United Kingdom¶

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  • 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 09:39:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("United Kingdom", weeks=5);
2023-03-07T09:39:24.438589 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 25 25 30 30 35 35 40 40 7-day incidence rate (per 100K people) 39.0 United Kingdom, 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 0.000 0.025 0.050 0.075 0.100 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) United Kingdom cases daily growth factor United Kingdom cases daily growth factor (rolling mean) United Kingdom 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) United Kingdom deaths daily growth factor United Kingdom deaths daily growth factor (rolling mean) United Kingdom estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] United Kingdom doubling time cases (rolling mean) United Kingdom doubling time deaths (rolling mean) 0 6789 13577 20366 27154 daily change United Kingdom new cases (rolling 7d mean) United Kingdom new cases 0.00 16.97 33.94 50.91 67.89 daily change United Kingdom new deaths (rolling 7d mean) United Kingdom new deaths 0 6480 12960 19440 25920 deaths doubling time [days]
In [4]:
overview("United Kingdom");
2023-03-07T09:39:33.190117 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 39.0 United Kingdom, 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 0 250 500 750 1000 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 2.0 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 1.0 1.0 1.2 1.2 R & growth factor (based on cases) United Kingdom cases daily growth factor United Kingdom cases daily growth factor (rolling mean) United Kingdom 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 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) United Kingdom deaths daily growth factor United Kingdom deaths daily growth factor (rolling mean) United Kingdom 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 0.0 0.5 1.0 1.5 2.0 cases doubling time [days] 1e6 United Kingdom doubling time cases (rolling mean) United Kingdom doubling time deaths (rolling mean) 0 169715 339430 509145 678860 daily change United Kingdom new cases (rolling 7d mean) United Kingdom new cases 0 339 679 1018 1358 daily change United Kingdom new deaths (rolling 7d mean) United Kingdom new deaths 0 16468 32936 49404 65872 deaths doubling time [days]
In [5]:
compare_plot("United Kingdom", normalise=True);
2023-03-07T09:39:37.409684 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 United Kingdom United Kingdom 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) United Kingdom Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("United Kingdom")

# get population of the region for future normalisation:
inhabitants = population("United Kingdom")
print(f'Population of "United Kingdom": {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 "United Kingdom": 67886004 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 24629840 0 220222 0
2023-03-05 24629840 0 220222 0
2023-03-04 24629840 0 220222 0
2023-03-03 24629840 10 220222 0
2023-03-02 24629830 26380 220222 0
... ... ... ... ...
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: 0:00:17.378129