Kazakhstan¶

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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 09:35:31 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Kazakhstan", weeks=5);
2023-03-07T09:35:35.290828 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 7-day incidence rate (per 100K people) 0.0 Kazakhstan, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2 4 6 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.002 0.004 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on cases) Kazakhstan cases daily growth factor Kazakhstan cases daily growth factor (rolling mean) Kazakhstan estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Kazakhstan deaths daily growth factor Kazakhstan deaths daily growth factor (rolling mean) Kazakhstan estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Kazakhstan doubling time cases (rolling mean) 0 376 751 1127 daily change Kazakhstan new cases (rolling 7d mean) Kazakhstan new cases 0.000 0.376 0.751 daily change Kazakhstan new deaths (rolling 7d mean) Kazakhstan new deaths 0.000 0.220 0.441 0.661 0.882
In [4]:
overview("Kazakhstan");
2023-03-07T09:35:43.912446 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 0 200 200 400 400 7-day incidence rate (per 100K people) 0.0 Kazakhstan, 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 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 2.5 5.0 7.5 10.0 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 0 1 1 2 2 3 3 4 4 R & growth factor (based on cases) Kazakhstan cases daily growth factor Kazakhstan cases daily growth factor (rolling mean) Kazakhstan 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 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Kazakhstan deaths daily growth factor Kazakhstan deaths daily growth factor (rolling mean) Kazakhstan 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 100000 200000 cases doubling time [days] Kazakhstan doubling time cases (rolling mean) Kazakhstan doubling time deaths (rolling mean) 0 18777 37553 56330 daily change Kazakhstan new cases (rolling 7d mean) Kazakhstan new cases 0 469 939 1408 1878 daily change Kazakhstan new deaths (rolling 7d mean) Kazakhstan new deaths 0 6950 13900 deaths doubling time [days]
In [5]:
compare_plot("Kazakhstan", normalise=True);
2023-03-07T09:35:47.777100 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 Kazakhstan Kazakhstan 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) Kazakhstan Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Kazakhstan")

# get population of the region for future normalisation:
inhabitants = population("Kazakhstan")
print(f'Population of "Kazakhstan": {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 "Kazakhstan": 18776707 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 1498668 0 19069 0
2023-03-05 1498668 0 19069 0
2023-03-04 1498668 0 19069 0
2023-03-03 1498668 0 19069 0
2023-03-02 1498668 0 19069 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: 0:00:16.736793