Zambia¶

  • 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("Zambia", weeks=5);
2023-03-07T09:40:05.148546 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 7-day incidence rate (per 100K people) 1.7 Zambia, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 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 1 1 2 2 3 3 4 4 R & growth factor (based on cases) Zambia cases daily growth factor Zambia cases daily growth factor (rolling mean) Zambia estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 R & growth factor (based on deaths) Zambia deaths daily growth factor Zambia deaths daily growth factor (rolling mean) Zambia estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20000 40000 cases doubling time [days] Zambia doubling time cases (rolling mean) Zambia doubling time deaths (rolling mean) 0.0 91.9 183.8 275.8 367.7 daily change Zambia new cases (rolling 7d mean) Zambia new cases 0.000 0.919 1.838 2.758 daily change Zambia new deaths (rolling 7d mean) Zambia new deaths 0 1344 2688 deaths doubling time [days]
In [4]:
overview("Zambia");
2023-03-07T09:40:13.351222 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 7-day incidence rate (per 100K people) 1.7 Zambia, 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 0.1 0.2 0.3 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 4 4 R & growth factor (based on cases) Zambia cases daily growth factor Zambia cases daily growth factor (rolling mean) Zambia 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 2 2 4 4 6 6 R & growth factor (based on deaths) Zambia deaths daily growth factor Zambia deaths daily growth factor (rolling mean) Zambia 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 100000 200000 300000 400000 cases doubling time [days] Zambia doubling time cases (rolling mean) Zambia doubling time deaths (rolling mean) 0 1838 3677 5515 daily change Zambia new cases (rolling 7d mean) Zambia new cases 0.00 18.38 36.77 55.15 daily change Zambia new deaths (rolling 7d mean) Zambia new deaths 0 1079 2157 3236 4315 deaths doubling time [days]
In [5]:
compare_plot("Zambia", normalise=True);
2023-03-07T09:40:17.109956 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 Zambia Zambia 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) Zambia Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Zambia")

# get population of the region for future normalisation:
inhabitants = population("Zambia")
print(f'Population of "Zambia": {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 "Zambia": 18383956 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 343135 0 4057 0
2023-03-05 343135 56 4057 0
2023-03-04 343079 0 4057 0
2023-03-03 343079 0 4057 0
2023-03-02 343079 67 4057 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: 0:00:15.792021