Germany: SK Hamm (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 14:40:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Hamm", weeks=5);
2023-03-07T14:40:21.113324 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 150 150 7-day incidence rate (per 100K people) 52.4 SK Hamm, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Hamm new deaths (rolling 7d mean) Germany-SK Hamm new deaths 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Hamm cases daily growth factor Germany-SK Hamm cases daily growth factor (rolling mean) Germany-SK Hamm estimated R (using cases) 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 deaths) Germany-SK Hamm deaths daily growth factor Germany-SK Hamm deaths daily growth factor (rolling mean) Germany-SK Hamm 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-SK Hamm doubling time cases (rolling mean) 0.00 17.92 35.85 53.77 71.70 daily change Germany-SK Hamm new cases (rolling 7d mean) Germany-SK Hamm new cases 0.000 0.271 0.542 0.814
In [4]:
overview(country="Germany", subregion="SK Hamm");
2023-03-07T14:40:40.702091 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 2000 2000 7-day incidence rate (per 100K people) 52.4 SK Hamm, 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 4 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Hamm cases daily growth factor Germany-SK Hamm cases daily growth factor (rolling mean) Germany-SK Hamm estimated R (using cases) 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 deaths) Germany-SK Hamm deaths daily growth factor Germany-SK Hamm deaths daily growth factor (rolling mean) Germany-SK Hamm 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-SK Hamm doubling time cases (rolling mean) Germany-SK Hamm doubling time deaths (rolling mean) 0.0 179.2 358.5 537.7 717.0 daily change Germany-SK Hamm new cases (rolling 7d mean) Germany-SK Hamm new cases 0.000 1.792 3.585 5.377 7.170 daily change Germany-SK Hamm new deaths (rolling 7d mean) Germany-SK Hamm new deaths 0.0 64.3 128.7 193.0 257.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Hamm", dates="2020-03-15:");
2023-03-07T14:42:52.599152 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: SK Hamm SK Hamm 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 SK Hamm Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Hamm")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Hamm")
print(f'Population of country="Germany", subregion="SK Hamm": {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="SK Hamm": 179238 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 86399 2 300 0
2023-03-05 86397 2 300 0
2023-03-03 86395 12 300 0
2023-03-02 86383 12 300 0
2023-03-01 86371 17 300 0
... ... ... ... ...
2020-03-19 47 24 0 0
2020-03-18 23 7 0 0
2020-03-17 16 8 0 0
2020-03-16 8 4 0 0
2020-03-13 4 2 0 0

1030 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: 0:03:02.988582