Germany: SK Potsdam (Brandenburg)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 12:28:24 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Potsdam", weeks=5);
2023-03-07T12:28:39.777138 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 200 200 400 400 7-day incidence rate (per 100K people) 55.7 SK Potsdam, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 100 200 300 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on cases) Germany-SK Potsdam cases daily growth factor Germany-SK Potsdam cases daily growth factor (rolling mean) Germany-SK Potsdam 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 Potsdam deaths daily growth factor Germany-SK Potsdam deaths daily growth factor (rolling mean) Germany-SK Potsdam 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 Potsdam doubling time cases (rolling mean) 0.0 183.2 366.3 549.5 daily change Germany-SK Potsdam new cases (rolling 7d mean) Germany-SK Potsdam new cases 0.000 0.366 0.733 daily change Germany-SK Potsdam new deaths (rolling 7d mean) Germany-SK Potsdam new deaths 0.000 0.316 0.631 0.947
In [4]:
overview(country="Germany", subregion="SK Potsdam");
2023-03-07T12:28:54.845613 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) 55.7 SK Potsdam, 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 5 10 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on cases) Germany-SK Potsdam cases daily growth factor Germany-SK Potsdam cases daily growth factor (rolling mean) Germany-SK Potsdam 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 Potsdam deaths daily growth factor Germany-SK Potsdam deaths daily growth factor (rolling mean) Germany-SK Potsdam estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 20000 40000 cases doubling time [days] Germany-SK Potsdam doubling time cases (rolling mean) Germany-SK Potsdam doubling time deaths (rolling mean) 0.0 183.2 366.3 549.5 732.6 daily change Germany-SK Potsdam new cases (rolling 7d mean) Germany-SK Potsdam new cases 0.00 9.16 18.32 daily change Germany-SK Potsdam new deaths (rolling 7d mean) Germany-SK Potsdam new deaths 0.0 127.0 253.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Potsdam", dates="2020-03-15:");
2023-03-07T12:31:14.495760 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 Potsdam SK Potsdam 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 Potsdam Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Potsdam")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Potsdam")
print(f'Population of country="Germany", subregion="SK Potsdam": {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 Potsdam": 183154 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 80734 24 288 0
2023-03-03 80710 6 288 0
2023-03-02 80704 6 288 0
2023-03-01 80698 48 288 0
2023-02-28 80650 18 288 0
2023-02-27 80632 26 288 0
2023-02-26 80606 6 288 0
2023-02-25 80600 10 288 0
2023-02-24 80590 31 288 0
2023-02-23 80559 33 288 0
2023-02-22 80526 47 288 0
2023-02-21 80479 36 288 0
2023-02-20 80443 24 288 0
2023-02-19 80419 13 288 0
2023-02-18 80406 7 288 0
2023-02-17 80399 20 288 0
2023-02-16 80379 698 288 0
2023-02-15 79681 71 288 0
2023-02-14 79610 93 288 1
2023-02-13 79517 24 287 0
2023-02-12 79493 8 287 0
2023-02-11 79485 25 287 0
2023-02-10 79460 36 287 0
2023-02-09 79424 23 287 0
2023-02-08 79401 28 287 0
2023-02-07 79373 33 287 0
2023-02-06 79340 21 287 0
2023-02-05 79319 7 287 0
2023-02-04 79312 8 287 0
2023-02-03 79304 32 287 0
2023-02-02 79272 21 287 0
2023-02-01 79251 20 287 0
2023-01-29 79231 1 287 0
2023-01-23 79230 1 287 0
2023-01-19 79229 1 287 0
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2023-01-03 79219 145 287 1
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2020-03-25 58 10 2 2
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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: 0:03:05.771976