Denmark¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 09:33:40 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Denmark", weeks=5);
2023-03-07T09:33:44.706095 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 14 14 16 16 18 18 7-day incidence rate (per 100K people) 12.6 Denmark, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2 4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 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) Denmark cases daily growth factor Denmark cases daily growth factor (rolling mean) Denmark estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Denmark deaths daily growth factor Denmark deaths daily growth factor (rolling mean) Denmark estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 cases doubling time [days] Denmark doubling time cases (rolling mean) Denmark doubling time deaths (rolling mean) 0.0 116.7 233.5 daily change Denmark new cases (rolling 7d mean) Denmark new cases 0.00 5.84 11.67 daily change Denmark new deaths (rolling 7d mean) Denmark new deaths 0 2349 4698 deaths doubling time [days]
In [4]:
overview("Denmark");
2023-03-07T09:33:53.045780 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 2000 2000 4000 4000 7-day incidence rate (per 100K people) 12.6 Denmark, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200 400 600 800 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.5 1.0 1.5 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 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) Denmark cases daily growth factor Denmark cases daily growth factor (rolling mean) Denmark estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Denmark deaths daily growth factor Denmark deaths daily growth factor (rolling mean) Denmark estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 10000 20000 30000 cases doubling time [days] Denmark doubling time cases (rolling mean) Denmark doubling time deaths (rolling mean) 0 11674 23349 35023 46698 daily change Denmark new cases (rolling 7d mean) Denmark new cases 0.0 29.2 58.4 87.6 daily change Denmark new deaths (rolling 7d mean) Denmark new deaths 0 1672 3343 5015 deaths doubling time [days]
In [5]:
compare_plot("Denmark", normalise=True);
2023-03-07T09:33:57.087057 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.001 0.001 0.1 0.1 10 10 1000 1000 daily new cases per 100K people (rolling 7-day mean) Daily cases (top) and deaths (below) for Denmark Denmark Germany Australia Poland Korea, South Belarus Switzerland US 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.0001 0.0001 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths per 100K people (rolling 7-day mean) Denmark Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Denmark")

# get population of the region for future normalisation:
inhabitants = population("Denmark")
print(f'Population of "Denmark": {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 "Denmark": 5837213 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 3450587 251 8333 10
2023-03-05 3450336 0 8323 0
2023-03-04 3450336 0 8323 0
2023-03-03 3450336 113 8323 2
2023-03-02 3450223 122 8321 3
... ... ... ... ...
2020-01-27 0 0 0 0
2020-01-26 0 0 0 0
2020-01-25 0 0 0 0
2020-01-24 0 0 0 0
2020-01-23 0 0 0 0

1139 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: 0:00:17.072347