Serbia¶

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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 09:37:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Serbia", weeks=5);
2023-03-07T09:37:51.209851 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 70 70 7-day incidence rate (per 100K people) 63.5 Serbia, 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 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.05 0.10 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) Serbia cases daily growth factor Serbia cases daily growth factor (rolling mean) Serbia estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Serbia deaths daily growth factor Serbia deaths daily growth factor (rolling mean) Serbia estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Serbia doubling time cases (rolling mean) Serbia doubling time deaths (rolling mean) 0 437 874 1311 1747 daily change Serbia new cases (rolling 7d mean) Serbia new cases 0.00 4.37 8.74 daily change Serbia new deaths (rolling 7d mean) Serbia new deaths 0 1678 3356 5034 deaths doubling time [days]
In [4]:
overview("Serbia");
2023-03-07T09:38:00.195936 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 63.5 Serbia, 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 0 100 200 300 400 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Serbia cases daily growth factor Serbia cases daily growth factor (rolling mean) Serbia 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Serbia deaths daily growth factor Serbia deaths daily growth factor (rolling mean) Serbia 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 8000 cases doubling time [days] Serbia doubling time cases (rolling mean) Serbia doubling time deaths (rolling mean) 0 8737 17475 26212 34949 daily change Serbia new cases (rolling 7d mean) Serbia new cases 0.0 43.7 87.4 daily change Serbia new deaths (rolling 7d mean) Serbia new deaths 0 2651 5303 7954 10606 deaths doubling time [days]
In [5]:
compare_plot("Serbia", normalise=True);
2023-03-07T09:38:04.647153 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 Serbia Serbia 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) Serbia Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Serbia")

# get population of the region for future normalisation:
inhabitants = population("Serbia")
print(f'Population of "Serbia": {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 "Serbia": 8737370 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 2497248 953 17864 9
2023-03-05 2496295 487 17855 8
2023-03-04 2495808 623 17847 3
2023-03-03 2495185 804 17844 5
2023-03-02 2494381 1818 17839 13
... ... ... ... ...
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: 0:00:17.686853