Australia¶

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  • 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 09:31:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Australia", weeks=5);
2023-03-07T09:31:51.245058 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 7-day incidence rate (per 100K people) 75.3 Australia, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 daily change normalised per 100K 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 cases) Australia cases daily growth factor Australia cases daily growth factor (rolling mean) Australia estimated R (using cases) 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 R & growth factor (based on deaths) Australia deaths daily growth factor Australia deaths daily growth factor (rolling mean) Australia estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Australia doubling time cases (rolling mean) Australia doubling time deaths (rolling mean) 0 5092 10184 15276 daily change Australia new cases (rolling 7d mean) Australia new cases 0.0 50.9 101.8 152.8 203.7 daily change Australia new deaths (rolling 7d mean) Australia new deaths 0 2000 4000 6000 deaths doubling time [days]
In [4]:
overview("Australia");
2023-03-07T09:31:59.776672 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 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 75.3 Australia, 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 0 200 400 600 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 1.5 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 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Australia cases daily growth factor Australia cases daily growth factor (rolling mean) Australia 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Australia deaths daily growth factor Australia deaths daily growth factor (rolling mean) Australia 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 2000 4000 6000 cases doubling time [days] Australia doubling time cases (rolling mean) Australia doubling time deaths (rolling mean) 0 50919 101839 152758 daily change Australia new cases (rolling 7d mean) Australia new cases 0.0 127.3 254.6 381.9 daily change Australia new deaths (rolling 7d mean) Australia new deaths 0 2000 4000 6000 deaths doubling time [days]
In [5]:
compare_plot("Australia", normalise=True);
2023-03-07T09:32:03.932944 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 Australia Australia Germany 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) Australia Germany Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Australia")

# get population of the region for future normalisation:
inhabitants = population("Australia")
print(f'Population of "Australia": {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 "Australia": 25459700 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 11385534 0 19459 0
2023-03-05 11385534 0 19459 0
2023-03-04 11385534 0 19459 0
2023-03-03 11385534 633 19459 1
2023-03-02 11384901 18540 19458 85
... ... ... ... ...
2020-01-27 5 1 0 0
2020-01-26 4 4 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: 0:00:17.307454