Germany: LK Coburg (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 10:40:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Coburg", weeks=5);
2023-03-07T10:41:04.300471 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) 69.3 LK Coburg, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Coburg cases daily growth factor Germany-LK Coburg cases daily growth factor (rolling mean) Germany-LK Coburg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Coburg deaths daily growth factor Germany-LK Coburg deaths daily growth factor (rolling mean) Germany-LK Coburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Coburg doubling time cases (rolling mean) 0.00 8.65 17.31 25.96 daily change Germany-LK Coburg new cases (rolling 7d mean) Germany-LK Coburg new cases 0.000 0.216 0.433 0.649 0.865 daily change Germany-LK Coburg new deaths (rolling 7d mean) Germany-LK Coburg new deaths 0.000 0.342 0.684
In [4]:
overview(country="Germany", subregion="LK Coburg");
2023-03-07T10:41:22.938540 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) 69.3 LK Coburg, 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 5 10 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 Coburg cases daily growth factor Germany-LK Coburg cases daily growth factor (rolling mean) Germany-LK Coburg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Coburg deaths daily growth factor Germany-LK Coburg deaths daily growth factor (rolling mean) Germany-LK Coburg 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 15000 cases doubling time [days] Germany-LK Coburg doubling time cases (rolling mean) Germany-LK Coburg doubling time deaths (rolling mean) 0.0 173.1 346.2 519.3 daily change Germany-LK Coburg new cases (rolling 7d mean) Germany-LK Coburg new cases 0.00 4.33 8.65 daily change Germany-LK Coburg new deaths (rolling 7d mean) Germany-LK Coburg new deaths 0.0 56.3 112.6 168.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Coburg", dates="2020-03-15:");
2023-03-07T10:44:00.330230 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 Coburg LK Coburg 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 Coburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Coburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Coburg")
print(f'Population of country="Germany", subregion="LK Coburg": {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 Coburg": 86544 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 42900 11 223 0
2023-03-03 42889 4 223 0
2023-03-02 42885 6 223 0
2023-03-01 42879 18 223 1
2023-02-28 42861 21 222 0
2023-02-27 42840 28 222 0
2023-02-24 42812 14 222 0
2023-02-23 42798 16 222 0
2023-02-22 42782 26 222 0
2023-02-21 42756 17 222 0
2023-02-20 42739 17 222 1
2023-02-17 42722 12 221 0
2023-02-16 42710 9 221 0
2023-02-15 42701 21 221 0
2023-02-14 42680 18 221 0
2023-02-13 42662 19 221 0
2023-02-10 42643 7 221 0
2023-02-09 42636 9 221 0
2023-02-08 42627 13 221 0
2023-02-07 42614 9 221 0
2023-02-06 42605 20 221 0
2023-02-03 42585 10 221 0
2023-02-02 42575 12 221 0
2023-02-01 42563 8 221 0
2023-01-31 42555 10 221 0
2023-01-30 42545 12 221 0
2023-01-27 42533 5 221 0
2023-01-26 42528 4 221 0
2023-01-25 42524 5 221 0
2023-01-24 42519 12 221 0
2023-01-23 42507 7 221 0
2023-01-20 42500 5 221 0
2023-01-19 42495 8 221 0
2023-01-18 42487 3 221 0
2023-01-17 42484 9 221 0
2023-01-16 42475 2 221 0
2023-01-13 42473 2 221 0
2023-01-12 42471 3 221 0
2023-01-11 42468 11 221 0
2023-01-10 42457 8 221 0
2023-01-09 42449 8 221 0
2023-01-05 42441 14 221 0
2023-01-04 42427 13 221 0
2023-01-03 42414 20 221 0
2023-01-02 42394 6 221 0
2022-12-30 42388 9 221 0
2022-12-29 42379 17 221 0
2022-12-28 42362 25 221 0
2022-12-27 42337 19 221 0
2022-12-23 42318 13 221 0
2022-12-22 42305 14 221 0
2022-12-21 42291 23 221 0
2022-12-20 42268 17 221 0
2022-12-19 42251 20 221 0
2022-12-16 42231 16 221 0
2022-12-15 42215 12 221 0
2022-12-14 42203 28 221 0
2022-12-13 42175 19 221 0
2022-12-12 42156 17 221 0
2022-12-09 42139 13 221 0
2022-12-08 42126 17 221 0
2022-12-07 42109 25 221 0
2022-12-06 42084 18 221 0
2022-12-05 42066 24 221 0
2022-12-02 42042 11 221 0
2022-12-01 42031 7 221 0
2022-11-30 42024 13 221 0
2022-11-29 42011 15 221 0
2022-11-28 41996 27 221 0
2022-11-25 41969 10 221 0
2022-11-24 41959 12 221 0
2022-11-23 41947 17 221 0
2022-11-22 41930 21 221 0
2022-11-21 41909 25 221 0
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2022-10-24 41077 124 219 1
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2022-10-13 40109 150 216 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: 0:03:22.972985