Germany: SK Berlin Pankow (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 11:58:38 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Pankow", weeks=5);
2023-03-07T11:58:53.804327 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 7-day incidence rate (per 100K people) 63.0 SK Berlin Pankow, 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 0.00 0.05 0.10 0.15 0.20 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 Pankow cases daily growth factor Germany-SK Berlin Pankow cases daily growth factor (rolling mean) Germany-SK Berlin Pankow estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-SK Berlin Pankow deaths daily growth factor Germany-SK Berlin Pankow deaths daily growth factor (rolling mean) Germany-SK Berlin Pankow estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Berlin Pankow doubling time cases (rolling mean) 0.0 20.2 40.5 60.7 81.0 daily change Germany-SK Berlin Pankow new cases (rolling 7d mean) Germany-SK Berlin Pankow new cases 0.000 0.202 0.405 0.607 0.810 daily change Germany-SK Berlin Pankow new deaths (rolling 7d mean) Germany-SK Berlin Pankow new deaths 0.000 0.372 0.744
In [4]:
overview(country="Germany", subregion="SK Berlin Pankow");
2023-03-07T11:59:14.347058 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) 63.0 SK Berlin Pankow, 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 0.0 0.5 1.0 1.5 2.0 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 Pankow cases daily growth factor Germany-SK Berlin Pankow cases daily growth factor (rolling mean) Germany-SK Berlin Pankow estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-SK Berlin Pankow deaths daily growth factor Germany-SK Berlin Pankow deaths daily growth factor (rolling mean) Germany-SK Berlin Pankow 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 Berlin Pankow doubling time cases (rolling mean) Germany-SK Berlin Pankow doubling time deaths (rolling mean) 0 810 1619 2429 daily change Germany-SK Berlin Pankow new cases (rolling 7d mean) Germany-SK Berlin Pankow new cases 0.00 2.02 4.05 6.07 8.10 daily change Germany-SK Berlin Pankow new deaths (rolling 7d mean) Germany-SK Berlin Pankow new deaths 0.0 70.3 140.7 211.0 281.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Pankow", dates="2020-03-15:");
2023-03-07T12:01:52.983871 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 Pankow SK Berlin Pankow 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 Pankow Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Pankow")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Pankow")
print(f'Population of country="Germany", subregion="SK Berlin Pankow": {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 Pankow": 404795 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 164254 51 374 0
2023-03-03 164203 22 374 0
2023-03-02 164181 43 374 1
2023-03-01 164138 62 373 0
2023-02-28 164076 77 373 1
2023-02-27 163999 98 372 0
2023-02-24 163901 55 372 0
2023-02-23 163846 55 372 0
2023-02-22 163791 62 372 0
2023-02-21 163729 86 372 0
2023-02-20 163643 61 372 0
2023-02-17 163582 36 372 0
2023-02-16 163546 52 372 1
2023-02-15 163494 65 371 0
2023-02-14 163429 62 371 1
2023-02-13 163367 80 370 0
2023-02-10 163287 34 370 0
2023-02-09 163253 41 370 0
2023-02-08 163212 50 370 1
2023-02-07 163162 65 369 0
2023-02-06 163097 54 369 0
2023-02-03 163043 36 369 1
2023-02-02 163007 35 368 1
2023-02-01 162972 46 367 0
2023-01-31 162926 71 367 0
2023-01-30 162855 71 367 1
2023-01-27 162784 39 366 0
2023-01-26 162745 55 366 0
2023-01-25 162690 32 366 0
2023-01-24 162658 40 366 0
2023-01-23 162618 52 366 1
2023-01-20 162566 52 365 0
2023-01-19 162514 37 365 0
2023-01-18 162477 55 365 0
2023-01-17 162422 54 365 0
2023-01-16 162368 86 365 1
2023-01-13 162282 50 364 1
2023-01-12 162232 79 363 0
2023-01-11 162153 56 363 0
2023-01-10 162097 77 363 0
2023-01-09 162020 132 363 1
2023-01-06 161888 90 362 1
2023-01-05 161798 97 361 1
2023-01-04 161701 123 360 0
2023-01-03 161578 147 360 1
2023-01-02 161431 171 359 1
2022-12-30 161260 142 358 0
2022-12-29 161118 186 358 0
2022-12-28 160932 170 358 0
2022-12-27 160762 331 358 2
2022-12-23 160431 222 356 0
2022-12-22 160209 209 356 0
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2022-12-20 159763 284 356 1
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2022-12-14 158707 333 350 2
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2022-12-12 158064 164 348 2
2022-12-09 157900 212 346 1
2022-12-08 157688 179 345 1
2022-12-07 157509 227 344 0
2022-12-06 157282 277 344 1
2022-12-05 157005 307 343 0
2022-12-02 156698 176 343 0
2022-12-01 156522 197 343 2
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2022-11-01 152729 276 338 0
2022-10-31 152453 361 338 1
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2022-10-11 147709 457 330 0
2022-10-10 147252 623 330 4
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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: 0:03:21.654105