
2023-02-08 42190 14 85 0
2023-02-07 42176 18 85 0
2023-02-06 42158 10 85 0
2023-02-04 42148 12 85 0
2023-02-03 42136 9 85 0
2023-02-02 42127 8 85 0
2023-02-01 42119 14 85 1
2023-01-31 42105 41 84 0
2023-01-30 42064 21 84 0
2023-01-29 42043 2 84 0
2023-01-28 42041 17 84 0
2023-01-27 42024 24 84 0
2023-01-26 42000 17 84 0
2023-01-25 41983 20 84 0
2023-01-24 41963 30 84 0
2023-01-23 41933 11 84 0
2023-01-22 41922 3 84 0
2023-01-21 41919 12 84 0
2023-01-20 41907 21 84 0
2023-01-19 41886 17 84 0
2023-01-18 41869 16 84 1
2023-01-17 41853 52 83 0
2023-01-16 41801 5 83 0
2023-01-15 41796 1 83 0
2023-01-14 41795 20 83 1
2023-01-13 41775 13 82 0
2023-01-12 41762 24 82 0
2023-01-11 41738 21 82 1
2023-01-10 41717 69 81 0
2023-01-09 41648 24 81 0
2023-01-08 41624 2 81 0
2023-01-07 41622 25 81 0
2023-01-06 41597 13 81 0
2023-01-05 41584 29 81 0
2023-01-04 41555 21 81 0
2023-01-03 41534 62 81 0
2023-01-02 41472 28 81 0
2023-01-01 41444 2 81 0
2022-12-31 41442 41 81 1
2022-12-30 41401 60 80 1
2022-12-29 41341 49 79 0
2022-12-28 41292 94 79 0
2022-12-27 41198 57 79 0
2022-12-26 41141 20 79 0
2022-12-24 41121 47 79 0
2022-12-23 41074 49 79 0
2022-12-22 41025 106 79 0
2022-12-21 40919 99 79 0
2022-12-20 40820 134 79 0
2022-12-19 40686 23 79 1
2022-12-18 40663 9 78 0
2022-12-17 40654 47 78 0
2022-12-16 40607 33 78 0
2022-12-15 40574 92 78 0
2022-12-14 40482 78 78 0
2022-12-13 40404 138 78 0
2022-12-12 40266 49 78 0
2022-12-11 40217 10 78 0
2022-12-10 40207 56 78 0
2022-12-09 40151 73 78 0
2022-12-08 40078 58 78 0
2022-12-07 40020 79 78 1
2022-12-06 39941 107 77 0
2022-12-05 39834 38 77 0
2022-12-04 39796 3 77 0
2022-12-03 39793 62 77 0
2022-12-02 39731 61 77 0
2022-12-01 39670 86 77 1
2022-11-30 39584 72 76 0
2022-11-29 39512 126 76 0
2022-11-28 39386 56 76 1
2022-11-27 39330 4 75 0
2022-11-26 39326 52 75 0
2022-11-25 39274 45 75 0
2022-11-24 39229 82 75 0
2022-11-23 39147 85 75 0
2022-11-22 39062 125 75 0
2022-11-21 38937 66 75 0
2022-11-20 38871 15 75 0
2022-11-19 38856 45 75 0
2022-11-18 38811 72 75 0
2022-11-17 38739 97 75 0
2022-11-16 38642 58 75 0
2022-11-15 38584 176 75 0
2022-11-14 38408 51 75 0
2022-11-13 38357 14 75 0
2022-11-12 38343 58 75 1
2022-11-11 38285 89 74 0
2022-11-10 38196 64 74 0
2022-11-09 38132 131 74 0
2022-11-08 38001 53 74 0
2022-11-07 37948 14 74 0
2022-11-06 37934 14 74 0
2022-11-05 37920 29 74 0
2022-11-04 37891 45 74 0
2022-11-03 37846 67 74 1
2022-11-02 37779 159 73 0
2022-11-01 37620 55 73 0
2022-10-31 37565 6 73 0
2022-10-30 37559 7 73 0
2022-10-29 37552 59 73 0
2022-10-28 37493 72 73 0
2022-10-27 37421 75 73 0
2022-10-26 37346 115 73 0
2022-10-25 37231 219 73 0
2022-10-24 37012 88 73 0
2022-10-23 36924 9 73 0
2022-10-22 36915 86 73 0
2022-10-21 36829 130 73 1
2022-10-20 36699 109 72 0
2022-10-19 36590 216 72 0
2022-10-18 36374 230 72 0
2022-10-17 36144 84 72 1
2022-10-16 36060 19 71 0
2022-10-15 36041 95 71 0
2022-10-14 35946 133 71 0
2022-10-13 35813 180 71 0
2022-10-12 35633 232 71 0
2022-10-11 35401 254 71 0
2022-10-10 35147 28 71 0
2022-10-09 35119 79 71 0
2022-10-08 35040 65 71 0
2022-10-07 34975 131 71 0
2022-10-06 34844 233 71 0
2022-10-05 34611 173 71 1
2022-10-04 34438 63 70 0
2022-10-03 34375 25 70 0
2022-10-02 34350 19 70 0
2022-10-01 34331 117 70 0
2022-09-30 34214 84 70 0
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2022-08-24 32383 57 70 1
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2022-08-22 32227 31 69 1
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2022-07-31 30993 15 67 0
2022-07-30 30978 62 67 0
2022-07-29 30916 81 67 1
2022-07-28 30835 107 66 0
2022-07-27 30728 142 66 0
2022-07-26 30586 217 66 0
2022-07-25 30369 59 66 0
2022-07-24 30310 32 66 0
2022-07-23 30278 83 66 0
2022-07-22 30195 119 66 0
2022-07-21 30076 136 66 0
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2022-07-19 29718 268 66 1
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2021-01-22 923 6 28 0
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2021-01-11 853 11 28 0
2021-01-10 842 2 28 0
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2021-01-07 826 12 28 2
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2021-01-04 797 4 25 0
2021-01-03 793 4 25 0
2021-01-02 789 1 25 0
2021-01-01 788 3 25 0
2020-12-31 785 23 25 0
2020-12-30 762 4 25 0
2020-12-29 758 5 25 1
2020-12-27 753 10 24 2
2020-12-25 743 9 22 0
2020-12-24 734 16 22 1
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2020-12-18 646 10 21 2
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2020-12-12 590 18 19 0
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2020-12-08 535 19 18 3
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2020-12-05 500 40 14 4
2020-12-04 460 10 10 0
2020-12-03 450 11 10 1
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2020-11-22 307 8 6 1
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2020-10-31 187 7 3 0
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2020-10-27 157 3 3 0
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2020-10-24 149 1 3 0
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2020-10-18 136 1 3 0
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2020-10-15 132 2 3 0
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2020-10-10 121 1 3 0
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2020-09-28 105 2 2 0
2020-09-27 103 1 2 0
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2020-09-24 84 3 2 0
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2020-09-18 68 1 2 0
2020-09-16 67 5 2 0
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2020-08-21 46 1 2 0
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2020-08-19 41 1 2 0
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2020-07-08 39 1 2 0
2020-07-07 38 1 2 0
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2020-06-19 35 1 2 0
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2020-04-24 31 1 1 0
2020-04-19 30 1 1 0
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2020-04-12 27 2 1 0
2020-04-11 25 1 1 0
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2020-04-06 23 1 1 1
2020-04-03 22 5 0 0
2020-04-01 17 2 0 0
2020-03-31 15 3 0 0
2020-03-30 12 2 0 0
2020-03-27 10 1 0 0
2020-03-25 9 2 0 0
2020-03-24 7 1 0 0
2020-03-22 6 1 0 0
2020-03-21 5 2 0 0
2020-03-13 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: 0:02:53.317086