Germany: LK Cloppenburg (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 12:58:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Cloppenburg", weeks=5);
2023-03-07T12:59:13.989060 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 54.6 LK Cloppenburg, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 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 Cloppenburg new deaths (rolling 7d mean) Germany-LK Cloppenburg new deaths 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 Cloppenburg cases daily growth factor Germany-LK Cloppenburg cases daily growth factor (rolling mean) Germany-LK Cloppenburg 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 Cloppenburg deaths daily growth factor Germany-LK Cloppenburg deaths daily growth factor (rolling mean) Germany-LK Cloppenburg 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 Cloppenburg doubling time cases (rolling mean) 0.0 34.8 69.6 daily change Germany-LK Cloppenburg new cases (rolling 7d mean) Germany-LK Cloppenburg new cases 0.000 0.235 0.469 0.704 0.938
In [4]:
overview(country="Germany", subregion="LK Cloppenburg");
2023-03-07T12:59:32.065852 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 3000 3000 7-day incidence rate (per 100K people) 54.6 LK Cloppenburg, 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 0 200 400 600 800 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 8 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 Cloppenburg cases daily growth factor Germany-LK Cloppenburg cases daily growth factor (rolling mean) Germany-LK Cloppenburg 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 Cloppenburg deaths daily growth factor Germany-LK Cloppenburg deaths daily growth factor (rolling mean) Germany-LK Cloppenburg estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 cases doubling time [days] Germany-LK Cloppenburg doubling time cases (rolling mean) Germany-LK Cloppenburg doubling time deaths (rolling mean) 0 348 696 1044 1392 daily change Germany-LK Cloppenburg new cases (rolling 7d mean) Germany-LK Cloppenburg new cases 0.00 3.48 6.96 10.44 13.92 daily change Germany-LK Cloppenburg new deaths (rolling 7d mean) Germany-LK Cloppenburg new deaths 0.0 99.4 198.8 298.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Cloppenburg", dates="2020-03-15:");
2023-03-07T13:02:02.572506 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 Cloppenburg LK Cloppenburg 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 Cloppenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Cloppenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Cloppenburg")
print(f'Population of country="Germany", subregion="LK Cloppenburg": {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 Cloppenburg": 173980 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 99716 2 338 0
2023-03-05 99714 1 338 0
2023-03-04 99713 3 338 0
2023-03-03 99710 12 338 0
2023-03-02 99698 8 338 0
2023-03-01 99690 16 338 0
2023-02-28 99674 53 338 0
2023-02-27 99621 59 338 0
2023-02-26 99562 18 338 0
2023-02-25 99544 23 338 0
2023-02-24 99521 52 338 0
2023-02-23 99469 46 338 0
2023-02-22 99423 77 338 0
2023-02-21 99346 44 338 0
2023-02-20 99302 52 338 0
2023-02-19 99250 13 338 0
2023-02-18 99237 15 338 0
2023-02-17 99222 48 338 0
2023-02-16 99174 40 338 0
2023-02-15 99134 49 338 0
2023-02-14 99085 59 338 0
2023-02-13 99026 68 338 0
2023-02-12 98958 18 338 0
2023-02-11 98940 7 338 0
2023-02-10 98933 32 338 0
2023-02-09 98901 35 338 0
2023-02-08 98866 36 338 0
2023-02-07 98830 57 338 0
2023-02-06 98773 59 338 0
2023-02-05 98714 13 338 0
2023-02-04 98701 11 338 0
2023-02-03 98690 45 338 0
2023-02-02 98645 38 338 0
2023-02-01 98607 54 338 0
2023-01-31 98553 88 338 0
2023-01-30 98465 119 338 0
2023-01-29 98346 28 338 0
2023-01-28 98318 24 338 0
2023-01-27 98294 61 338 0
2023-01-26 98233 46 338 0
2023-01-25 98187 53 338 0
2023-01-24 98134 68 338 0
2023-01-23 98066 91 338 0
2023-01-22 97975 15 338 0
2023-01-21 97960 14 338 0
2023-01-20 97946 43 338 0
2023-01-19 97903 46 338 0
2023-01-18 97857 62 338 0
2023-01-17 97795 66 338 0
2023-01-16 97729 81 338 0
2023-01-15 97648 16 338 0
2023-01-14 97632 21 338 0
2023-01-13 97611 48 338 0
2023-01-12 97563 47 338 0
2023-01-11 97516 44 338 1
2023-01-10 97472 81 337 1
2023-01-09 97391 89 336 0
2023-01-08 97302 18 336 0
2023-01-07 97284 48 336 0
2023-01-06 97236 36 336 0
2023-01-05 97200 88 336 1
2023-01-04 97112 95 335 2
2023-01-03 97017 107 333 0
2023-01-02 96910 137 333 1
2023-01-01 96773 15 332 0
2022-12-31 96758 38 332 0
2022-12-30 96720 88 332 0
2022-12-29 96632 74 332 1
2022-12-28 96558 143 331 0
2022-12-27 96415 144 331 0
2022-12-26 96271 41 331 0
2022-12-25 96230 19 331 0
2022-12-24 96211 39 331 0
2022-12-23 96172 98 331 2
2022-12-22 96074 135 329 0
2022-12-21 95939 132 329 1
2022-12-20 95807 143 328 0
2022-12-19 95664 170 328 0
2022-12-18 95494 45 328 0
2022-12-17 95449 58 328 0
2022-12-16 95391 124 328 0
2022-12-15 95267 137 328 1
2022-12-14 95130 138 327 0
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2022-12-12 94859 190 327 1
2022-12-11 94669 35 326 0
2022-12-10 94634 52 326 0
2022-12-09 94582 118 326 1
2022-12-08 94464 140 325 1
2022-12-07 94324 165 324 0
2022-12-06 94159 201 324 2
2022-12-05 93958 136 322 1
2022-12-04 93822 22 321 0
2022-12-03 93800 36 321 0
2022-12-02 93764 85 321 0
2022-12-01 93679 92 321 0
2022-11-30 93587 84 321 0
2022-11-29 93503 123 321 0
2022-11-28 93380 138 321 0
2022-11-27 93242 16 321 0
2022-11-26 93226 46 321 0
2022-11-25 93180 72 321 0
2022-11-24 93108 110 321 0
2022-11-23 92998 67 321 0
2022-11-22 92931 119 321 0
2022-11-21 92812 76 321 0
2022-11-20 92736 15 321 0
2022-11-19 92721 20 321 0
2022-11-18 92701 53 321 0
2022-11-17 92648 81 321 0
2022-11-16 92567 60 321 0
2022-11-15 92507 88 321 0
2022-11-14 92419 123 321 1
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2022-11-12 92280 22 319 0
2022-11-11 92258 83 319 1
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2022-11-09 92113 85 317 0
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2022-11-07 91955 95 316 2
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2022-11-03 91722 78 312 0
2022-11-02 91644 88 312 0
2022-11-01 91556 145 312 1
2022-10-31 91411 19 311 0
2022-10-30 91392 18 311 0
2022-10-29 91374 46 311 0
2022-10-28 91328 94 311 2
2022-10-27 91234 97 309 0
2022-10-26 91137 118 309 0
2022-10-25 91019 128 309 0
2022-10-24 90891 144 309 0
2022-10-23 90747 33 309 0
2022-10-22 90714 47 309 0
2022-10-21 90667 142 309 0
2022-10-20 90525 130 309 1
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2022-10-18 90232 226 308 1
2022-10-17 90006 213 307 0
2022-10-16 89793 53 307 0
2022-10-15 89740 77 307 0
2022-10-14 89663 164 307 0
2022-10-13 89499 184 307 0
2022-10-12 89315 208 307 1
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2022-10-09 88623 55 304 0
2022-10-08 88568 103 304 0
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2022-10-06 88257 246 304 1
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2022-10-04 87769 288 302 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: 0:03:52.177338