Nepal¶

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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 09:36:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Nepal", weeks=5);
2023-03-07T09:36:58.617523 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.02 0.02 0.04 0.04 0.06 0.06 7-day incidence rate (per 100K people) 0.0 Nepal, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.005 0.010 0.015 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 Nepal new deaths (rolling 7d mean) Nepal 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 R & growth factor (based on cases) Nepal cases daily growth factor Nepal cases daily growth factor (rolling mean) Nepal 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) Nepal deaths daily growth factor Nepal deaths daily growth factor (rolling mean) Nepal estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 200000 400000 600000 800000 cases doubling time [days] Nepal doubling time cases (rolling mean) 0.000 1.457 2.914 4.371 daily change Nepal new cases (rolling 7d mean) Nepal new cases 0.000 0.212 0.423 0.635 0.846
In [4]:
overview("Nepal");
2023-03-07T09:37:07.223661 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 50 50 100 100 150 150 200 200 7-day incidence rate (per 100K people) 0.0 Nepal, 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 10 20 30 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 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Nepal cases daily growth factor Nepal cases daily growth factor (rolling mean) Nepal 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) Nepal deaths daily growth factor Nepal deaths daily growth factor (rolling mean) Nepal 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 200000 400000 600000 800000 cases doubling time [days] Nepal doubling time cases (rolling mean) Nepal doubling time deaths (rolling mean) 0 2914 5827 8741 daily change Nepal new cases (rolling 7d mean) Nepal new cases 0.0 145.7 291.4 437.1 582.7 daily change Nepal new deaths (rolling 7d mean) Nepal new deaths 0 3316 6632 9949 13265 deaths doubling time [days]
In [5]:
compare_plot("Nepal", normalise=True);
2023-03-07T09:37:11.390999 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 Nepal Nepal 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) Nepal Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Nepal")

# get population of the region for future normalisation:
inhabitants = population("Nepal")
print(f'Population of "Nepal": {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 "Nepal": 29136808 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 1001149 0 12020 0
2023-03-05 1001149 0 12020 0
2023-03-04 1001149 0 12020 0
2023-03-03 1001149 0 12020 0
2023-03-02 1001149 1 12020 0
... ... ... ... ...
2020-01-27 1 0 0 0
2020-01-26 1 0 0 0
2020-01-25 1 1 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: 0:00:17.333972