Germany: LK Celle (Niedersachsen)¶

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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 13:07:29 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Celle", weeks=5);
2023-03-07T13:07:38.898314 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 80.6 LK Celle, 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 40 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 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-LK Celle cases daily growth factor Germany-LK Celle cases daily growth factor (rolling mean) Germany-LK Celle estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Celle deaths daily growth factor Germany-LK Celle deaths daily growth factor (rolling mean) Germany-LK Celle estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Celle doubling time cases (rolling mean) 0.00 17.99 35.98 53.97 71.97 daily change Germany-LK Celle new cases (rolling 7d mean) Germany-LK Celle new cases 0.00 0.36 0.72 daily change Germany-LK Celle new deaths (rolling 7d mean) Germany-LK Celle new deaths 0.000 0.228 0.456 0.684 0.912
In [4]:
overview(country="Germany", subregion="LK Celle");
2023-03-07T13:07:57.876875 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) 80.6 LK Celle, 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 1 2 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-LK Celle cases daily growth factor Germany-LK Celle cases daily growth factor (rolling mean) Germany-LK Celle estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Celle deaths daily growth factor Germany-LK Celle deaths daily growth factor (rolling mean) Germany-LK Celle 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 Celle doubling time cases (rolling mean) Germany-LK Celle doubling time deaths (rolling mean) 0 360 720 1079 daily change Germany-LK Celle new cases (rolling 7d mean) Germany-LK Celle new cases 0.000 1.799 3.598 daily change Germany-LK Celle new deaths (rolling 7d mean) Germany-LK Celle new deaths 0.0 95.5 191.0 286.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Celle", dates="2020-03-15:");
2023-03-07T13:10:23.622648 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 Celle LK Celle 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 Celle Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Celle")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Celle")
print(f'Population of country="Germany", subregion="LK Celle": {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 Celle": 179915 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 81943 22 243 0
2023-03-03 81921 14 243 0
2023-03-02 81907 31 243 0
2023-03-01 81876 35 243 0
2023-02-28 81841 43 243 0
2023-02-27 81798 62 243 0
2023-02-24 81736 12 243 0
2023-02-23 81724 41 243 0
2023-02-22 81683 50 243 0
2023-02-21 81633 33 243 0
2023-02-20 81600 61 243 0
2023-02-17 81539 33 243 0
2023-02-16 81506 22 243 0
2023-02-15 81484 53 243 1
2023-02-14 81431 34 242 1
2023-02-13 81397 45 241 0
2023-02-10 81352 23 241 0
2023-02-09 81329 30 241 0
2023-02-08 81299 33 241 0
2023-02-07 81266 48 241 0
2023-02-06 81218 43 241 0
2023-02-03 81175 19 241 0
2023-02-02 81156 58 241 0
2023-02-01 81098 78 241 1
2023-01-31 81020 41 240 0
2023-01-30 80979 53 240 0
2023-01-27 80926 11 240 0
2023-01-26 80915 51 240 0
2023-01-25 80864 78 240 0
2023-01-24 80786 67 240 0
2023-01-23 80719 52 240 2
2023-01-20 80667 23 238 0
2023-01-19 80644 33 238 0
2023-01-18 80611 65 238 0
2023-01-17 80546 50 238 0
2023-01-16 80496 50 238 0
2023-01-13 80446 24 238 1
2023-01-12 80422 28 237 0
2023-01-11 80394 101 237 0
2023-01-10 80293 47 237 1
2023-01-09 80246 84 236 0
2023-01-06 80162 49 236 1
2023-01-05 80113 44 235 0
2023-01-04 80069 105 235 0
2023-01-03 79964 123 235 0
2023-01-02 79841 84 235 1
2022-12-30 79757 90 234 0
2022-12-29 79667 115 234 1
2022-12-28 79552 149 233 3
2022-12-27 79403 137 230 1
2022-12-23 79266 118 229 0
2022-12-22 79148 203 229 2
2022-12-21 78945 224 227 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: 0:03:06.399900