Germany: SK Magdeburg (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 15:45:57 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Magdeburg", weeks=5);
2023-03-07T15:46:13.329380 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 7-day incidence rate (per 100K people) 43.6 SK Magdeburg, 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 0.1 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 Magdeburg cases daily growth factor Germany-SK Magdeburg cases daily growth factor (rolling mean) Germany-SK Magdeburg 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 Magdeburg deaths daily growth factor Germany-SK Magdeburg deaths daily growth factor (rolling mean) Germany-SK Magdeburg 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 Magdeburg doubling time cases (rolling mean) 0.00 11.81 23.62 35.43 daily change Germany-SK Magdeburg new cases (rolling 7d mean) Germany-SK Magdeburg new cases 0.000 0.236 0.472 0.709 0.945 daily change Germany-SK Magdeburg new deaths (rolling 7d mean) Germany-SK Magdeburg new deaths 0.000 0.284 0.567 0.851
In [4]:
overview(country="Germany", subregion="SK Magdeburg");
2023-03-07T15:46:32.572231 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) 43.6 SK Magdeburg, 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 1 2 3 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 Magdeburg cases daily growth factor Germany-SK Magdeburg cases daily growth factor (rolling mean) Germany-SK Magdeburg 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 Magdeburg deaths daily growth factor Germany-SK Magdeburg deaths daily growth factor (rolling mean) Germany-SK Magdeburg 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 Magdeburg doubling time cases (rolling mean) Germany-SK Magdeburg doubling time deaths (rolling mean) 0 472 945 daily change Germany-SK Magdeburg new cases (rolling 7d mean) Germany-SK Magdeburg new cases 0.000 2.362 4.724 7.086 daily change Germany-SK Magdeburg new deaths (rolling 7d mean) Germany-SK Magdeburg new deaths 0.0 57.5 114.9 172.4 229.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Magdeburg", dates="2020-03-15:");
2023-03-07T15:49:07.852823 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 Magdeburg SK Magdeburg 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 Magdeburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Magdeburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Magdeburg")
print(f'Population of country="Germany", subregion="SK Magdeburg": {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 Magdeburg": 236188 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 88381 19 354 0
2023-03-03 88362 18 354 0
2023-03-02 88344 10 354 0
2023-03-01 88334 32 354 0
2023-02-28 88302 24 354 0
2023-02-27 88278 37 354 0
2023-02-24 88241 14 354 0
2023-02-23 88227 26 354 0
2023-02-22 88201 15 354 0
2023-02-21 88186 44 354 0
2023-02-20 88142 38 354 1
2023-02-17 88104 6 353 0
2023-02-16 88098 30 353 0
2023-02-15 88068 39 353 0
2023-02-14 88029 35 353 0
2023-02-13 87994 30 353 0
2023-02-10 87964 23 353 0
2023-02-09 87941 25 353 0
2023-02-08 87916 19 353 0
2023-02-07 87897 30 353 0
2023-02-06 87867 28 353 0
2023-02-03 87839 12 353 0
2023-02-02 87827 16 353 0
2023-02-01 87811 15 353 0
2023-01-31 87796 38 353 0
2023-01-30 87758 30 353 0
2023-01-27 87728 19 353 0
2023-01-26 87709 26 353 0
2023-01-25 87683 32 353 0
2023-01-24 87651 7 353 0
2023-01-23 87644 24 353 0
2023-01-20 87620 16 353 0
2023-01-19 87604 17 353 0
2023-01-18 87587 25 353 0
2023-01-17 87562 19 353 0
2023-01-16 87543 24 353 0
2023-01-13 87519 15 353 0
2023-01-12 87504 26 353 0
2023-01-11 87478 18 353 0
2023-01-10 87460 53 353 0
2023-01-09 87407 59 353 0
2023-01-05 87348 40 353 0
2023-01-04 87308 81 353 0
2023-01-03 87227 14 353 0
2023-01-02 87213 58 353 0
2022-12-30 87155 55 353 0
2022-12-29 87100 55 353 0
2022-12-28 87045 49 353 0
2022-12-27 86996 96 353 0
2022-12-23 86900 53 353 1
2022-12-22 86847 62 352 1
2022-12-21 86785 81 351 0
2022-12-20 86704 87 351 0
2022-12-19 86617 88 351 1
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2022-11-29 85525 75 344 1
2022-11-28 85450 81 343 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: 0:03:20.372962