Israel¶

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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 09:34:46 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Israel", weeks=5);
2023-03-07T09:34:50.537129 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 35 35 40 40 45 45 50 50 7-day incidence rate (per 100K people) 33.0 Israel, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.05 0.10 0.15 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) Israel cases daily growth factor Israel cases daily growth factor (rolling mean) Israel estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Israel deaths daily growth factor Israel deaths daily growth factor (rolling mean) Israel estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 cases doubling time [days] Israel doubling time cases (rolling mean) Israel doubling time deaths (rolling mean) 0 433 866 1298 daily change Israel new cases (rolling 7d mean) Israel new cases 0.00 4.33 8.66 12.98 daily change Israel new deaths (rolling 7d mean) Israel new deaths 0 3423 6845 deaths doubling time [days]
In [4]:
overview("Israel");
2023-03-07T09:34:59.023373 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 2000 2000 4000 4000 6000 6000 8000 8000 7-day incidence rate (per 100K people) 33.0 Israel, 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 1000 2000 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Israel cases daily growth factor Israel cases daily growth factor (rolling mean) Israel 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 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Israel deaths daily growth factor Israel deaths daily growth factor (rolling mean) Israel 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] Israel doubling time cases (rolling mean) Israel doubling time deaths (rolling mean) 0 86555 173111 daily change Israel new cases (rolling 7d mean) Israel new cases 0.0 43.3 86.6 daily change Israel new deaths (rolling 7d mean) Israel new deaths 0 3315 6629 9944 deaths doubling time [days]
In [5]:
compare_plot("Israel", normalise=True);
2023-03-07T09:35:03.452539 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 Israel Israel 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) Israel Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Israel")

# get population of the region for future normalisation:
inhabitants = population("Israel")
print(f'Population of "Israel": {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 "Israel": 8655541 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4801502 0 12307 0
2023-03-05 4801502 281 12307 4
2023-03-04 4801221 317 12303 0
2023-03-03 4800904 516 12303 0
2023-03-02 4800388 757 12303 11
... ... ... ... ...
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: 0:00:17.339748