Germany: LK Augsburg (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 10:33:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Augsburg", weeks=5);
2023-03-07T10:33:50.381409 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 59.0 LK Augsburg, 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 60 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Augsburg cases daily growth factor Germany-LK Augsburg cases daily growth factor (rolling mean) Germany-LK Augsburg 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 Augsburg deaths daily growth factor Germany-LK Augsburg deaths daily growth factor (rolling mean) Germany-LK Augsburg 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 Augsburg doubling time cases (rolling mean) 0.0 51.6 103.1 154.7 daily change Germany-LK Augsburg new cases (rolling 7d mean) Germany-LK Augsburg new cases 0.000 0.516 1.031 1.547 daily change Germany-LK Augsburg new deaths (rolling 7d mean) Germany-LK Augsburg new deaths 0.000 0.257 0.513 0.770
In [4]:
overview(country="Germany", subregion="LK Augsburg");
2023-03-07T10:34:08.646490 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) 59.0 LK Augsburg, 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 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 Augsburg cases daily growth factor Germany-LK Augsburg cases daily growth factor (rolling mean) Germany-LK Augsburg 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 Augsburg deaths daily growth factor Germany-LK Augsburg deaths daily growth factor (rolling mean) Germany-LK Augsburg 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 cases doubling time [days] Germany-LK Augsburg doubling time cases (rolling mean) Germany-LK Augsburg doubling time deaths (rolling mean) 0 258 516 773 1031 daily change Germany-LK Augsburg new cases (rolling 7d mean) Germany-LK Augsburg new cases 0.00 5.16 10.31 daily change Germany-LK Augsburg new deaths (rolling 7d mean) Germany-LK Augsburg new deaths 0.0 132.9 265.8 398.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Augsburg", dates="2020-03-15:");
2023-03-07T10:36:54.073453 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 Augsburg LK Augsburg 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 Augsburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Augsburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Augsburg")
print(f'Population of country="Germany", subregion="LK Augsburg": {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 Augsburg": 257790 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 126159 47 473 0
2023-03-03 126112 6 473 0
2023-03-02 126106 15 473 0
2023-03-01 126091 26 473 0
2023-02-28 126065 58 473 0
2023-02-27 126007 63 473 0
2023-02-26 125944 2 473 0
2023-02-24 125942 33 473 0
2023-02-23 125909 28 473 0
2023-02-22 125881 173 473 2
2023-02-21 125708 2 471 0
2023-02-20 125706 1 471 0
2023-02-19 125705 1 471 0
2023-02-17 125704 26 471 0
2023-02-16 125678 24 471 0
2023-02-15 125654 41 471 0
2023-02-14 125613 65 471 0
2023-02-13 125548 54 471 1
2023-02-10 125494 43 470 0
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2023-02-08 125421 41 470 1
2023-02-07 125380 82 469 0
2023-02-06 125298 87 469 0
2023-02-05 125211 1 469 0
2023-02-04 125210 2 469 0
2023-02-03 125208 33 469 0
2023-02-02 125175 54 469 0
2023-02-01 125121 53 469 0
2023-01-31 125068 75 469 0
2023-01-30 124993 74 469 1
2023-01-27 124919 36 468 0
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2023-01-25 124849 77 468 2
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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: 0:03:38.136425