Bhutan¶

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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 09:32:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Bhutan", weeks=5);
2023-03-07T09:32:13.479531 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.8 0.8 7-day incidence rate (per 100K people) 0.6 Bhutan, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 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 Bhutan new deaths (rolling 7d mean) Bhutan new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases) Bhutan cases daily growth factor Bhutan cases daily growth factor (rolling mean) Bhutan 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) Bhutan deaths daily growth factor Bhutan deaths daily growth factor (rolling mean) Bhutan estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20000 40000 60000 80000 cases doubling time [days] Bhutan doubling time cases (rolling mean) 0.000 1.543 3.086 4.630 daily change Bhutan new cases (rolling 7d mean) Bhutan new cases 0.000 0.213 0.426 0.639 0.853
In [4]:
overview("Bhutan");
2023-03-07T09:32:21.856908 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 7-day incidence rate (per 100K people) 0.6 Bhutan, 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 0 100 200 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 0.1 0.2 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 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases) Bhutan cases daily growth factor Bhutan cases daily growth factor (rolling mean) Bhutan 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 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Bhutan deaths daily growth factor Bhutan deaths daily growth factor (rolling mean) Bhutan 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 20000 40000 60000 80000 cases doubling time [days] Bhutan doubling time cases (rolling mean) Bhutan doubling time deaths (rolling mean) 0 772 1543 daily change Bhutan new cases (rolling 7d mean) Bhutan new cases 0.000 0.772 1.543 daily change Bhutan new deaths (rolling 7d mean) Bhutan new deaths 0.00 4.17 8.33 12.50 16.66 deaths doubling time [days]
In [5]:
compare_plot("Bhutan", normalise=True);
2023-03-07T09:32:25.723239 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 Bhutan Bhutan 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) Bhutan Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Bhutan")

# get population of the region for future normalisation:
inhabitants = population("Bhutan")
print(f'Population of "Bhutan": {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 "Bhutan": 771612 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 62620 0 21 0
2023-03-05 62620 0 21 0
2023-03-04 62620 0 21 0
2023-03-03 62620 0 21 0
2023-03-02 62620 0 21 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: 0:00:16.704871