Germany: SK Berlin Steglitz-Zehlendorf (Berlin)¶

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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 12:06:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf", weeks=5);
2023-03-07T12:06:24.392031 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 66.0 SK Berlin Steglitz-Zehlendorf, 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 0.2 0.4 0.6 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-SK Berlin Steglitz-Zehlendorf cases daily growth factor Germany-SK Berlin Steglitz-Zehlendorf cases daily growth factor (rolling mean) Germany-SK Berlin Steglitz-Zehlendorf estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-SK Berlin Steglitz-Zehlendorf deaths daily growth factor Germany-SK Berlin Steglitz-Zehlendorf deaths daily growth factor (rolling mean) Germany-SK Berlin Steglitz-Zehlendorf estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Berlin Steglitz-Zehlendorf doubling time cases (rolling mean) 0.00 29.23 58.45 daily change Germany-SK Berlin Steglitz-Zehlendorf new cases (rolling 7d mean) Germany-SK Berlin Steglitz-Zehlendorf new cases 0.000 0.585 1.169 1.754 daily change Germany-SK Berlin Steglitz-Zehlendorf new deaths (rolling 7d mean) Germany-SK Berlin Steglitz-Zehlendorf new deaths 0.000 0.220 0.441 0.661 0.882
In [4]:
overview(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf");
2023-03-07T12:06:44.722957 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 7-day incidence rate (per 100K people) 66.0 SK Berlin Steglitz-Zehlendorf, 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 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-SK Berlin Steglitz-Zehlendorf cases daily growth factor Germany-SK Berlin Steglitz-Zehlendorf cases daily growth factor (rolling mean) Germany-SK Berlin Steglitz-Zehlendorf estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-SK Berlin Steglitz-Zehlendorf deaths daily growth factor Germany-SK Berlin Steglitz-Zehlendorf deaths daily growth factor (rolling mean) Germany-SK Berlin Steglitz-Zehlendorf estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Berlin Steglitz-Zehlendorf doubling time cases (rolling mean) Germany-SK Berlin Steglitz-Zehlendorf doubling time deaths (rolling mean) 0 292 585 877 daily change Germany-SK Berlin Steglitz-Zehlendorf new cases (rolling 7d mean) Germany-SK Berlin Steglitz-Zehlendorf new cases 0.00 2.92 5.85 8.77 11.69 daily change Germany-SK Berlin Steglitz-Zehlendorf new deaths (rolling 7d mean) Germany-SK Berlin Steglitz-Zehlendorf new deaths 0.0 154.8 309.5 464.3 619.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf", dates="2020-03-15:");
2023-03-07T12:08:55.067921 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 Berlin Steglitz-Zehlendorf SK Berlin Steglitz-Zehlendorf 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 SK Berlin Steglitz-Zehlendorf Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Steglitz-Zehlendorf")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Steglitz-Zehlendorf")
print(f'Population of country="Germany", subregion="SK Berlin Steglitz-Zehlendorf": {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 Berlin Steglitz-Zehlendorf": 292255 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 107122 21 630 0
2023-03-03 107101 17 630 0
2023-03-02 107084 26 630 1
2023-03-01 107058 59 629 1
2023-02-28 106999 70 628 0
2023-02-27 106929 66 628 0
2023-02-24 106863 42 628 0
2023-02-23 106821 41 628 0
2023-02-22 106780 42 628 0
2023-02-21 106738 77 628 2
2023-02-20 106661 54 626 0
2023-02-17 106607 32 626 0
2023-02-16 106575 22 626 0
2023-02-15 106553 44 626 0
2023-02-14 106509 48 626 0
2023-02-13 106461 67 626 2
2023-02-10 106394 38 624 0
2023-02-09 106356 26 624 0
2023-02-08 106330 33 624 0
2023-02-07 106297 46 624 0
2023-02-06 106251 43 624 1
2023-02-03 106208 32 623 0
2023-02-02 106176 25 623 0
2023-02-01 106151 39 623 0
2023-01-31 106112 77 623 1
2023-01-30 106035 48 622 0
2023-01-27 105987 30 622 0
2023-01-26 105957 46 622 0
2023-01-25 105911 35 622 1
2023-01-24 105876 39 621 0
2023-01-23 105837 37 621 0
2023-01-20 105800 40 621 0
2023-01-19 105760 12 621 0
2023-01-18 105748 27 621 1
2023-01-17 105721 49 620 0
2023-01-16 105672 55 620 0
2023-01-13 105617 28 620 0
2023-01-12 105589 44 620 0
2023-01-11 105545 60 620 1
2023-01-10 105485 48 619 0
2023-01-09 105437 85 619 2
2023-01-06 105352 44 617 0
2023-01-05 105308 73 617 0
2023-01-04 105235 107 617 0
2023-01-03 105128 131 617 1
2023-01-02 104997 136 616 2
2022-12-31 104861 1 614 0
2022-12-30 104860 109 614 5
2022-12-29 104751 170 609 3
2022-12-28 104581 171 606 3
2022-12-27 104410 194 603 1
2022-12-23 104216 104 602 1
2022-12-22 104112 164 601 2
2022-12-21 103948 149 599 1
2022-12-20 103799 206 598 1
2022-12-19 103593 231 597 2
2022-12-17 103362 1 595 0
2022-12-16 103361 185 595 1
2022-12-15 103176 174 594 2
2022-12-14 103002 133 592 1
2022-12-13 102869 172 591 0
2022-12-12 102697 277 591 0
2022-12-11 102420 1 591 0
2022-12-10 102419 1 591 0
2022-12-09 102418 123 591 1
2022-12-08 102295 160 590 0
2022-12-07 102135 130 590 0
2022-12-06 102005 213 590 2
2022-12-05 101792 212 588 1
2022-12-03 101580 2 587 0
2022-12-02 101578 134 587 2
2022-12-01 101444 156 585 1
2022-11-30 101288 146 584 0
2022-11-29 101142 135 584 0
2022-11-28 101007 224 584 1
2022-11-25 100783 106 583 1
2022-11-24 100677 100 582 0
2022-11-23 100577 120 582 0
2022-11-22 100457 83 582 0
2022-11-21 100374 153 582 1
2022-11-18 100221 103 581 0
2022-11-17 100118 142 581 0
2022-11-16 99976 98 581 0
2022-11-15 99878 81 581 0
2022-11-14 99797 174 581 1
2022-11-11 99623 83 580 0
2022-11-10 99540 70 580 2
2022-11-09 99470 103 578 0
2022-11-08 99367 131 578 0
2022-11-07 99236 136 578 0
2022-11-04 99100 70 578 0
2022-11-03 99030 117 578 1
2022-11-02 98913 108 577 0
2022-11-01 98805 167 577 0
2022-10-31 98638 268 577 0
2022-10-30 98370 2 577 0
2022-10-29 98368 7 577 0
2022-10-28 98361 152 577 3
2022-10-27 98209 167 574 1
2022-10-26 98042 269 573 0
2022-10-25 97773 260 573 1
2022-10-24 97513 257 572 0
2022-10-23 97256 5 572 0
2022-10-22 97251 6 572 0
2022-10-21 97245 392 572 1
2022-10-20 96853 332 571 1
2022-10-19 96521 289 570 0
2022-10-18 96232 305 570 2
2022-10-17 95927 323 568 1
2022-10-16 95604 2 567 0
2022-10-15 95602 15 567 0
2022-10-14 95587 278 567 1
2022-10-13 95309 270 566 0
2022-10-12 95039 288 566 0
2022-10-11 94751 343 566 1
2022-10-10 94408 378 565 3
2022-10-09 94030 2 562 0
2022-10-07 94028 376 562 2
2022-10-06 93652 344 560 0
2022-10-05 93308 354 560 1
2022-10-04 92954 199 559 3
2022-09-30 92755 223 556 4
2022-09-29 92532 136 552 0
2022-09-28 92396 201 552 0
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2022-09-26 91980 141 550 1
2022-09-25 91839 2 549 0
2022-09-24 91837 1 549 0
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2022-09-18 91131 1 545 0
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2022-09-15 90990 149 545 0
2022-09-14 90841 110 545 0
2022-09-13 90731 112 545 0
2022-09-12 90619 133 545 0
2022-09-11 90486 3 545 0
2022-09-10 90483 3 545 0
2022-09-09 90480 115 545 0
2022-09-08 90365 146 545 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: 0:03:25.124201