Germany: SK Ulm (Baden-Württemberg)¶

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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 10:26:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Ulm", weeks=5);
2023-03-07T10:26:20.915352 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 93.7 SK Ulm, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Ulm new deaths (rolling 7d mean) Germany-SK Ulm new deaths 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 Ulm cases daily growth factor Germany-SK Ulm cases daily growth factor (rolling mean) Germany-SK Ulm estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Ulm deaths daily growth factor Germany-SK Ulm deaths daily growth factor (rolling mean) Germany-SK Ulm estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Ulm doubling time cases (rolling mean) 0.00 25.39 50.78 daily change Germany-SK Ulm new cases (rolling 7d mean) Germany-SK Ulm new cases 0.000 0.247 0.495 0.742 0.989
In [4]:
overview(country="Germany", subregion="SK Ulm");
2023-03-07T10:26:39.667484 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) 93.7 SK Ulm, 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 0.5 1.0 1.5 2.0 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Ulm cases daily growth factor Germany-SK Ulm cases daily growth factor (rolling mean) Germany-SK Ulm estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Ulm deaths daily growth factor Germany-SK Ulm deaths daily growth factor (rolling mean) Germany-SK Ulm estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 cases doubling time [days] Germany-SK Ulm doubling time cases (rolling mean) Germany-SK Ulm doubling time deaths (rolling mean) 0.0 253.9 507.8 daily change Germany-SK Ulm new cases (rolling 7d mean) Germany-SK Ulm new cases 0.000 0.635 1.269 1.904 2.539 daily change Germany-SK Ulm new deaths (rolling 7d mean) Germany-SK Ulm new deaths 0.0 47.1 94.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Ulm", dates="2020-03-15:");
2023-03-07T10:28:42.480240 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 Ulm SK Ulm 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 Ulm Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Ulm")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Ulm")
print(f'Population of country="Germany", subregion="SK Ulm": {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 Ulm": 126949 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 60263 23 176 0
2023-03-03 60240 14 176 0
2023-03-02 60226 12 176 0
2023-03-01 60214 34 176 0
2023-02-28 60180 36 176 0
2023-02-27 60144 51 176 0
2023-02-24 60093 23 176 0
2023-02-23 60070 39 176 0
2023-02-22 60031 30 176 0
2023-02-21 60001 46 176 0
2023-02-20 59955 68 176 0
2023-02-17 59887 36 176 0
2023-02-16 59851 37 176 0
2023-02-15 59814 39 176 0
2023-02-14 59775 42 176 0
2023-02-13 59733 54 176 0
2023-02-10 59679 23 176 0
2023-02-09 59656 31 176 0
2023-02-08 59625 30 176 0
2023-02-07 59595 47 176 0
2023-02-06 59548 46 176 0
2023-02-04 59502 1 176 0
2023-02-03 59501 19 176 0
2023-02-02 59482 24 176 0
2023-02-01 59458 32 176 0
2023-01-31 59426 23 176 0
2023-01-30 59403 22 176 0
2023-01-27 59381 20 176 0
2023-01-26 59361 15 176 0
2023-01-25 59346 33 176 0
2023-01-24 59313 33 176 0
2023-01-23 59280 53 176 0
2023-01-20 59227 10 176 0
2023-01-19 59217 14 176 0
2023-01-18 59203 14 176 0
2023-01-17 59189 25 176 0
2023-01-16 59164 31 176 0
2023-01-13 59133 8 176 0
2023-01-12 59125 26 176 0
2023-01-11 59099 34 176 0
2023-01-10 59065 34 176 0
2023-01-09 59031 32 176 0
2023-01-05 58999 17 176 0
2023-01-04 58982 17 176 0
2023-01-03 58965 38 176 0
2023-01-02 58927 46 176 0
2022-12-30 58881 21 176 0
2022-12-29 58860 30 176 0
2022-12-28 58830 31 176 0
2022-12-27 58799 103 176 0
2022-12-23 58696 42 176 0
2022-12-22 58654 40 176 1
2022-12-21 58614 37 175 0
2022-12-20 58577 42 175 0
2022-12-19 58535 64 175 0
2022-12-16 58471 23 175 0
2022-12-15 58448 42 175 0
2022-12-14 58406 56 175 1
2022-12-13 58350 41 174 1
2022-12-12 58309 45 173 0
2022-12-09 58264 30 173 0
2022-12-08 58234 43 173 2
2022-12-07 58191 52 171 0
2022-12-06 58139 103 171 1
2022-12-05 58036 63 170 0
2022-12-02 57973 22 170 0
2022-12-01 57951 28 170 0
2022-11-30 57923 27 170 0
2022-11-29 57896 30 170 0
2022-11-28 57866 32 170 1
2022-11-25 57834 28 169 0
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2022-10-29 56983 7 165 0
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2022-10-27 56910 85 165 1
2022-10-26 56825 61 164 0
2022-10-25 56764 163 164 0
2022-10-24 56601 151 164 0
2022-10-21 56450 104 164 0
2022-10-20 56346 98 164 0
2022-10-19 56248 176 164 0
2022-10-18 56072 176 164 2
2022-10-17 55896 243 162 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: 0:03:15.537917