Switzerland¶

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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 09:38:27 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Switzerland", weeks=5);
2023-03-07T09:38:30.818974 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 10.0 10.0 12.5 12.5 15.0 15.0 17.5 17.5 7-day incidence rate (per 100K people) 15.9 Switzerland, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.01 0.02 0.03 0.04 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) Switzerland cases daily growth factor Switzerland cases daily growth factor (rolling mean) Switzerland estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Switzerland deaths daily growth factor Switzerland deaths daily growth factor (rolling mean) Switzerland estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 30000 cases doubling time [days] Switzerland doubling time cases (rolling mean) Switzerland doubling time deaths (rolling mean) 0 433 865 1298 daily change Switzerland new cases (rolling 7d mean) Switzerland new cases 0.000 0.865 1.731 2.596 3.462 daily change Switzerland new deaths (rolling 7d mean) Switzerland new deaths 0 5653 11305 16958 deaths doubling time [days]
In [4]:
overview("Switzerland");
2023-03-07T09:38:40.053512 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 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 15.9 Switzerland, 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 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) Switzerland cases daily growth factor Switzerland cases daily growth factor (rolling mean) Switzerland 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 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Switzerland deaths daily growth factor Switzerland deaths daily growth factor (rolling mean) Switzerland 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] Switzerland doubling time cases (rolling mean) Switzerland doubling time deaths (rolling mean) 0 21637 43273 64910 86546 daily change Switzerland new cases (rolling 7d mean) Switzerland new cases 0.0 43.3 86.5 129.8 daily change Switzerland new deaths (rolling 7d mean) Switzerland new deaths 0 6241 12482 18724 deaths doubling time [days]
In [5]:
compare_plot("Switzerland", normalise=True);
2023-03-07T09:38:44.372638 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 Switzerland Switzerland Germany Australia Poland Korea, South Belarus 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) Switzerland Germany Australia Poland Korea, South Belarus US
In [6]:
# load the data
cases, deaths = get_country_data("Switzerland")

# get population of the region for future normalisation:
inhabitants = population("Switzerland")
print(f'Population of "Switzerland": {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 "Switzerland": 8654618 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4412439 0 14207 0
2023-03-05 4412439 0 14207 0
2023-03-04 4412439 0 14207 0
2023-03-03 4412439 0 14207 0
2023-03-02 4412439 0 14207 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: 0:00:17.588994