Germany: SK Berlin Lichtenberg (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 11:58:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Lichtenberg", weeks=5);
2023-03-07T11:59:02.145059 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 7-day incidence rate (per 100K people) 45.0 SK Berlin Lichtenberg, 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 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 Lichtenberg cases daily growth factor Germany-SK Berlin Lichtenberg cases daily growth factor (rolling mean) Germany-SK Berlin Lichtenberg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-SK Berlin Lichtenberg deaths daily growth factor Germany-SK Berlin Lichtenberg deaths daily growth factor (rolling mean) Germany-SK Berlin Lichtenberg 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 Lichtenberg doubling time cases (rolling mean) Germany-SK Berlin Lichtenberg doubling time deaths (rolling mean) 0.00 14.66 29.31 43.97 daily change Germany-SK Berlin Lichtenberg new cases (rolling 7d mean) Germany-SK Berlin Lichtenberg new cases 0.000 0.586 1.173 1.759 daily change Germany-SK Berlin Lichtenberg new deaths (rolling 7d mean) Germany-SK Berlin Lichtenberg new deaths 0 100 200 300 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="SK Berlin Lichtenberg");
2023-03-07T11:59:18.205729 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 2000 2000 7-day incidence rate (per 100K people) 45.0 SK Berlin Lichtenberg, 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 0 100 200 300 400 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 Lichtenberg cases daily growth factor Germany-SK Berlin Lichtenberg cases daily growth factor (rolling mean) Germany-SK Berlin Lichtenberg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-SK Berlin Lichtenberg deaths daily growth factor Germany-SK Berlin Lichtenberg deaths daily growth factor (rolling mean) Germany-SK Berlin Lichtenberg 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 Lichtenberg doubling time cases (rolling mean) Germany-SK Berlin Lichtenberg doubling time deaths (rolling mean) 0 293 586 879 1173 daily change Germany-SK Berlin Lichtenberg new cases (rolling 7d mean) Germany-SK Berlin Lichtenberg new cases 0.00 2.93 5.86 8.79 11.73 daily change Germany-SK Berlin Lichtenberg new deaths (rolling 7d mean) Germany-SK Berlin Lichtenberg new deaths 0.0 71.2 142.5 213.7 284.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Lichtenberg", dates="2020-03-15:");
2023-03-07T12:01:58.985409 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 Lichtenberg SK Berlin Lichtenberg 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 Lichtenberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Lichtenberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Lichtenberg")
print(f'Population of country="Germany", subregion="SK Berlin Lichtenberg": {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 Lichtenberg": 293139 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 112790 22 413 0
2023-03-03 112768 7 413 0
2023-03-02 112761 13 413 0
2023-03-01 112748 47 413 0
2023-02-28 112701 43 413 0
2023-02-27 112658 51 413 1
2023-02-24 112607 43 412 0
2023-02-23 112564 31 412 0
2023-02-22 112533 32 412 0
2023-02-21 112501 56 412 0
2023-02-20 112445 42 412 2
2023-02-17 112403 18 410 0
2023-02-16 112385 24 410 0
2023-02-15 112361 28 410 1
2023-02-14 112333 38 409 1
2023-02-13 112295 50 408 0
2023-02-10 112245 55 408 0
2023-02-08 112190 50 408 0
2023-02-07 112140 35 408 0
2023-02-06 112105 39 408 0
2023-02-03 112066 27 408 0
2023-02-02 112039 30 408 0
2023-02-01 112009 40 408 0
2023-01-31 111969 46 408 0
2023-01-30 111923 43 408 1
2023-01-27 111880 22 407 0
2023-01-26 111858 44 407 0
2023-01-25 111814 23 407 1
2023-01-24 111791 55 406 0
2023-01-23 111736 47 406 0
2023-01-20 111689 28 406 0
2023-01-19 111661 24 406 0
2023-01-18 111637 34 406 0
2023-01-17 111603 51 406 0
2023-01-16 111552 60 406 3
2023-01-13 111492 19 403 0
2023-01-12 111473 21 403 0
2023-01-11 111452 74 403 1
2023-01-10 111378 32 402 0
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2023-01-06 111241 42 402 0
2023-01-05 111199 71 402 0
2023-01-04 111128 81 402 0
2023-01-03 111047 93 402 0
2023-01-02 110954 137 402 0
2022-12-30 110817 76 402 1
2022-12-29 110741 116 401 0
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2022-12-27 110482 257 401 2
2022-12-23 110225 122 399 0
2022-12-22 110103 148 399 0
2022-12-21 109955 140 399 0
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2022-12-19 109583 234 399 2
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2022-12-09 108438 99 388 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: 0:03:28.327327