Germany: LK Harburg (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 13:30:48 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Harburg", weeks=5);
2023-03-07T13:31:00.207171 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 7-day incidence rate (per 100K people) 45.0 LK Harburg, 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 0.0 0.1 0.2 0.3 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 Harburg cases daily growth factor Germany-LK Harburg cases daily growth factor (rolling mean) Germany-LK Harburg 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 Harburg deaths daily growth factor Germany-LK Harburg deaths daily growth factor (rolling mean) Germany-LK Harburg 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 Harburg doubling time cases (rolling mean) 0.0 25.8 51.5 77.3 daily change Germany-LK Harburg new cases (rolling 7d mean) Germany-LK Harburg new cases 0.000 0.258 0.515 0.773 daily change Germany-LK Harburg new deaths (rolling 7d mean) Germany-LK Harburg new deaths 0.000 0.244 0.489 0.733 0.978
In [4]:
overview(country="Germany", subregion="LK Harburg");
2023-03-07T13:31:15.647000 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 45.0 LK Harburg, 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 1 2 3 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 Harburg cases daily growth factor Germany-LK Harburg cases daily growth factor (rolling mean) Germany-LK Harburg 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 Harburg deaths daily growth factor Germany-LK Harburg deaths daily growth factor (rolling mean) Germany-LK Harburg 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 8000 cases doubling time [days] Germany-LK Harburg doubling time cases (rolling mean) Germany-LK Harburg doubling time deaths (rolling mean) 0 258 515 773 1030 daily change Germany-LK Harburg new cases (rolling 7d mean) Germany-LK Harburg new cases 0.00 2.58 5.15 7.73 daily change Germany-LK Harburg new deaths (rolling 7d mean) Germany-LK Harburg new deaths 0.0 67.9 135.9 203.8 271.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Harburg", dates="2020-03-15:");
2023-03-07T13:33:48.361045 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 Harburg LK Harburg 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 Harburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Harburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Harburg")
print(f'Population of country="Germany", subregion="LK Harburg": {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 Harburg": 257548 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 119669 6 306 0
2023-03-05 119663 2 306 0
2023-03-04 119661 7 306 0
2023-03-03 119654 9 306 0
2023-03-02 119645 16 306 0
... ... ... ... ...
2020-03-14 22 8 0 0
2020-03-13 14 2 0 0
2020-03-12 12 7 0 0
2020-03-11 5 1 0 0
2020-03-07 4 1 0 0

1007 rows × 4 columns

Explore the data in your web browser¶

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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: 0:03:28.003010