Germany: SK Berlin Reinickendorf (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 12:06:53 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Reinickendorf", weeks=5);
2023-03-07T12:07:09.656890 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 7-day incidence rate (per 100K people) 54.5 SK Berlin Reinickendorf, 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 0.1 0.2 0.3 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 Reinickendorf cases daily growth factor Germany-SK Berlin Reinickendorf cases daily growth factor (rolling mean) Germany-SK Berlin Reinickendorf 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 Berlin Reinickendorf deaths daily growth factor Germany-SK Berlin Reinickendorf deaths daily growth factor (rolling mean) Germany-SK Berlin Reinickendorf 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 Reinickendorf doubling time cases (rolling mean) 0.00 13.03 26.06 39.09 52.12 daily change Germany-SK Berlin Reinickendorf new cases (rolling 7d mean) Germany-SK Berlin Reinickendorf new cases 0.000 0.261 0.521 0.782 daily change Germany-SK Berlin Reinickendorf new deaths (rolling 7d mean) Germany-SK Berlin Reinickendorf new deaths 0.000 0.368 0.736
In [4]:
overview(country="Germany", subregion="SK Berlin Reinickendorf");
2023-03-07T12:07:31.501683 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) 54.5 SK Berlin Reinickendorf, 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 Reinickendorf cases daily growth factor Germany-SK Berlin Reinickendorf cases daily growth factor (rolling mean) Germany-SK Berlin Reinickendorf 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 Berlin Reinickendorf deaths daily growth factor Germany-SK Berlin Reinickendorf deaths daily growth factor (rolling mean) Germany-SK Berlin Reinickendorf 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 cases doubling time [days] Germany-SK Berlin Reinickendorf doubling time cases (rolling mean) Germany-SK Berlin Reinickendorf doubling time deaths (rolling mean) 0 261 521 782 daily change Germany-SK Berlin Reinickendorf new cases (rolling 7d mean) Germany-SK Berlin Reinickendorf new cases 0.00 2.61 5.21 7.82 10.42 daily change Germany-SK Berlin Reinickendorf new deaths (rolling 7d mean) Germany-SK Berlin Reinickendorf new deaths 0.0 140.6 281.3 421.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Reinickendorf", dates="2020-03-15:");
2023-03-07T12:09:49.609065 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 Reinickendorf SK Berlin Reinickendorf 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 Reinickendorf Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Reinickendorf")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Reinickendorf")
print(f'Population of country="Germany", subregion="SK Berlin Reinickendorf": {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 Reinickendorf": 260576 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 94478 20 473 0
2023-03-04 94458 11 473 0
2023-03-03 94447 19 473 0
2023-03-02 94428 13 473 0
2023-03-01 94415 42 473 1
... ... ... ... ...
2020-03-14 12 1 0 0
2020-03-13 11 2 0 0
2020-03-12 9 4 0 0
2020-03-11 5 2 0 0
2020-03-09 3 1 0 0

1014 rows × 4 columns

Explore the data in your web browser¶

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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: 0:03:06.757132