South Africa¶

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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 09:38:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("South Africa", weeks=5);
2023-03-07T09:38:11.160049 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 7-day incidence rate (per 100K people) 4.4 South Africa, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.000 0.025 0.025 0.050 0.050 0.075 0.075 0.100 0.100 daily change South Africa new deaths (rolling 7d mean) South Africa new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 R & growth factor (based on cases) South Africa cases daily growth factor South Africa cases daily growth factor (rolling mean) South Africa 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) South Africa deaths daily growth factor South Africa deaths daily growth factor (rolling mean) South Africa estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 30000 40000 cases doubling time [days] South Africa doubling time cases (rolling mean) 0 593 1186 1779 daily change South Africa new cases (rolling 7d mean) South Africa new cases 0.000 0.232 0.464 0.697 0.929
In [4]:
overview("South Africa");
2023-03-07T09:38:20.322152 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 0 100 100 200 200 7-day incidence rate (per 100K people) 4.4 South Africa, 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 20 40 60 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 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 3 3 R & growth factor (based on cases) South Africa cases daily growth factor South Africa cases daily growth factor (rolling mean) South Africa 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) South Africa deaths daily growth factor South Africa deaths daily growth factor (rolling mean) South Africa 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 cases doubling time [days] South Africa doubling time cases (rolling mean) South Africa doubling time deaths (rolling mean) 0 11862 23723 35585 daily change South Africa new cases (rolling 7d mean) South Africa new cases 0.0 296.5 593.1 daily change South Africa new deaths (rolling 7d mean) South Africa new deaths 0 32569 65138 97708 deaths doubling time [days]
In [5]:
compare_plot("South Africa", normalise=True);
2023-03-07T09:38:24.541083 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 South Africa South Africa 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) South Africa Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("South Africa")

# get population of the region for future normalisation:
inhabitants = population("South Africa")
print(f'Population of "South Africa": {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 "South Africa": 59308690 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4065857 968 102595 0
2023-03-05 4064889 0 102595 0
2023-03-04 4064889 0 102595 0
2023-03-03 4064889 432 102595 0
2023-03-02 4064457 594 102595 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: 0:00:17.751277