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: 26/01/2023 09:26:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("China", weeks=5);
2023-01-26T09:26:10.170058 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 5 5 10 10 15 15 7-day incidence rate (per 100K people) 1.5 China, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 1 2 3 4 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) China cases daily growth factor China cases daily growth factor (rolling mean) China estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 2 2 4 4 6 6 8 8 10 10 R & growth factor (based on deaths) China deaths daily growth factor China deaths daily growth factor (rolling mean) China estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 500 1000 1500 cases doubling time [days] China doubling time cases (rolling mean) China doubling time deaths (rolling mean) 0 7059 14118 21177 28236 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 0 500 1000 1500 deaths doubling time [days]
In [4]:
overview("China");
2023-01-26T09:26:18.205931 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 10 10 20 20 30 30 7-day incidence rate (per 100K people) 1.5 China, last data point from 2023-01-25 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 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 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 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)