Germany: LK Leer (Niedersachsen)¶

  • 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 13:23:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Leer", weeks=5);
2023-03-07T13:23:19.994750 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 53.9 LK Leer, 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 0.0 0.2 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Leer cases daily growth factor Germany-LK Leer cases daily growth factor (rolling mean) Germany-LK Leer estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Leer deaths daily growth factor Germany-LK Leer deaths daily growth factor (rolling mean) Germany-LK Leer 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 Leer doubling time cases (rolling mean) 0.00 17.24 34.48 daily change Germany-LK Leer new cases (rolling 7d mean) Germany-LK Leer new cases 0.000 0.345 0.690 daily change Germany-LK Leer new deaths (rolling 7d mean) Germany-LK Leer new deaths 0.00 0.34 0.68
In [4]:
overview(country="Germany", subregion="LK Leer");
2023-03-07T13:23:35.305661 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) 53.9 LK Leer, 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 0.0 0.5 1.0 1.5 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Leer cases daily growth factor Germany-LK Leer cases daily growth factor (rolling mean) Germany-LK Leer estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Leer deaths daily growth factor Germany-LK Leer deaths daily growth factor (rolling mean) Germany-LK Leer 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 Leer doubling time cases (rolling mean) Germany-LK Leer doubling time deaths (rolling mean) 0 345 690 daily change Germany-LK Leer new cases (rolling 7d mean) Germany-LK Leer new cases 0.000 0.862 1.724 2.586 daily change Germany-LK Leer new deaths (rolling 7d mean) Germany-LK Leer new deaths 0.0 75.3 150.7 226.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Leer", dates="2020-03-15:");
2023-03-07T13:26:04.291363 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 Leer LK Leer 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 Leer Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Leer")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Leer")
print(f'Population of country="Germany", subregion="LK Leer": {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 Leer": 172421 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 72313 2 215 0
2023-03-04 72311 9 215 0
2023-03-03 72302 12 215 0
2023-03-02 72290 9 215 0
2023-03-01 72281 27 215 0
2023-02-28 72254 34 215 0
2023-02-27 72220 29 215 0
2023-02-26 72191 1 215 0
2023-02-25 72190 6 215 0
2023-02-24 72184 38 215 0
2023-02-23 72146 22 215 0
2023-02-22 72124 42 215 1
2023-02-21 72082 41 214 0
2023-02-20 72041 20 214 0
2023-02-19 72021 1 214 0
2023-02-18 72020 6 214 0
2023-02-17 72014 31 214 0
2023-02-16 71983 21 214 1
2023-02-15 71962 28 213 0
2023-02-14 71934 21 213 0
2023-02-13 71913 27 213 0
2023-02-12 71886 1 213 0
2023-02-11 71885 6 213 0
2023-02-10 71879 30 213 1
2023-02-09 71849 21 212 1
2023-02-08 71828 29 211 0
2023-02-07 71799 27 211 0
2023-02-06 71772 16 211 0
2023-02-04 71756 21 211 1
2023-02-03 71735 21 210 0
2023-02-02 71714 17 210 0
2023-02-01 71697 46 210 0
2023-01-31 71651 44 210 0
2023-01-30 71607 38 210 1
2023-01-28 71569 13 209 0
2023-01-27 71556 47 209 0
2023-01-26 71509 24 209 0
2023-01-25 71485 37 209 0
2023-01-24 71448 24 209 0
2023-01-23 71424 24 209 0
2023-01-22 71400 3 209 0
2023-01-21 71397 10 209 0
2023-01-20 71387 14 209 0
2023-01-19 71373 19 209 0
2023-01-18 71354 20 209 1
2023-01-17 71334 29 208 0
2023-01-16 71305 20 208 1
2023-01-15 71285 1 207 0
2023-01-14 71284 11 207 0
2023-01-13 71273 19 207 0
2023-01-12 71254 28 207 0
2023-01-11 71226 31 207 0
2023-01-10 71195 53 207 0
2023-01-09 71142 31 207 0
2023-01-08 71111 4 207 0
2023-01-07 71107 16 207 0
2023-01-06 71091 37 207 0
2023-01-05 71054 57 207 0
2023-01-04 70997 77 207 1
2023-01-03 70920 63 206 2
2023-01-02 70857 51 204 1
2023-01-01 70806 3 203 0
2022-12-31 70803 16 203 0
2022-12-30 70787 70 203 0
2022-12-29 70717 57 203 0
2022-12-28 70660 91 203 2
2022-12-27 70569 107 201 2
2022-12-26 70462 6 199 0
2022-12-25 70456 5 199 1
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2022-11-29 68495 100 182 0
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2022-11-27 68321 24 181 0
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2022-11-25 68288 55 181 0
2022-11-24 68233 58 181 0
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2022-11-22 68103 116 181 1
2022-11-21 67987 64 180 3
2022-11-20 67923 6 177 0
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2022-11-18 67891 62 177 2
2022-11-17 67829 47 175 1
2022-11-16 67782 46 174 0
2022-11-15 67736 105 174 0
2022-11-14 67631 45 174 0
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2022-10-23 65929 10 167 2
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2022-10-20 65738 210 165 0
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2020-03-24 17 3 2 0
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2020-03-18 10 7 2 1
2020-03-15 3 2 1 1

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: 0:03:45.932841