Germany: LK Waldshut (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 10:19:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Waldshut", weeks=5);
2023-03-07T10:19:31.124558 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 40.9 LK Waldshut, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on cases) Germany-LK Waldshut cases daily growth factor Germany-LK Waldshut cases daily growth factor (rolling mean) Germany-LK Waldshut estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Waldshut deaths daily growth factor Germany-LK Waldshut deaths daily growth factor (rolling mean) Germany-LK Waldshut estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Waldshut doubling time cases (rolling mean) Germany-LK Waldshut doubling time deaths (rolling mean) 0.00 17.12 34.25 daily change Germany-LK Waldshut new cases (rolling 7d mean) Germany-LK Waldshut new cases 0.000 0.428 0.856 1.284 1.712 daily change Germany-LK Waldshut new deaths (rolling 7d mean) Germany-LK Waldshut new deaths 0.0 92.0 184.1 276.1 368.1 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Waldshut");
2023-03-07T10:19:50.612552 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) 40.9 LK Waldshut, 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 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on cases) Germany-LK Waldshut cases daily growth factor Germany-LK Waldshut cases daily growth factor (rolling mean) Germany-LK Waldshut estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Waldshut deaths daily growth factor Germany-LK Waldshut deaths daily growth factor (rolling mean) Germany-LK Waldshut 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 Waldshut doubling time cases (rolling mean) Germany-LK Waldshut doubling time deaths (rolling mean) 0 342 685 daily change Germany-LK Waldshut new cases (rolling 7d mean) Germany-LK Waldshut new cases 0.000 1.712 3.425 5.137 6.850 daily change Germany-LK Waldshut new deaths (rolling 7d mean) Germany-LK Waldshut new deaths 0.0 197.2 394.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Waldshut", dates="2020-03-15:");
2023-03-07T10:21:56.199368 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 Waldshut LK Waldshut 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 Waldshut Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Waldshut")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Waldshut")
print(f'Population of country="Germany", subregion="LK Waldshut": {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 Waldshut": 171249 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 73443 2 396 0
2023-03-05 73441 3 396 0
2023-03-04 73438 1 396 0
2023-03-03 73437 6 396 0
2023-03-02 73431 10 396 0
2023-03-01 73421 13 396 0
2023-02-28 73408 35 396 0
2023-02-27 73373 31 396 0
2023-02-26 73342 9 396 0
2023-02-25 73333 18 396 1
2023-02-24 73315 18 395 0
2023-02-23 73297 46 395 0
2023-02-22 73251 41 395 0
2023-02-21 73210 41 395 0
2023-02-20 73169 45 395 0
2023-02-19 73124 12 395 0
2023-02-18 73112 6 395 0
2023-02-17 73106 25 395 0
2023-02-16 73081 21 395 0
2023-02-15 73060 19 395 1
2023-02-14 73041 32 394 0
2023-02-13 73009 23 394 2
2023-02-12 72986 8 392 0
2023-02-11 72978 18 392 2
2023-02-10 72960 5 390 0
2023-02-09 72955 17 390 1
2023-02-08 72938 21 389 0
2023-02-07 72917 22 389 0
2023-02-06 72895 21 389 0
2023-02-05 72874 8 389 0
2023-02-04 72866 10 389 0
2023-02-03 72856 10 389 0
2023-02-02 72846 11 389 0
2023-02-01 72835 17 389 0
2023-01-31 72818 13 389 0
2023-01-30 72805 28 389 0
2023-01-29 72777 6 389 0
2023-01-28 72771 4 389 0
2023-01-27 72767 10 389 0
2023-01-26 72757 12 389 0
2023-01-25 72745 6 389 0
2023-01-24 72739 10 389 0
2023-01-23 72729 15 389 1
2023-01-22 72714 7 388 0
2023-01-21 72707 4 388 0
2023-01-20 72703 12 388 0
2023-01-19 72691 9 388 0
2023-01-18 72682 4 388 0
2023-01-17 72678 15 388 0
2023-01-16 72663 22 388 0
2023-01-15 72641 5 388 0
2023-01-14 72636 3 388 0
2023-01-13 72633 9 388 0
2023-01-12 72624 11 388 0
2023-01-11 72613 13 388 0
2023-01-10 72600 17 388 1
2023-01-09 72583 35 387 0
2023-01-08 72548 5 387 0
2023-01-07 72543 5 387 0
2023-01-06 72538 6 387 1
2023-01-05 72532 20 386 2
2023-01-04 72512 17 384 0
2023-01-03 72495 21 384 0
2023-01-02 72474 17 384 0
2023-01-01 72457 20 384 1
2022-12-31 72437 6 383 0
2022-12-30 72431 20 383 0
2022-12-29 72411 25 383 1
2022-12-28 72386 32 382 0
2022-12-27 72354 42 382 0
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2022-11-22 70661 75 363 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: 0:02:49.020484