Germany: LK Gotha (Thüringen)¶

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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 16:01:41 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Gotha", weeks=5);
2023-03-07T16:01:56.633971 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 40 40 50 50 60 60 7-day incidence rate (per 100K people) 33.6 LK Gotha, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Gotha cases daily growth factor Germany-LK Gotha cases daily growth factor (rolling mean) Germany-LK Gotha 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 Gotha deaths daily growth factor Germany-LK Gotha deaths daily growth factor (rolling mean) Germany-LK Gotha 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 Gotha doubling time cases (rolling mean) 0.00 6.69 13.38 20.07 daily change Germany-LK Gotha new cases (rolling 7d mean) Germany-LK Gotha new cases 0.000 0.268 0.535 0.803 daily change Germany-LK Gotha new deaths (rolling 7d mean) Germany-LK Gotha new deaths 0.000 0.228 0.456 0.684 0.912
In [4]:
overview(country="Germany", subregion="LK Gotha");
2023-03-07T16:02:19.270678 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) 33.6 LK Gotha, 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Gotha cases daily growth factor Germany-LK Gotha cases daily growth factor (rolling mean) Germany-LK Gotha 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 Gotha deaths daily growth factor Germany-LK Gotha deaths daily growth factor (rolling mean) Germany-LK Gotha 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 Gotha doubling time cases (rolling mean) Germany-LK Gotha doubling time deaths (rolling mean) 0.0 267.7 535.3 daily change Germany-LK Gotha new cases (rolling 7d mean) Germany-LK Gotha new cases 0.000 2.676 5.353 daily change Germany-LK Gotha new deaths (rolling 7d mean) Germany-LK Gotha new deaths 0.0 98.3 196.7 295.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Gotha", dates="2020-03-15:");
2023-03-07T16:04:26.803020 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 Gotha LK Gotha 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 Gotha Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Gotha")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Gotha")
print(f'Population of country="Germany", subregion="LK Gotha": {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 Gotha": 133825 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 53025 2 382 0
2023-03-03 53023 7 382 0
2023-03-02 53016 12 382 0
2023-03-01 53004 9 382 0
2023-02-28 52995 15 382 0
2023-02-27 52980 18 382 0
2023-02-24 52962 18 382 1
2023-02-23 52944 15 381 0
2023-02-22 52929 11 381 0
2023-02-21 52918 24 381 0
2023-02-20 52894 6 381 0
2023-02-17 52888 3 381 0
2023-02-16 52885 15 381 0
2023-02-15 52870 16 381 0
2023-02-14 52854 20 381 0
2023-02-13 52834 22 381 0
2023-02-10 52812 7 381 1
2023-02-09 52805 14 380 0
2023-02-08 52791 2 380 0
2023-02-07 52789 9 380 0
2023-02-06 52780 17 380 0
2023-02-03 52763 17 380 0
2023-02-02 52746 10 380 0
2023-02-01 52736 16 380 0
2023-01-31 52720 6 380 0
2023-01-30 52714 8 380 0
2023-01-27 52706 11 380 0
2023-01-26 52695 11 380 0
2023-01-24 52684 23 380 1
2023-01-23 52661 15 379 0
2023-01-20 52646 15 379 0
2023-01-18 52631 10 379 0
2023-01-17 52621 18 379 0
2023-01-16 52603 16 379 0
2023-01-13 52587 5 379 0
2023-01-12 52582 21 379 0
2023-01-10 52561 20 379 0
2023-01-09 52541 29 379 0
2023-01-06 52512 17 379 0
2023-01-05 52495 13 379 0
2023-01-04 52482 31 379 0
2023-01-03 52451 47 379 0
2023-01-02 52404 105 379 2
2022-12-23 52299 16 377 0
2022-12-22 52283 51 377 0
2022-12-21 52232 45 377 0
2022-12-20 52187 55 377 1
2022-12-16 52132 54 376 0
2022-12-14 52078 17 376 1
2022-12-13 52061 47 375 0
2022-12-12 52014 40 375 1
2022-12-09 51974 42 374 0
2022-12-08 51932 22 374 0
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2020-11-30 891 5 42 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: 0:02:53.830557