Germany: LK Oberspreewald-Lausitz (Brandenburg)¶

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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 12:13:25 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Oberspreewald-Lausitz", weeks=5);
2023-03-07T12:13:37.446820 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 7-day incidence rate (per 100K people) 32.5 LK Oberspreewald-Lausitz, 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 0.2 0.4 0.6 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Oberspreewald-Lausitz cases daily growth factor Germany-LK Oberspreewald-Lausitz cases daily growth factor (rolling mean) Germany-LK Oberspreewald-Lausitz estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Oberspreewald-Lausitz deaths daily growth factor Germany-LK Oberspreewald-Lausitz deaths daily growth factor (rolling mean) Germany-LK Oberspreewald-Lausitz estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Oberspreewald-Lausitz doubling time cases (rolling mean) 0.00 5.38 10.76 16.13 21.51 daily change Germany-LK Oberspreewald-Lausitz new cases (rolling 7d mean) Germany-LK Oberspreewald-Lausitz new cases 0.000 0.215 0.430 0.645 0.860 daily change Germany-LK Oberspreewald-Lausitz new deaths (rolling 7d mean) Germany-LK Oberspreewald-Lausitz new deaths 0.00 0.21 0.42 0.63 0.84
In [4]:
overview(country="Germany", subregion="LK Oberspreewald-Lausitz");
2023-03-07T12:14:03.260735 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) 32.5 LK Oberspreewald-Lausitz, 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 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 2.5 5.0 7.5 10.0 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Oberspreewald-Lausitz cases daily growth factor Germany-LK Oberspreewald-Lausitz cases daily growth factor (rolling mean) Germany-LK Oberspreewald-Lausitz estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Oberspreewald-Lausitz deaths daily growth factor Germany-LK Oberspreewald-Lausitz deaths daily growth factor (rolling mean) Germany-LK Oberspreewald-Lausitz 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-LK Oberspreewald-Lausitz doubling time cases (rolling mean) Germany-LK Oberspreewald-Lausitz doubling time deaths (rolling mean) 0.0 215.1 430.2 daily change Germany-LK Oberspreewald-Lausitz new cases (rolling 7d mean) Germany-LK Oberspreewald-Lausitz new cases 0.00 2.69 5.38 8.07 10.76 daily change Germany-LK Oberspreewald-Lausitz new deaths (rolling 7d mean) Germany-LK Oberspreewald-Lausitz new deaths 0.0 64.8 129.5 194.3 259.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Oberspreewald-Lausitz", dates="2020-03-15:");
2023-03-07T12:16:59.470533 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: LK Oberspreewald-Lausitz LK Oberspreewald-Lausitz 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 LK Oberspreewald-Lausitz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Oberspreewald-Lausitz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Oberspreewald-Lausitz")
print(f'Population of country="Germany", subregion="LK Oberspreewald-Lausitz": {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="LK Oberspreewald-Lausitz": 107558 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 52869 7 429 0
2023-03-05 52862 1 429 0
2023-03-04 52861 2 429 0
2023-03-03 52859 7 429 0
2023-03-02 52852 1 429 0
2023-03-01 52851 6 429 0
2023-02-28 52845 11 429 0
2023-02-27 52834 24 429 0
2023-02-26 52810 2 429 0
2023-02-25 52808 10 429 0
2023-02-24 52798 12 429 0
2023-02-23 52786 12 429 0
2023-02-22 52774 9 429 0
2023-02-21 52765 25 429 0
2023-02-20 52740 12 429 0
2023-02-19 52728 1 429 0
2023-02-18 52727 4 429 0
2023-02-17 52723 8 429 0
2023-02-16 52715 12 429 0
2023-02-15 52703 15 429 0
2023-02-14 52688 13 429 0
2023-02-13 52675 17 429 0
2023-02-12 52658 1 429 0
2023-02-11 52657 6 429 0
2023-02-10 52651 13 429 0
2023-02-09 52638 8 429 1
2023-02-08 52630 14 428 0
2023-02-07 52616 15 428 0
2023-02-06 52601 12 428 0
2023-02-05 52589 1 428 0
2023-02-04 52588 4 428 1
2023-02-03 52584 15 427 0
2023-02-02 52569 11 427 0
2023-02-01 52558 16 427 0
2023-01-31 52542 19 427 0
2023-01-30 52523 11 427 0
2023-01-28 52512 1 427 0
2023-01-27 52511 15 427 0
2023-01-26 52496 8 427 0
2023-01-25 52488 16 427 0
2023-01-24 52472 14 427 0
2023-01-23 52458 18 427 0
2023-01-22 52440 1 427 0
2023-01-21 52439 4 427 0
2023-01-20 52435 10 427 0
2023-01-19 52425 6 427 0
2023-01-18 52419 15 427 0
2023-01-17 52404 23 427 0
2023-01-16 52381 15 427 0
2023-01-15 52366 1 427 0
2023-01-14 52365 5 427 0
2023-01-13 52360 16 427 0
2023-01-12 52344 16 427 0
2023-01-11 52328 26 427 0
2023-01-10 52302 26 427 0
2023-01-09 52276 32 427 0
2023-01-08 52244 5 427 0
2023-01-07 52239 15 427 1
2023-01-06 52224 15 426 0
2023-01-05 52209 13 426 0
2023-01-04 52196 39 426 0
2023-01-03 52157 53 426 0
2023-01-02 52104 24 426 2
2023-01-01 52080 2 424 0
2022-12-31 52078 6 424 0
2022-12-30 52072 25 424 1
2022-12-29 52047 28 423 0
2022-12-28 52019 45 423 1
2022-12-27 51974 53 422 2
2022-12-26 51921 15 420 0
2022-12-25 51906 3 420 0
2022-12-24 51903 23 420 0
2022-12-23 51880 28 420 0
2022-12-22 51852 28 420 1
2022-12-21 51824 28 419 0
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2022-12-18 51710 9 418 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: 0:03:47.716667