Germany: LK Sigmaringen (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 10:11:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Sigmaringen", weeks=5);
2023-03-07T10:11:56.179943 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 7-day incidence rate (per 100K people) 87.3 LK Sigmaringen, 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 0.0 0.2 0.4 0.6 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-LK Sigmaringen cases daily growth factor Germany-LK Sigmaringen cases daily growth factor (rolling mean) Germany-LK Sigmaringen 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-LK Sigmaringen deaths daily growth factor Germany-LK Sigmaringen deaths daily growth factor (rolling mean) Germany-LK Sigmaringen 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-LK Sigmaringen doubling time cases (rolling mean) 0.00 26.34 52.69 daily change Germany-LK Sigmaringen new cases (rolling 7d mean) Germany-LK Sigmaringen new cases 0.000 0.263 0.527 0.790 daily change Germany-LK Sigmaringen new deaths (rolling 7d mean) Germany-LK Sigmaringen new deaths 0.000 0.203 0.406 0.609 0.813
In [4]:
overview(country="Germany", subregion="LK Sigmaringen");
2023-03-07T10:12:17.281879 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 3000 3000 7-day incidence rate (per 100K people) 87.3 LK Sigmaringen, 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 0 200 400 600 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-LK Sigmaringen cases daily growth factor Germany-LK Sigmaringen cases daily growth factor (rolling mean) Germany-LK Sigmaringen 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-LK Sigmaringen deaths daily growth factor Germany-LK Sigmaringen deaths daily growth factor (rolling mean) Germany-LK Sigmaringen 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 6000 8000 cases doubling time [days] Germany-LK Sigmaringen doubling time cases (rolling mean) Germany-LK Sigmaringen doubling time deaths (rolling mean) 0 263 527 790 daily change Germany-LK Sigmaringen new cases (rolling 7d mean) Germany-LK Sigmaringen new cases 0.000 2.635 5.269 daily change Germany-LK Sigmaringen new deaths (rolling 7d mean) Germany-LK Sigmaringen new deaths 0.0 31.4 62.7 94.1 125.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Sigmaringen", dates="2020-03-15:");
2023-03-07T10:14:25.999151 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 Sigmaringen LK Sigmaringen 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 Sigmaringen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Sigmaringen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Sigmaringen")
print(f'Population of country="Germany", subregion="LK Sigmaringen": {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 Sigmaringen": 131725 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 67477 21 199 0
2023-03-03 67456 6 199 0
2023-03-02 67450 11 199 0
2023-03-01 67439 28 199 0
2023-02-28 67411 49 199 0
2023-02-27 67362 79 199 0
2023-02-24 67283 38 199 1
2023-02-23 67245 27 198 0
2023-02-22 67218 30 198 0
2023-02-21 67188 29 198 0
2023-02-20 67159 53 198 0
2023-02-17 67106 30 198 0
2023-02-16 67076 16 198 0
2023-02-15 67060 37 198 0
2023-02-14 67023 46 198 0
2023-02-13 66977 46 198 1
2023-02-10 66931 30 197 0
2023-02-09 66901 34 197 0
2023-02-08 66867 24 197 0
2023-02-07 66843 39 197 0
2023-02-06 66804 42 197 0
2023-02-03 66762 20 197 0
2023-02-02 66742 32 197 0
2023-02-01 66710 27 197 0
2023-01-31 66683 15 197 0
2023-01-30 66668 28 197 0
2023-01-27 66640 13 197 0
2023-01-26 66627 9 197 0
2023-01-25 66618 5 197 0
2023-01-24 66613 13 197 0
2023-01-23 66600 19 197 0
2023-01-20 66581 9 197 0
2023-01-19 66572 14 197 0
2023-01-18 66558 11 197 1
2023-01-17 66547 19 196 0
2023-01-16 66528 15 196 1
2023-01-13 66513 13 195 0
2023-01-12 66500 11 195 0
2023-01-11 66489 15 195 0
2023-01-10 66474 18 195 0
2023-01-09 66456 39 195 0
2023-01-05 66417 14 195 0
2023-01-04 66403 21 195 0
2023-01-03 66382 27 195 1
2023-01-02 66355 26 194 0
2022-12-30 66329 11 194 0
2022-12-29 66318 34 194 0
2022-12-28 66284 24 194 1
2022-12-27 66260 55 193 1
2022-12-26 66205 1 192 0
2022-12-24 66204 1 192 0
2022-12-23 66203 15 192 0
2022-12-22 66188 26 192 0
2022-12-21 66162 28 192 0
2022-12-20 66134 33 192 0
2022-12-19 66101 44 192 0
2022-12-16 66057 26 192 0
2022-12-15 66031 31 192 0
2022-12-14 66000 28 192 1
2022-12-13 65972 32 191 0
2022-12-12 65940 30 191 0
2022-12-09 65910 16 191 0
2022-12-08 65894 16 191 0
2022-12-07 65878 38 191 0
2022-12-06 65840 28 191 0
2022-12-05 65812 50 191 0
2022-12-02 65762 25 191 0
2022-12-01 65737 37 191 0
2022-11-30 65700 46 191 0
2022-11-29 65654 43 191 0
2022-11-28 65611 53 191 1
2022-11-25 65558 20 190 0
2022-11-24 65538 33 190 0
2022-11-23 65505 30 190 0
2022-11-22 65475 40 190 0
2022-11-21 65435 61 190 0
2022-11-18 65374 22 190 0
2022-11-17 65352 21 190 1
2022-11-16 65331 64 189 0
2022-11-15 65267 43 189 0
2022-11-14 65224 69 189 0
2022-11-11 65155 26 189 0
2022-11-10 65129 34 189 0
2022-11-09 65095 29 189 0
2022-11-08 65066 71 189 0
2022-11-07 64995 72 189 1
2022-11-04 64923 46 188 1
2022-11-03 64877 75 187 0
2022-11-02 64802 168 187 1
2022-10-28 64634 71 186 0
2022-10-27 64563 69 186 0
2022-10-26 64494 94 186 0
2022-10-25 64400 119 186 0
2022-10-24 64281 196 186 1
2022-10-21 64085 103 185 0
2022-10-20 63982 134 185 0
2022-10-19 63848 188 185 0
2022-10-18 63660 225 185 0
2022-10-17 63435 303 185 1
2022-10-14 63132 147 184 0
2022-10-13 62985 190 184 0
2022-10-12 62795 231 184 0
2022-10-11 62564 410 184 1
2022-10-10 62154 402 183 0
2022-10-07 61752 216 183 0
2022-10-06 61536 244 183 0
2022-10-05 61292 265 183 0
2022-10-04 61027 144 183 0
2022-10-02 60883 1 183 0
2022-09-30 60882 134 183 0
2022-09-29 60748 163 183 0
2022-09-28 60585 150 183 1
2022-09-27 60435 163 182 0
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2022-09-24 60150 46 181 0
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2022-09-22 60043 77 181 0
2022-09-21 59966 108 181 0
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2022-09-19 59772 98 181 0
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2022-09-15 59640 41 181 0
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2022-09-13 59536 51 181 0
2022-09-12 59485 130 181 1
2022-09-09 59355 43 180 0
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2022-09-07 59267 38 180 0
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2022-08-30 58926 38 180 0
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2022-08-11 58255 66 180 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: 0:03:09.287054