Germany: LK Höxter (Nordrhein-Westfalen)¶

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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 13:53:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Höxter", weeks=5);
2023-03-07T13:53:59.938166 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) 77.1 LK Höxter, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 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 Höxter cases daily growth factor Germany-LK Höxter cases daily growth factor (rolling mean) Germany-LK Höxter 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 Höxter deaths daily growth factor Germany-LK Höxter deaths daily growth factor (rolling mean) Germany-LK Höxter 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 Höxter doubling time cases (rolling mean) 0 28 56 84 daily change Germany-LK Höxter new cases (rolling 7d mean) Germany-LK Höxter new cases 0.0 0.7 1.4 daily change Germany-LK Höxter new deaths (rolling 7d mean) Germany-LK Höxter new deaths 0.000 0.245 0.490 0.735 0.980
In [4]:
overview(country="Germany", subregion="LK Höxter");
2023-03-07T13:54:18.810590 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) 77.1 LK Höxter, 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 8 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 Höxter cases daily growth factor Germany-LK Höxter cases daily growth factor (rolling mean) Germany-LK Höxter 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 Höxter deaths daily growth factor Germany-LK Höxter deaths daily growth factor (rolling mean) Germany-LK Höxter 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 cases doubling time [days] Germany-LK Höxter doubling time cases (rolling mean) Germany-LK Höxter doubling time deaths (rolling mean) 0 280 560 daily change Germany-LK Höxter new cases (rolling 7d mean) Germany-LK Höxter new cases 0.0 2.8 5.6 8.4 11.2 daily change Germany-LK Höxter new deaths (rolling 7d mean) Germany-LK Höxter new deaths 0.0 86.2 172.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Höxter", dates="2020-03-15:");
2023-03-07T13:56:23.362932 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 Höxter LK Höxter 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 Höxter Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Höxter")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Höxter")
print(f'Population of country="Germany", subregion="LK Höxter": {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 Höxter": 139994 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 56119 19 241 0
2023-03-03 56100 7 241 0
2023-03-02 56093 26 241 0
2023-03-01 56067 23 241 1
2023-02-28 56044 33 240 0
2023-02-27 56011 100 240 0
2023-02-24 55911 40 240 0
2023-02-23 55871 42 240 0
2023-02-22 55829 39 240 0
2023-02-21 55790 44 240 0
2023-02-20 55746 44 240 0
2023-02-17 55702 20 240 0
2023-02-16 55682 28 240 0
2023-02-15 55654 39 240 0
2023-02-14 55615 32 240 0
2023-02-13 55583 27 240 2
2023-02-10 55556 18 238 0
2023-02-09 55538 25 238 0
2023-02-08 55513 35 238 1
2023-02-07 55478 25 237 0
2023-02-06 55453 38 237 0
2023-02-03 55415 16 237 2
2023-02-02 55399 13 235 0
2023-02-01 55386 22 235 0
2023-01-31 55364 19 235 0
2023-01-30 55345 30 235 0
2023-01-27 55315 9 235 0
2023-01-26 55306 18 235 0
2023-01-25 55288 16 235 0
2023-01-24 55272 24 235 0
2023-01-23 55248 28 235 0
2023-01-20 55220 10 235 0
2023-01-19 55210 20 235 0
2023-01-18 55190 20 235 0
2023-01-17 55170 14 235 0
2023-01-16 55156 33 235 0
2023-01-13 55123 18 235 0
2023-01-12 55105 20 235 0
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2023-01-06 54982 47 235 0
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2023-01-04 54919 52 235 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: 0:02:54.402094