Germany: LK Bad Tölz-Wolfratshausen (Bayern)¶

  • 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 10:33:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bad Tölz-Wolfratshausen", weeks=5);
2023-03-07T10:33:49.091168 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 47.7 LK Bad Tölz-Wolfratshausen, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 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 Bad Tölz-Wolfratshausen cases daily growth factor Germany-LK Bad Tölz-Wolfratshausen cases daily growth factor (rolling mean) Germany-LK Bad Tölz-Wolfratshausen 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 Bad Tölz-Wolfratshausen deaths daily growth factor Germany-LK Bad Tölz-Wolfratshausen deaths daily growth factor (rolling mean) Germany-LK Bad Tölz-Wolfratshausen 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 Bad Tölz-Wolfratshausen doubling time cases (rolling mean) 0.00 6.40 12.79 19.19 25.58 daily change Germany-LK Bad Tölz-Wolfratshausen new cases (rolling 7d mean) Germany-LK Bad Tölz-Wolfratshausen new cases 0.000 0.256 0.512 0.768 daily change Germany-LK Bad Tölz-Wolfratshausen new deaths (rolling 7d mean) Germany-LK Bad Tölz-Wolfratshausen new deaths 0.000 0.262 0.523 0.785
In [4]:
overview(country="Germany", subregion="LK Bad Tölz-Wolfratshausen");
2023-03-07T10:34:06.816523 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) 47.7 LK Bad Tölz-Wolfratshausen, 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 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 Bad Tölz-Wolfratshausen cases daily growth factor Germany-LK Bad Tölz-Wolfratshausen cases daily growth factor (rolling mean) Germany-LK Bad Tölz-Wolfratshausen 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 Bad Tölz-Wolfratshausen deaths daily growth factor Germany-LK Bad Tölz-Wolfratshausen deaths daily growth factor (rolling mean) Germany-LK Bad Tölz-Wolfratshausen 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 Bad Tölz-Wolfratshausen doubling time cases (rolling mean) Germany-LK Bad Tölz-Wolfratshausen doubling time deaths (rolling mean) 0.0 255.8 511.7 daily change Germany-LK Bad Tölz-Wolfratshausen new cases (rolling 7d mean) Germany-LK Bad Tölz-Wolfratshausen new cases 0.000 2.558 5.117 daily change Germany-LK Bad Tölz-Wolfratshausen new deaths (rolling 7d mean) Germany-LK Bad Tölz-Wolfratshausen new deaths 0.0 42.2 84.4 126.6 168.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bad Tölz-Wolfratshausen", dates="2020-03-15:");
2023-03-07T10:36:54.076808 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 Bad Tölz-Wolfratshausen LK Bad Tölz-Wolfratshausen 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 Bad Tölz-Wolfratshausen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bad Tölz-Wolfratshausen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bad Tölz-Wolfratshausen")
print(f'Population of country="Germany", subregion="LK Bad Tölz-Wolfratshausen": {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 Bad Tölz-Wolfratshausen": 127919 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 61927 7 203 0
2023-03-04 61920 2 203 0
2023-03-03 61918 10 203 0
2023-03-02 61908 15 203 0
2023-03-01 61893 7 203 0
2023-02-28 61886 20 203 0
2023-02-27 61866 10 203 0
2023-02-26 61856 3 203 0
2023-02-25 61853 6 203 0
2023-02-24 61847 3 203 0
2023-02-23 61844 16 203 1
2023-02-22 61828 14 202 0
2023-02-21 61814 27 202 0
2023-02-20 61787 9 202 0
2023-02-19 61778 7 202 1
2023-02-18 61771 4 201 0
2023-02-17 61767 12 201 0
2023-02-16 61755 6 201 0
2023-02-15 61749 25 201 0
2023-02-14 61724 25 201 0
2023-02-13 61699 14 201 0
2023-02-12 61685 3 201 0
2023-02-11 61682 4 201 1
2023-02-10 61678 9 200 0
2023-02-09 61669 23 200 0
2023-02-08 61646 9 200 0
2023-02-07 61637 21 200 0
2023-02-06 61616 8 200 0
2023-02-05 61608 1 200 0
2023-02-04 61607 1 200 0
2023-02-03 61606 12 200 0
2023-02-02 61594 11 200 0
2023-02-01 61583 15 200 0
2023-01-31 61568 11 200 0
2023-01-30 61557 9 200 0
2023-01-28 61548 2 200 0
2023-01-27 61546 12 200 0
2023-01-26 61534 8 200 0
2023-01-25 61526 18 200 0
2023-01-24 61508 7 200 0
2023-01-23 61501 17 200 0
2023-01-22 61484 5 200 0
2023-01-21 61479 8 200 0
2023-01-20 61471 9 200 1
2023-01-19 61462 7 199 0
2023-01-18 61455 6 199 0
2023-01-17 61449 13 199 0
2023-01-16 61436 6 199 0
2023-01-15 61430 1 199 0
2023-01-14 61429 6 199 0
2023-01-13 61423 8 199 0
2023-01-12 61415 11 199 1
2023-01-11 61404 14 198 0
2023-01-10 61390 12 198 0
2023-01-09 61378 15 198 0
2023-01-08 61363 1 198 0
2023-01-07 61362 2 198 0
2023-01-06 61360 10 198 0
2023-01-05 61350 28 198 0
2023-01-04 61322 16 198 0
2023-01-03 61306 14 198 0
2023-01-02 61292 27 198 0
2023-01-01 61265 3 198 0
2022-12-31 61262 3 198 0
2022-12-30 61259 8 198 0
2022-12-29 61251 16 198 1
2022-12-28 61235 20 197 0
2022-12-27 61215 25 197 0
2022-12-26 61190 11 197 0
2022-12-25 61179 4 197 0
2022-12-24 61175 7 197 0
2022-12-23 61168 18 197 0
2022-12-22 61150 35 197 0
2022-12-21 61115 22 197 0
2022-12-20 61093 36 197 0
2022-12-19 61057 19 197 0
2022-12-18 61038 2 197 0
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2022-12-03 60819 8 197 0
2022-12-02 60811 13 197 0
2022-12-01 60798 18 197 0
2022-11-30 60780 22 197 0
2022-11-29 60758 33 197 0
2022-11-28 60725 28 197 0
2022-11-27 60697 3 197 0
2022-11-26 60694 6 197 0
2022-11-25 60688 14 197 0
2022-11-24 60674 22 197 0
2022-11-23 60652 23 197 0
2022-11-22 60629 25 197 0
2022-11-21 60604 17 197 1
2022-11-20 60587 3 196 0
2022-11-19 60584 4 196 0
2022-11-18 60580 12 196 0
2022-11-17 60568 17 196 0
2022-11-16 60551 8 196 0
2022-11-15 60543 32 196 0
2022-11-14 60511 27 196 1
2022-11-13 60484 4 195 0
2022-11-12 60480 6 195 0
2022-11-11 60474 17 195 0
2022-11-10 60457 32 195 0
2022-11-09 60425 15 195 0
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2022-11-07 60379 34 195 0
2022-11-06 60345 4 195 0
2022-11-05 60341 5 195 0
2022-11-04 60336 19 195 0
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2022-10-26 60136 28 194 0
2022-10-25 60108 53 194 0
2022-10-24 60055 65 194 0
2022-10-23 59990 9 194 0
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2022-10-21 59965 52 194 1
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2022-10-07 58516 206 192 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: 0:03:38.357639