Bolivia¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 09:32:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Bolivia", weeks=5);
2023-03-07T09:32:05.423960 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 10 10 20 20 30 30 7-day incidence rate (per 100K people) 8.9 Bolivia, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1 2 3 4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.01 0.02 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Bolivia cases daily growth factor Bolivia cases daily growth factor (rolling mean) Bolivia 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) Bolivia deaths daily growth factor Bolivia deaths daily growth factor (rolling mean) Bolivia estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 cases doubling time [days] Bolivia doubling time cases (rolling mean) Bolivia doubling time deaths (rolling mean) 0.0 116.7 233.5 350.2 466.9 daily change Bolivia new cases (rolling 7d mean) Bolivia new cases 0.000 1.167 2.335 daily change Bolivia new deaths (rolling 7d mean) Bolivia new deaths 0 8697 17394 deaths doubling time [days]
In [4]:
overview("Bolivia");
2023-03-07T09:32:14.701517 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 0 200 200 400 400 600 600 7-day incidence rate (per 100K people) 8.9 Bolivia, 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 50 100 150 200 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 5 10 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Bolivia cases daily growth factor Bolivia cases daily growth factor (rolling mean) Bolivia 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) Bolivia deaths daily growth factor Bolivia deaths daily growth factor (rolling mean) Bolivia 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 40000 cases doubling time [days] Bolivia doubling time cases (rolling mean) Bolivia doubling time deaths (rolling mean) 0 5837 11673 17510 23346 daily change Bolivia new cases (rolling 7d mean) Bolivia new cases 0 584 1167 daily change Bolivia new deaths (rolling 7d mean) Bolivia new deaths 0 5319 10637 15956 21275 deaths doubling time [days]
In [5]:
compare_plot("Bolivia", normalise=True);
2023-03-07T09:32:18.356992 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 Bolivia Bolivia 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) Bolivia Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Bolivia")

# get population of the region for future normalisation:
inhabitants = population("Bolivia")
print(f'Population of "Bolivia": {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 "Bolivia": 11673029 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 1193970 62 22365 0
2023-03-05 1193908 93 22365 0
2023-03-04 1193815 165 22365 0
2023-03-03 1193650 232 22365 0
2023-03-02 1193418 162 22365 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: 0:00:17.097977