Germany: LK Hildesheim (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 13:14:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hildesheim", weeks=5);
2023-03-07T13:14:55.882348 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 7-day incidence rate (per 100K people) 57.1 LK Hildesheim, 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 0.1 0.2 0.3 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 Hildesheim cases daily growth factor Germany-LK Hildesheim cases daily growth factor (rolling mean) Germany-LK Hildesheim 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 Hildesheim deaths daily growth factor Germany-LK Hildesheim deaths daily growth factor (rolling mean) Germany-LK Hildesheim estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Hildesheim doubling time cases (rolling mean) 0.0 27.5 55.0 82.4 109.9 daily change Germany-LK Hildesheim new cases (rolling 7d mean) Germany-LK Hildesheim new cases 0.000 0.275 0.550 0.824 daily change Germany-LK Hildesheim new deaths (rolling 7d mean) Germany-LK Hildesheim new deaths 0.000 0.284 0.568 0.852
In [4]:
overview(country="Germany", subregion="LK Hildesheim");
2023-03-07T13:15:15.186931 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) 57.1 LK Hildesheim, 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 6 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 Hildesheim cases daily growth factor Germany-LK Hildesheim cases daily growth factor (rolling mean) Germany-LK Hildesheim 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 Hildesheim deaths daily growth factor Germany-LK Hildesheim deaths daily growth factor (rolling mean) Germany-LK Hildesheim estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Hildesheim doubling time cases (rolling mean) Germany-LK Hildesheim doubling time deaths (rolling mean) 0 550 1099 daily change Germany-LK Hildesheim new cases (rolling 7d mean) Germany-LK Hildesheim new cases 0.00 5.50 10.99 16.49 daily change Germany-LK Hildesheim new deaths (rolling 7d mean) Germany-LK Hildesheim new deaths 0 200 400 601 801 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hildesheim", dates="2020-03-15:");
2023-03-07T13:17:56.305936 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 Hildesheim LK Hildesheim 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 Hildesheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hildesheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hildesheim")
print(f'Population of country="Germany", subregion="LK Hildesheim": {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 Hildesheim": 274773 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 131155 20 741 0
2023-03-03 131135 16 741 0
2023-03-02 131119 8 741 0
2023-03-01 131111 32 741 0
2023-02-28 131079 81 741 0
2023-02-27 130998 80 741 0
2023-02-24 130918 62 741 0
2023-02-23 130856 54 741 0
2023-02-22 130802 76 741 0
2023-02-21 130726 97 741 0
2023-02-20 130629 136 741 0
2023-02-17 130493 58 741 0
2023-02-16 130435 45 741 0
2023-02-15 130390 90 741 0
2023-02-14 130300 100 741 0
2023-02-13 130200 84 741 0
2023-02-10 130116 58 741 0
2023-02-09 130058 56 741 0
2023-02-08 130002 70 741 0
2023-02-07 129932 80 741 0
2023-02-06 129852 96 741 1
2023-02-03 129756 47 740 0
2023-02-02 129709 44 740 0
2023-02-01 129665 86 740 0
2023-01-31 129579 114 740 0
2023-01-30 129465 155 740 0
2023-01-27 129310 66 740 0
2023-01-26 129244 73 740 0
2023-01-25 129171 73 740 0
2023-01-24 129098 137 740 0
2023-01-23 128961 114 740 0
2023-01-20 128847 47 740 0
2023-01-19 128800 72 740 0
2023-01-18 128728 67 740 0
2023-01-17 128661 92 740 0
2023-01-16 128569 114 740 0
2023-01-13 128455 59 740 0
2023-01-12 128396 71 740 0
2023-01-11 128325 158 740 0
2023-01-10 128167 130 740 0
2023-01-09 128037 148 740 0
2023-01-06 127889 89 740 1
2023-01-05 127800 98 739 1
2023-01-04 127702 172 738 2
2023-01-03 127530 169 736 1
2023-01-02 127361 254 735 0
2022-12-30 127107 123 735 1
2022-12-29 126984 194 734 1
2022-12-28 126790 305 733 2
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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: 0:03:44.760841