Germany: LK Schwalm-Eder-Kreis (Hessen)¶

  • Homepage of project: https://oscovida.github.io
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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 12:44:41 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schwalm-Eder-Kreis", weeks=5);
2023-03-07T12:44:54.676022 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 52.2 LK Schwalm-Eder-Kreis, 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 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Schwalm-Eder-Kreis cases daily growth factor Germany-LK Schwalm-Eder-Kreis cases daily growth factor (rolling mean) Germany-LK Schwalm-Eder-Kreis 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 Schwalm-Eder-Kreis deaths daily growth factor Germany-LK Schwalm-Eder-Kreis deaths daily growth factor (rolling mean) Germany-LK Schwalm-Eder-Kreis 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-LK Schwalm-Eder-Kreis doubling time cases (rolling mean) 0.00 18.01 36.01 54.02 72.02 daily change Germany-LK Schwalm-Eder-Kreis new cases (rolling 7d mean) Germany-LK Schwalm-Eder-Kreis new cases 0.00 0.36 0.72 daily change Germany-LK Schwalm-Eder-Kreis new deaths (rolling 7d mean) Germany-LK Schwalm-Eder-Kreis new deaths 0.000 0.324 0.648 0.971
In [4]:
overview(country="Germany", subregion="LK Schwalm-Eder-Kreis");
2023-03-07T12:45:10.103925 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 7-day incidence rate (per 100K people) 52.2 LK Schwalm-Eder-Kreis, 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 6 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Schwalm-Eder-Kreis cases daily growth factor Germany-LK Schwalm-Eder-Kreis cases daily growth factor (rolling mean) Germany-LK Schwalm-Eder-Kreis 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 Schwalm-Eder-Kreis deaths daily growth factor Germany-LK Schwalm-Eder-Kreis deaths daily growth factor (rolling mean) Germany-LK Schwalm-Eder-Kreis 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 Schwalm-Eder-Kreis doubling time cases (rolling mean) Germany-LK Schwalm-Eder-Kreis doubling time deaths (rolling mean) 0.0 180.1 360.1 540.2 720.2 daily change Germany-LK Schwalm-Eder-Kreis new cases (rolling 7d mean) Germany-LK Schwalm-Eder-Kreis new cases 0.0 3.6 7.2 10.8 daily change Germany-LK Schwalm-Eder-Kreis new deaths (rolling 7d mean) Germany-LK Schwalm-Eder-Kreis new deaths 0.0 54.0 107.9 161.9 215.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schwalm-Eder-Kreis", dates="2020-03-15:");
2023-03-07T12:47:24.428740 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 Schwalm-Eder-Kreis LK Schwalm-Eder-Kreis 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 Schwalm-Eder-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schwalm-Eder-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schwalm-Eder-Kreis")
print(f'Population of country="Germany", subregion="LK Schwalm-Eder-Kreis": {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 Schwalm-Eder-Kreis": 180052 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 73426 11 294 0
2023-03-04 73415 3 294 0
2023-03-03 73412 8 294 0
2023-03-02 73404 16 294 0
2023-03-01 73388 20 294 0
2023-02-28 73368 36 294 0
2023-02-27 73332 17 294 0
2023-02-25 73315 5 294 0
2023-02-24 73310 20 294 0
2023-02-23 73290 29 294 0
2023-02-22 73261 40 294 0
2023-02-21 73221 79 294 0
2023-02-20 73142 5 294 0
2023-02-17 73137 35 294 0
2023-02-16 73102 28 294 0
2023-02-15 73074 12 294 0
2023-02-14 73062 23 294 0
2023-02-13 73039 8 294 0
2023-02-12 73031 3 294 0
2023-02-11 73028 10 294 0
2023-02-10 73018 21 294 0
2023-02-09 72997 22 294 0
2023-02-08 72975 20 294 0
2023-02-07 72955 39 294 0
2023-02-06 72916 26 294 0
2023-02-03 72890 13 294 0
2023-02-02 72877 16 294 1
2023-02-01 72861 16 293 0
2023-01-31 72845 70 293 0
2023-01-30 72775 3 293 0
2023-01-27 72772 9 293 0
2023-01-26 72763 14 293 0
2023-01-25 72749 11 293 0
2023-01-24 72738 32 293 0
2023-01-23 72706 33 293 0
2023-01-20 72673 8 293 0
2023-01-19 72665 6 293 0
2023-01-18 72659 12 293 0
2023-01-17 72647 40 293 0
2023-01-16 72607 11 293 0
2023-01-13 72596 10 293 0
2023-01-12 72586 26 293 0
2023-01-11 72560 25 293 0
2023-01-10 72535 36 293 0
2023-01-09 72499 60 293 0
2023-01-07 72439 7 293 0
2023-01-06 72432 36 293 0
2023-01-05 72396 31 293 0
2023-01-04 72365 38 293 0
2023-01-03 72327 73 293 0
2023-01-02 72254 66 293 0
2023-01-01 72188 1 293 0
2022-12-30 72187 19 293 1
2022-12-29 72168 39 292 0
2022-12-28 72129 70 292 0
2022-12-27 72059 105 292 0
2022-12-26 71954 2 292 0
2022-12-23 71952 27 292 0
2022-12-22 71925 78 292 0
2022-12-21 71847 103 292 0
2022-12-20 71744 83 292 0
2022-12-19 71661 86 292 0
2022-12-16 71575 16 292 0
2022-12-15 71559 48 292 0
2022-12-14 71511 29 292 0
2022-12-13 71482 65 292 0
2022-12-12 71417 68 292 0
2022-12-11 71349 1 292 0
2022-12-10 71348 3 292 0
2022-12-09 71345 51 292 0
2022-12-08 71294 40 292 0
2022-12-07 71254 70 292 0
2022-12-06 71184 118 292 0
2022-12-05 71066 13 292 0
2022-12-03 71053 1 292 0
2022-12-02 71052 24 292 0
2022-12-01 71028 39 292 0
2022-11-30 70989 49 292 0
2022-11-29 70940 64 292 0
2022-11-28 70876 43 292 0
2022-11-25 70833 24 292 0
2022-11-24 70809 45 292 0
2022-11-23 70764 53 292 0
2022-11-22 70711 80 292 0
2022-11-21 70631 44 292 0
2022-11-18 70587 31 292 0
2022-11-17 70556 46 292 0
2022-11-16 70510 79 292 0
2022-11-15 70431 50 292 0
2022-11-14 70381 64 292 0
2022-11-11 70317 29 292 0
2022-11-10 70288 68 292 0
2022-11-09 70220 61 292 0
2022-11-08 70159 101 292 0
2022-11-07 70058 90 292 0
2022-11-04 69968 50 292 0
2022-11-03 69918 71 292 0
2022-11-02 69847 139 292 0
2022-11-01 69708 102 292 0
2022-10-31 69606 140 292 0
2022-10-29 69466 2 292 0
2022-10-28 69464 113 292 0
2022-10-27 69351 99 292 0
2022-10-26 69252 164 292 0
2022-10-25 69088 189 292 0
2022-10-24 68899 373 292 0
2022-10-22 68526 1 292 0
2022-10-21 68525 57 292 0
2022-10-20 68468 336 292 0
2022-10-19 68132 322 292 0
2022-10-18 67810 220 292 0
2022-10-17 67590 260 292 0
2022-10-16 67330 4 292 0
2022-10-15 67326 2 292 0
2022-10-14 67324 157 292 0
2022-10-13 67167 249 292 0
2022-10-12 66918 279 292 0
2022-10-11 66639 331 292 0
2022-10-10 66308 460 292 1
2022-10-09 65848 4 291 0
2022-10-08 65844 2 291 0
2022-10-07 65842 424 291 0
2022-10-06 65418 321 291 0
2022-10-05 65097 353 291 0
2022-10-04 64744 235 291 0
2022-10-03 64509 4 291 0
2022-10-02 64505 5 291 0
2022-10-01 64500 1 291 0
2022-09-30 64499 89 291 0
2022-09-29 64410 161 291 0
2022-09-28 64249 203 291 0
2022-09-27 64046 198 291 0
2022-09-26 63848 134 291 0
2022-09-25 63714 2 291 0
2022-09-24 63712 5 291 0
2022-09-23 63707 68 291 0
2022-09-22 63639 79 291 0
2022-09-21 63560 100 291 0
2022-09-20 63460 76 291 0
2022-09-19 63384 74 291 0
2022-09-16 63310 63 291 0
2022-09-15 63247 67 291 0
2022-09-14 63180 85 291 0
2022-09-13 63095 109 291 0
2022-09-12 62986 70 291 0
2022-09-10 62916 1 291 0
2022-09-09 62915 74 291 0
2022-09-08 62841 52 291 0
2022-09-07 62789 108 291 0
2022-09-06 62681 84 291 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: 0:03:09.728014