Vietnam¶

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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 09:39:49 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Vietnam", weeks=5);
2023-03-07T09:39:52.734129 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.075 0.075 0.100 0.100 0.125 0.125 0.150 0.150 7-day incidence rate (per 100K people) 0.1 Vietnam, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.02 0.04 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 Vietnam new deaths (rolling 7d mean) Vietnam new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Vietnam cases daily growth factor Vietnam cases daily growth factor (rolling mean) Vietnam 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) Vietnam deaths daily growth factor Vietnam deaths daily growth factor (rolling mean) Vietnam estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 cases doubling time [days] 1e6 Vietnam doubling time cases (rolling mean) 0.00 19.47 38.94 daily change Vietnam new cases (rolling 7d mean) Vietnam new cases 0.000 0.383 0.767
In [4]:
overview("Vietnam");
2023-03-07T09:40:01.142363 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) 0.1 Vietnam, 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 0 100 200 300 400 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 0.2 0.4 0.6 0.8 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 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Vietnam cases daily growth factor Vietnam cases daily growth factor (rolling mean) Vietnam 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) Vietnam deaths daily growth factor Vietnam deaths daily growth factor (rolling mean) Vietnam 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 cases doubling time [days] 1e6 Vietnam doubling time cases (rolling mean) Vietnam doubling time deaths (rolling mean) 0 97339 194677 292016 389354 daily change Vietnam new cases (rolling 7d mean) Vietnam new cases 0.0 194.7 389.4 584.0 778.7 daily change Vietnam new deaths (rolling 7d mean) Vietnam new deaths 0 18237 36473 deaths doubling time [days]
In [5]:
compare_plot("Vietnam", normalise=True);
2023-03-07T09:40:04.872573 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 Vietnam Vietnam 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) Vietnam Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Vietnam")

# get population of the region for future normalisation:
inhabitants = population("Vietnam")
print(f'Population of "Vietnam": {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 "Vietnam": 97338583 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 11526966 0 43186 0
2023-03-05 11526966 4 43186 0
2023-03-04 11526962 12 43186 0
2023-03-03 11526950 13 43186 0
2023-03-02 11526937 11 43186 0
... ... ... ... ...
2020-01-27 2 0 0 0
2020-01-26 2 0 0 0
2020-01-25 2 0 0 0
2020-01-24 2 0 0 0
2020-01-23 2 2 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: 0:00:15.984402