China¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
  • Execute this Jupyter Notebook using myBinder
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 09:32:40 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("China", weeks=5);
2023-03-07T09:32:44.253897 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.00 0.25 0.25 0.50 0.50 0.75 0.75 1.00 1.00 7-day incidence rate (per 100K people) 0.0 China, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0000 0.0001 0.0002 0.0003 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 0.25 0.25 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on cases) China cases daily growth factor China cases daily growth factor (rolling mean) China estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) China deaths daily growth factor China deaths daily growth factor (rolling mean) China estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2 4 6 cases doubling time [days] 1e6 China doubling time cases (rolling mean) China doubling time deaths (rolling mean) 0.000 1.412 2.824 4.235 daily change China new cases (rolling 7d mean) China new cases 0 706 1412 2118 2824 daily change China new deaths (rolling 7d mean) China new deaths 0 23387 46774 70161 deaths doubling time [days]
In [4]:
overview("China");
2023-03-07T09:32:53.097939 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 10 10 20 20 30 30 7-day incidence rate (per 100K people) 0.0 China, 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 2 4 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 4 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 0.25 0.25 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on cases) China cases daily growth factor China cases daily growth factor (rolling mean) China 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 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) China deaths daily growth factor China deaths daily growth factor (rolling mean) China 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 2 4 6 cases doubling time [days] 1e6 China doubling time cases (rolling mean) China doubling time deaths (rolling mean) 0 28236 56471 daily change China new cases (rolling 7d mean) China new cases 0 14118 28236 42353 56471 daily change China new deaths (rolling 7d mean) China new deaths 0 23267 46534 69801 deaths doubling time [days]
In [5]:
compare_plot("China", normalise=True);
2023-03-07T09:32:57.460570 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 0.0001 0.0001 0.01 0.01 1 1 100 100 daily new cases per 100K people (rolling 7-day mean) Daily cases (top) and deaths (below) for China China 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 1e-05 1e-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) China Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("China")

# get population of the region for future normalisation:
inhabitants = population("China")
print(f'Population of "China": {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 "China": 1411778724 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4903524 0 101055 1
2023-03-05 4903524 0 101054 1
2023-03-04 4903524 0 101053 1
2023-03-03 4903524 0 101052 1
2023-03-02 4903524 0 101051 0
... ... ... ... ...
2020-01-27 2877 802 82 26
2020-01-26 2075 669 56 14
2020-01-25 1406 486 42 16
2020-01-24 920 277 26 8
2020-01-23 643 95 18 1

1139 rows × 4 columns

Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

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: 0:00:17.494095