Germany: SK Halle (Sachsen-Anhalt)¶

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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 16:02:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Halle", weeks=5);
2023-03-07T16:02:19.661019 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 90 90 7-day incidence rate (per 100K people) 61.3 SK Halle, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 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 0.4 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 Halle cases daily growth factor Germany-SK Halle cases daily growth factor (rolling mean) Germany-SK Halle 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-SK Halle deaths daily growth factor Germany-SK Halle deaths daily growth factor (rolling mean) Germany-SK Halle 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 Halle doubling time cases (rolling mean) 0.00 23.81 47.61 daily change Germany-SK Halle new cases (rolling 7d mean) Germany-SK Halle new cases 0.000 0.238 0.476 0.714 0.952 daily change Germany-SK Halle new deaths (rolling 7d mean) Germany-SK Halle new deaths 0.000 0.323 0.645 0.968
In [4]:
overview(country="Germany", subregion="SK Halle");
2023-03-07T16:02:33.723183 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 7-day incidence rate (per 100K people) 61.3 SK Halle, 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 2 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 Halle cases daily growth factor Germany-SK Halle cases daily growth factor (rolling mean) Germany-SK Halle 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-SK Halle deaths daily growth factor Germany-SK Halle deaths daily growth factor (rolling mean) Germany-SK Halle estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-SK Halle doubling time cases (rolling mean) Germany-SK Halle doubling time deaths (rolling mean) 0 238 476 714 952 daily change Germany-SK Halle new cases (rolling 7d mean) Germany-SK Halle new cases 0.00 4.76 9.52 daily change Germany-SK Halle new deaths (rolling 7d mean) Germany-SK Halle new deaths 0 338 675 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Halle", dates="2020-03-15:");
2023-03-07T16:04:39.974008 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 Halle SK Halle 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 Halle Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Halle")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Halle")
print(f'Population of country="Germany", subregion="SK Halle": {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 Halle": 238061 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 108298 26 700 0
2023-03-03 108272 15 700 0
2023-03-02 108257 12 700 0
2023-03-01 108245 26 700 0
2023-02-28 108219 67 700 0
2023-02-27 108152 51 700 0
2023-02-24 108101 30 700 0
2023-02-23 108071 34 700 0
2023-02-22 108037 42 700 0
2023-02-21 107995 54 700 0
2023-02-20 107941 50 700 0
2023-02-17 107891 31 700 0
2023-02-16 107860 33 700 0
2023-02-15 107827 43 700 0
2023-02-14 107784 59 700 0
2023-02-13 107725 32 700 0
2023-02-10 107693 22 700 0
2023-02-09 107671 26 700 1
2023-02-08 107645 24 699 0
2023-02-07 107621 39 699 0
2023-02-06 107582 38 699 0
2023-02-03 107544 15 699 0
2023-02-02 107529 39 699 1
2023-02-01 107490 29 698 0
2023-01-31 107461 58 698 0
2023-01-30 107403 42 698 0
2023-01-27 107361 16 698 0
2023-01-26 107345 31 698 0
2023-01-25 107314 24 698 0
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2023-01-23 107260 43 698 0
2023-01-20 107217 18 698 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: 0:02:52.891269