Venezuela¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
  • Execute this Jupyter Notebook using myBinder
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 09:40:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Venezuela", weeks=5);
2023-03-07T09:40:05.259954 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.2 0.2 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 7-day incidence rate (per 100K people) 0.5 Venezuela, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.002 0.004 0.006 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 R & growth factor (based on cases) Venezuela cases daily growth factor Venezuela cases daily growth factor (rolling mean) Venezuela estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 R & growth factor (based on deaths) Venezuela deaths daily growth factor Venezuela deaths daily growth factor (rolling mean) Venezuela 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] Venezuela doubling time cases (rolling mean) Venezuela doubling time deaths (rolling mean) 0.00 28.44 56.87 daily change Venezuela new cases (rolling 7d mean) Venezuela new cases 0.000 0.569 1.137 1.706 daily change Venezuela new deaths (rolling 7d mean) Venezuela new deaths 0 1293 2586 3879 5173 deaths doubling time [days]
In [4]:
overview("Venezuela");
2023-03-07T09:40:13.237344 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 20 20 40 40 60 60 7-day incidence rate (per 100K people) 0.5 Venezuela, 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 5 10 15 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 0.000 0.025 0.050 0.075 0.100 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 1 1 2 2 R & growth factor (based on cases) Venezuela cases daily growth factor Venezuela cases daily growth factor (rolling mean) Venezuela 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 1 1 2 2 3 3 R & growth factor (based on deaths) Venezuela deaths daily growth factor Venezuela deaths daily growth factor (rolling mean) Venezuela 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] Venezuela doubling time cases (rolling mean) Venezuela doubling time deaths (rolling mean) 0 1422 2844 4265 daily change Venezuela new cases (rolling 7d mean) Venezuela new cases 0.00 7.11 14.22 21.33 28.44 daily change Venezuela new deaths (rolling 7d mean) Venezuela new deaths 0 1394 2787 4181 5574 deaths doubling time [days]
In [5]:
compare_plot("Venezuela", normalise=True);
2023-03-07T09:40:16.835776 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 Venezuela Venezuela 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) Venezuela Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Venezuela")

# get population of the region for future normalisation:
inhabitants = population("Venezuela")
print(f'Population of "Venezuela": {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 "Venezuela": 28435943 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 552125 74 5854 0
2023-03-05 552051 0 5854 0
2023-03-04 552051 37 5854 0
2023-03-03 552014 28 5854 0
2023-03-02 551986 0 5854 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¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

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: 0:00:15.306351