Germany: SK Emden (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 13:39:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Emden", weeks=5);
2023-03-07T13:39:20.071662 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 42.4 SK Emden, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 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 1.4 1.4 1.6 1.6 R & growth factor (based on cases) Germany-SK Emden cases daily growth factor Germany-SK Emden cases daily growth factor (rolling mean) Germany-SK Emden 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-SK Emden deaths daily growth factor Germany-SK Emden deaths daily growth factor (rolling mean) Germany-SK Emden 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-SK Emden doubling time cases (rolling mean) 0.00 2.48 4.95 7.43 9.90 daily change Germany-SK Emden new cases (rolling 7d mean) Germany-SK Emden new cases 0.000 0.248 0.495 0.743 0.990 daily change Germany-SK Emden new deaths (rolling 7d mean) Germany-SK Emden new deaths 0.000 0.241 0.482 0.723 0.964
In [4]:
overview(country="Germany", subregion="SK Emden");
2023-03-07T13:39:37.193182 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) 42.4 SK Emden, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 1.6 1.6 R & growth factor (based on cases) Germany-SK Emden cases daily growth factor Germany-SK Emden cases daily growth factor (rolling mean) Germany-SK Emden 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-SK Emden deaths daily growth factor Germany-SK Emden deaths daily growth factor (rolling mean) Germany-SK Emden 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-SK Emden doubling time cases (rolling mean) Germany-SK Emden doubling time deaths (rolling mean) 0.0 99.0 198.1 297.1 daily change Germany-SK Emden new cases (rolling 7d mean) Germany-SK Emden new cases 0.000 0.990 1.981 2.971 3.962 daily change Germany-SK Emden new deaths (rolling 7d mean) Germany-SK Emden new deaths 0.00 4.82 9.63 14.45 19.26 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Emden", dates="2020-03-15:");
2023-03-07T13:41:39.239465 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: SK Emden SK Emden 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people SK Emden Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Emden")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Emden")
print(f'Population of country="Germany", subregion="SK Emden": {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="SK Emden": 49523 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 22362 2 46 0
2023-03-04 22360 2 46 0
2023-03-03 22358 1 46 0
2023-03-02 22357 3 46 0
2023-03-01 22354 5 46 0
2023-02-28 22349 8 46 0
2023-02-27 22341 4 46 0
2023-02-26 22337 1 46 0
2023-02-25 22336 2 46 0
2023-02-24 22334 5 46 0
2023-02-23 22329 3 46 0
2023-02-22 22326 4 46 0
2023-02-21 22322 4 46 1
2023-02-20 22318 8 45 0
2023-02-19 22310 2 45 0
2023-02-18 22308 1 45 0
2023-02-17 22307 5 45 0
2023-02-16 22302 6 45 0
2023-02-15 22296 10 45 0
2023-02-14 22286 3 45 0
2023-02-13 22283 9 45 0
2023-02-10 22274 3 45 0
2023-02-09 22271 4 45 0
2023-02-08 22267 4 45 0
2023-02-07 22263 3 45 0
2023-02-06 22260 3 45 0
2023-02-05 22257 2 45 0
2023-02-04 22255 3 45 0
2023-02-03 22252 2 45 0
2023-02-02 22250 7 45 0
2023-02-01 22243 10 45 0
2023-01-31 22233 11 45 0
2023-01-30 22222 12 45 0
2023-01-28 22210 2 45 0
2023-01-27 22208 8 45 0
2023-01-26 22200 5 45 0
2023-01-25 22195 4 45 0
2023-01-24 22191 10 45 0
2023-01-23 22181 8 45 0
2023-01-22 22173 1 45 0
2023-01-21 22172 3 45 0
2023-01-20 22169 2 45 0
2023-01-19 22167 4 45 0
2023-01-18 22163 7 45 0
2023-01-17 22156 6 45 0
2023-01-16 22150 6 45 0
2023-01-15 22144 1 45 0
2023-01-13 22143 6 45 0
2023-01-12 22137 6 45 0
2023-01-11 22131 4 45 0
2023-01-10 22127 14 45 0
2023-01-09 22113 18 45 0
2023-01-08 22095 3 45 0
2023-01-07 22092 1 45 0
2023-01-06 22091 16 45 0
2023-01-05 22075 14 45 0
2023-01-04 22061 23 45 0
2023-01-03 22038 32 45 0
2023-01-02 22006 16 45 0
2023-01-01 21990 4 45 0
2022-12-31 21986 6 45 0
2022-12-30 21980 19 45 0
2022-12-29 21961 20 45 0
2022-12-28 21941 47 45 0
2022-12-27 21894 36 45 0
2022-12-26 21858 11 45 0
2022-12-25 21847 4 45 0
2022-12-24 21843 3 45 0
2022-12-23 21840 27 45 0
2022-12-22 21813 31 45 0
2022-12-21 21782 30 45 1
2022-12-20 21752 67 44 0
2022-12-19 21685 13 44 0
2022-12-18 21672 8 44 0
2022-12-17 21664 6 44 0
2022-12-16 21658 26 44 0
2022-12-15 21632 29 44 0
2022-12-14 21603 27 44 0
2022-12-13 21576 40 44 0
2022-12-12 21536 32 44 0
2022-12-11 21504 5 44 0
2022-12-10 21499 3 44 0
2022-12-09 21496 19 44 0
2022-12-08 21477 19 44 0
2022-12-07 21458 33 44 0
2022-12-06 21425 48 44 0
2022-12-05 21377 29 44 0
2022-12-03 21348 1 44 0
2022-12-02 21347 11 44 0
2022-12-01 21336 19 44 0
2022-11-30 21317 23 44 0
2022-11-29 21294 39 44 0
2022-11-28 21255 39 44 0
2022-11-27 21216 1 44 0
2022-11-26 21215 1 44 0
2022-11-25 21214 18 44 0
2022-11-24 21196 28 44 0
2022-11-23 21168 15 44 0
2022-11-22 21153 35 44 0
2022-11-21 21118 30 44 0
2022-11-19 21088 1 44 0
2022-11-18 21087 21 44 0
2022-11-17 21066 19 44 0
2022-11-16 21047 30 44 0
2022-11-15 21017 41 44 0
2022-11-14 20976 20 44 0
2022-11-13 20956 3 44 0
2022-11-12 20953 17 44 0
2022-11-11 20936 9 44 0
2022-11-10 20927 18 44 0
2022-11-09 20909 12 44 0
2022-11-08 20897 31 44 0
2022-11-07 20866 20 44 0
2022-11-06 20846 1 44 0
2022-11-05 20845 1 44 0
2022-11-04 20844 29 44 0
2022-11-03 20815 22 44 0
2022-11-02 20793 93 44 0
2022-11-01 20700 30 44 0
2022-10-30 20670 1 44 0
2022-10-29 20669 4 44 0
2022-10-28 20665 35 44 0
2022-10-27 20630 40 44 0
2022-10-26 20590 59 44 0
2022-10-25 20531 57 44 0
2022-10-24 20474 55 44 0
2022-10-23 20419 2 44 0
2022-10-22 20417 5 44 0
2022-10-21 20412 48 44 0
2022-10-20 20364 37 44 0
2022-10-19 20327 47 44 0
2022-10-18 20280 85 44 0
2022-10-17 20195 53 44 0
2022-10-16 20142 2 44 0
2022-10-15 20140 4 44 0
2022-10-14 20136 48 44 0
2022-10-13 20088 69 44 0
2022-10-12 20019 58 44 0
2022-10-11 19961 96 44 0
2022-10-10 19865 40 44 0
2022-10-09 19825 3 44 0
2022-10-08 19822 5 44 0
2022-10-07 19817 77 44 0
2022-10-06 19740 117 44 0
2022-10-05 19623 70 44 0
2022-10-04 19553 26 44 0
2022-10-03 19527 1 44 0
2022-10-02 19526 4 44 0
2022-10-01 19522 6 44 0
2022-09-30 19516 34 44 0
2022-09-29 19482 28 44 0
2022-09-28 19454 43 44 0
2022-09-27 19411 71 44 1
2022-09-26 19340 44 43 0
2022-09-25 19296 6 43 0
2022-09-24 19290 2 43 0
2022-09-23 19288 23 43 0
2022-09-22 19265 29 43 1
2022-09-21 19236 27 42 0
2022-09-20 19209 45 42 0
2022-09-19 19164 32 42 0
2022-09-17 19132 4 42 0
2022-09-16 19128 25 42 0
2022-09-15 19103 16 42 0
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2022-09-13 19054 31 42 0
2022-09-12 19023 24 42 0
2022-09-10 18999 8 42 0
2022-09-09 18991 16 42 0
2022-09-08 18975 18 42 0
2022-09-07 18957 16 42 0
2022-09-06 18941 32 42 0
2022-09-05 18909 34 42 0
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2022-08-30 18811 33 42 0
2022-08-29 18778 25 42 0
2022-08-26 18753 22 42 0
2022-08-25 18731 18 42 0
2022-08-24 18713 16 42 0
2022-08-23 18697 34 42 0
2022-08-22 18663 21 42 0
2022-08-21 18642 2 42 0
2022-08-19 18640 5 42 0
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2022-08-12 18490 37 42 0
2022-08-11 18453 31 42 0
2022-08-10 18422 34 42 0
2022-08-09 18388 57 42 0
2022-08-08 18331 34 42 0
2022-08-06 18297 1 42 0
2022-08-05 18296 19 42 0
2022-08-04 18277 23 42 0
2022-08-03 18254 31 42 0
2022-08-02 18223 52 42 0
2022-08-01 18171 64 42 0
2022-07-30 18107 9 42 0
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2022-07-26 17985 75 42 0
2022-07-25 17910 89 42 0
2022-07-24 17821 2 42 0
2022-07-23 17819 9 42 0
2022-07-22 17810 79 42 0
2022-07-21 17731 116 42 0
2022-07-20 17615 87 42 0
2022-07-19 17528 89 42 0
2022-07-18 17439 68 42 0
2022-07-16 17371 5 42 0
2022-07-15 17366 52 42 0
2022-07-14 17314 61 42 0
2022-07-13 17253 62 42 0
2022-07-12 17191 88 42 0
2022-07-11 17103 88 42 1
2022-07-10 17015 1 41 0
2022-07-09 17014 9 41 0
2022-07-08 17005 55 41 0
2022-07-07 16950 76 41 0
2022-07-06 16874 21 41 0
2022-07-05 16853 94 41 0
2022-07-04 16759 74 41 0
2022-07-03 16685 2 41 0
2022-07-02 16683 11 41 0
2022-07-01 16672 66 41 0
2022-06-30 16606 63 41 0
2022-06-29 16543 80 41 0
2022-06-28 16463 131 41 0
2022-06-27 16332 44 41 0
2022-06-26 16288 1 41 0
2022-06-25 16287 1 41 0
2022-06-24 16286 54 41 0
2022-06-23 16232 61 41 0
2022-06-22 16171 93 41 0
2022-06-21 16078 71 41 0
2022-06-20 16007 61 41 0
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2022-04-04 11905 19 41 1
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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: 0:02:53.054490