New Zealand¶

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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 09:37:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("New Zealand", weeks=5);
2023-03-07T09:37:05.016863 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 100 100 200 200 300 300 7-day incidence rate (per 100K people) 237.5 New Zealand, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 150 200 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) New Zealand cases daily growth factor New Zealand cases daily growth factor (rolling mean) New Zealand estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) New Zealand deaths daily growth factor New Zealand deaths daily growth factor (rolling mean) New Zealand estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] New Zealand doubling time cases (rolling mean) New Zealand doubling time deaths (rolling mean) 0 2411 4822 7233 9644 daily change New Zealand new cases (rolling 7d mean) New Zealand new cases 0.00 9.64 19.29 daily change New Zealand new deaths (rolling 7d mean) New Zealand new deaths 0 514 1028 1542 2056 deaths doubling time [days]
In [4]:
overview("New Zealand");
2023-03-07T09:37:14.049029 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) 237.5 New Zealand, 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 0 200 400 600 800 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 1 2 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 1.0 1.0 1.2 1.2 R & growth factor (based on cases) New Zealand cases daily growth factor New Zealand cases daily growth factor (rolling mean) New Zealand 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 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) New Zealand deaths daily growth factor New Zealand deaths daily growth factor (rolling mean) New Zealand 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 50000 100000 150000 200000 cases doubling time [days] New Zealand doubling time cases (rolling mean) New Zealand doubling time deaths (rolling mean) 0 9644 19289 28933 38578 daily change New Zealand new cases (rolling 7d mean) New Zealand new cases 0.0 48.2 96.4 daily change New Zealand new deaths (rolling 7d mean) New Zealand new deaths 0 524 1047 1571 2094 deaths doubling time [days]
In [5]:
compare_plot("New Zealand", normalise=True);
2023-03-07T09:37:18.369766 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 New Zealand New Zealand 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) New Zealand Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("New Zealand")

# get population of the region for future normalisation:
inhabitants = population("New Zealand")
print(f'Population of "New Zealand": {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 "New Zealand": 4822233 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 2236113 2 2550 0
2023-03-05 2236111 11439 2550 6
2023-03-04 2224672 0 2544 0
2023-03-03 2224672 10 2544 0
2023-03-02 2224662 0 2544 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: 0:00:17.561223