Germany: SK Berlin Spandau (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 12:21:12 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Spandau", weeks=5);
2023-03-07T12:21:25.323377 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) 49.9 SK Berlin Spandau, 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 0.1 0.2 0.3 0.4 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 Spandau cases daily growth factor Germany-SK Berlin Spandau cases daily growth factor (rolling mean) Germany-SK Berlin Spandau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on deaths) Germany-SK Berlin Spandau deaths daily growth factor Germany-SK Berlin Spandau deaths daily growth factor (rolling mean) Germany-SK Berlin Spandau 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-SK Berlin Spandau doubling time cases (rolling mean) 0.00 24.05 48.11 daily change Germany-SK Berlin Spandau new cases (rolling 7d mean) Germany-SK Berlin Spandau new cases 0.000 0.241 0.481 0.722 0.962 daily change Germany-SK Berlin Spandau new deaths (rolling 7d mean) Germany-SK Berlin Spandau new deaths 0.000 0.309 0.618 0.927
In [4]:
overview(country="Germany", subregion="SK Berlin Spandau");
2023-03-07T12:21:43.247947 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 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 2000 2000 7-day incidence rate (per 100K people) 49.9 SK Berlin Spandau, Germany, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 100 200 300 400 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 daily change normalised per 100K Jan 20 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 Spandau cases daily growth factor Germany-SK Berlin Spandau cases daily growth factor (rolling mean) Germany-SK Berlin Spandau estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on deaths) Germany-SK Berlin Spandau deaths daily growth factor Germany-SK Berlin Spandau deaths daily growth factor (rolling mean) Germany-SK Berlin Spandau estimated R (using deaths) Jan 20 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 Spandau doubling time cases (rolling mean) Germany-SK Berlin Spandau doubling time deaths (rolling mean) 0 241 481 722 962 daily change Germany-SK Berlin Spandau new cases (rolling 7d mean) Germany-SK Berlin Spandau new cases 0.00 4.81 9.62 daily change Germany-SK Berlin Spandau new deaths (rolling 7d mean) Germany-SK Berlin Spandau new deaths 0.0 93.5 187.0 280.6 374.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Spandau", dates="2020-03-15:");
2023-03-07T12:24:00.968587 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 Spandau SK Berlin Spandau 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 Spandau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Spandau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Spandau")
print(f'Population of country="Germany", subregion="SK Berlin Spandau": {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 Spandau": 240542 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 93651 20 398 0
2023-03-03 93631 6 398 0
2023-03-02 93625 17 398 0
2023-03-01 93608 25 398 0
2023-02-28 93583 52 398 0
2023-02-27 93531 41 398 1
2023-02-24 93490 28 397 0
2023-02-23 93462 34 397 0
2023-02-22 93428 36 397 1
2023-02-21 93392 32 396 0
2023-02-20 93360 49 396 1
2023-02-17 93311 24 395 0
2023-02-16 93287 30 395 0
2023-02-15 93257 35 395 0
2023-02-14 93222 18 395 0
2023-02-13 93204 68 395 1
2023-02-10 93136 40 394 1
2023-02-09 93096 22 393 0
2023-02-08 93074 58 393 1
2023-02-07 93016 34 392 0
2023-02-06 92982 48 392 0
2023-02-03 92934 33 392 0
2023-02-02 92901 20 392 0
2023-02-01 92881 26 392 0
2023-01-31 92855 40 392 0
2023-01-30 92815 48 392 0
2023-01-27 92767 29 392 0
2023-01-26 92738 34 392 0
2023-01-25 92704 24 392 0
2023-01-24 92680 45 392 0
2023-01-23 92635 49 392 0
2023-01-20 92586 22 392 1
2023-01-19 92564 21 391 0
2023-01-18 92543 43 391 0
2023-01-17 92500 33 391 1
2023-01-16 92467 38 390 4
2023-01-13 92429 23 386 0
2023-01-12 92406 27 386 0
2023-01-11 92379 41 386 0
2023-01-10 92338 45 386 0
2023-01-09 92293 71 386 0
2023-01-06 92222 31 386 1
2023-01-05 92191 56 385 0
2023-01-04 92135 86 385 0
2023-01-03 92049 74 385 1
2023-01-02 91975 136 384 2
2022-12-30 91839 56 382 0
2022-12-29 91783 101 382 4
2022-12-28 91682 113 378 1
2022-12-27 91569 256 377 4
2022-12-23 91313 106 373 0
2022-12-22 91207 97 373 0
2022-12-21 91110 141 373 2
2022-12-20 90969 142 371 0
2022-12-19 90827 228 371 3
2022-12-16 90599 113 368 0
2022-12-15 90486 150 368 1
2022-12-14 90336 146 367 2
2022-12-13 90190 133 365 2
2022-12-12 90057 275 363 2
2022-12-09 89782 153 361 1
2022-12-08 89629 118 360 1
2022-12-07 89511 208 359 1
2022-12-06 89303 25 358 0
2022-12-05 89278 190 358 3
2022-12-02 89088 108 355 0
2022-12-01 88980 99 355 2
2022-11-30 88881 120 353 3
2022-11-29 88761 120 350 0
2022-11-28 88641 212 350 2
2022-11-25 88429 61 348 0
2022-11-24 88368 85 348 0
2022-11-23 88283 71 348 1
2022-11-22 88212 56 347 0
2022-11-21 88156 124 347 1
2022-11-18 88032 46 346 0
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2022-11-01 87013 130 345 0
2022-10-31 86883 208 345 2
2022-10-28 86675 96 343 1
2022-10-27 86579 120 342 2
2022-10-26 86459 153 340 0
2022-10-25 86306 157 340 1
2022-10-24 86149 261 339 1
2022-10-21 85888 139 338 0
2022-10-20 85749 160 338 1
2022-10-19 85589 204 337 3
2022-10-18 85385 245 334 1
2022-10-17 85140 362 333 1
2022-10-14 84778 136 332 0
2022-10-13 84642 315 332 4
2022-10-12 84327 79 328 0
2022-10-11 84248 380 328 0
2022-10-10 83868 288 328 0
2022-10-07 83580 188 328 1
2022-10-06 83392 189 327 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: 0:03:16.260157