Germany: LK Schwandorf (Bayern)¶

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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 11:28:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schwandorf", weeks=5);
2023-03-07T11:28:57.144295 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 103.9 LK Schwandorf, 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 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 Schwandorf cases daily growth factor Germany-LK Schwandorf cases daily growth factor (rolling mean) Germany-LK Schwandorf estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 R & growth factor (based on deaths) Germany-LK Schwandorf deaths daily growth factor Germany-LK Schwandorf deaths daily growth factor (rolling mean) Germany-LK Schwandorf estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Schwandorf doubling time cases (rolling mean) 0.00 14.92 29.85 44.77 59.70 daily change Germany-LK Schwandorf new cases (rolling 7d mean) Germany-LK Schwandorf new cases 0.000 0.298 0.597 0.895 daily change Germany-LK Schwandorf new deaths (rolling 7d mean) Germany-LK Schwandorf new deaths 0.000 0.264 0.528 0.792
In [4]:
overview(country="Germany", subregion="LK Schwandorf");
2023-03-07T11:29:16.546912 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) 103.9 LK Schwandorf, 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 1 2 3 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 Schwandorf cases daily growth factor Germany-LK Schwandorf cases daily growth factor (rolling mean) Germany-LK Schwandorf 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 R & growth factor (based on deaths) Germany-LK Schwandorf deaths daily growth factor Germany-LK Schwandorf deaths daily growth factor (rolling mean) Germany-LK Schwandorf 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-LK Schwandorf doubling time cases (rolling mean) Germany-LK Schwandorf doubling time deaths (rolling mean) 0 298 597 895 daily change Germany-LK Schwandorf new cases (rolling 7d mean) Germany-LK Schwandorf new cases 0.000 1.492 2.985 4.477 5.970 daily change Germany-LK Schwandorf new deaths (rolling 7d mean) Germany-LK Schwandorf new deaths 0.0 122.7 245.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schwandorf", dates="2020-03-15:");
2023-03-07T11:31:59.174536 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 Schwandorf LK Schwandorf 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Schwandorf Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schwandorf")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schwandorf")
print(f'Population of country="Germany", subregion="LK Schwandorf": {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 Schwandorf": 149239 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 79090 4 328 1
2023-03-05 79086 5 327 0
2023-03-04 79081 3 327 0
2023-03-03 79078 24 327 0
2023-03-02 79054 40 327 0
2023-03-01 79014 25 327 0
2023-02-28 78989 54 327 0
2023-02-27 78935 48 327 0
2023-02-26 78887 1 327 0
2023-02-25 78886 12 327 0
2023-02-24 78874 42 327 0
2023-02-23 78832 43 327 0
2023-02-22 78789 64 327 0
2023-02-21 78725 30 327 0
2023-02-20 78695 26 327 1
2023-02-19 78669 12 326 0
2023-02-18 78657 11 326 0
2023-02-17 78646 50 326 0
2023-02-16 78596 58 326 1
2023-02-15 78538 32 325 1
2023-02-14 78506 51 324 0
2023-02-13 78455 43 324 0
2023-02-12 78412 4 324 0
2023-02-11 78408 12 324 0
2023-02-10 78396 33 324 0
2023-02-09 78363 40 324 0
2023-02-08 78323 54 324 0
2023-02-07 78269 62 324 1
2023-02-06 78207 30 323 0
2023-02-05 78177 3 323 0
2023-02-04 78174 13 323 1
2023-02-03 78161 24 322 0
2023-02-02 78137 36 322 0
2023-02-01 78101 46 322 1
2023-01-31 78055 54 321 0
2023-01-30 78001 37 321 0
2023-01-29 77964 2 321 0
2023-01-28 77962 3 321 0
2023-01-27 77959 30 321 0
2023-01-26 77929 26 321 0
2023-01-25 77903 48 321 0
2023-01-24 77855 30 321 0
2023-01-23 77825 26 321 0
2023-01-22 77799 1 321 0
2023-01-21 77798 2 321 0
2023-01-20 77796 22 321 0
2023-01-19 77774 22 321 0
2023-01-18 77752 26 321 0
2023-01-17 77726 25 321 0
2023-01-16 77701 22 321 0
2023-01-15 77679 1 321 0
2023-01-14 77678 4 321 0
2023-01-13 77674 16 321 0
2023-01-12 77658 18 321 0
2023-01-11 77640 24 321 0
2023-01-10 77616 37 321 1
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2023-01-06 77543 1 320 0
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2023-01-04 77516 16 320 0
2023-01-03 77500 22 320 0
2023-01-02 77478 16 320 0
2023-01-01 77462 1 320 0
2022-12-31 77461 8 320 0
2022-12-30 77453 20 320 0
2022-12-29 77433 18 320 0
2022-12-28 77415 31 320 0
2022-12-27 77384 28 320 0
2022-12-26 77356 4 320 0
2022-12-25 77352 6 320 2
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2022-11-28 76636 34 310 1
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2022-11-21 76449 31 309 0
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2022-11-19 76414 18 309 1
2022-11-18 76396 35 308 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: 0:03:30.553931