Romania¶

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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 09:37:57 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Romania", weeks=5);
2023-03-07T09:38:01.564737 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 15 15 20 20 25 25 30 30 7-day incidence rate (per 100K people) Romania, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 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 0.20 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) Romania cases daily growth factor Romania cases daily growth factor (rolling mean) Romania estimated R (using cases) 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 deaths) Romania deaths daily growth factor Romania deaths daily growth factor (rolling mean) Romania estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Romania doubling time cases (rolling mean) Romania doubling time deaths (rolling mean) 0 1924 3848 daily change Romania new cases (rolling 7d mean) Romania new cases 0.00 9.62 19.24 28.86 38.48 daily change Romania new deaths (rolling 7d mean) Romania new deaths 0 4534 9068 13602 deaths doubling time [days]
In [4]:
overview("Romania");
2023-03-07T09:38:10.483385 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 0 250 250 500 500 750 750 1000 1000 7-day incidence rate (per 100K people) 29.7 Romania, 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 50 100 150 200 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Romania cases daily growth factor Romania cases daily growth factor (rolling mean) Romania 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 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Romania deaths daily growth factor Romania deaths daily growth factor (rolling mean) Romania 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] Romania doubling time cases (rolling mean) Romania doubling time deaths (rolling mean) 0 9619 19238 28857 38475 daily change Romania new cases (rolling 7d mean) Romania new cases 0.0 192.4 384.8 577.1 daily change Romania new deaths (rolling 7d mean) Romania new deaths 0 17895 35790 53685 deaths doubling time [days]
In [5]:
compare_plot("Romania", normalise=True);
2023-03-07T09:38:14.793663 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 Romania Romania 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) Romania Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Romania")

# get population of the region for future normalisation:
inhabitants = population("Romania")
print(f'Population of "Romania": {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 "Romania": 19237682 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 3346046 5704 67736 32
2023-03-05 3340342 0 67704 0
2023-03-04 3340342 0 67704 0
2023-03-03 3340342 0 67704 0
2023-03-02 3340342 0 67704 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: 0:00:18.098144