Taiwan*¶

  • 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 09:31:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Taiwan*", weeks=5);
2023-01-26T09:31:50.412591 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 500 500 600 600 700 700 7-day incidence rate (per 100K people) 499.3 Taiwan*, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 50 100 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.1 0.2 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 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) Taiwan* cases daily growth factor Taiwan* cases daily growth factor (rolling mean) Taiwan* estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on deaths) Taiwan* deaths daily growth factor Taiwan* deaths daily growth factor (rolling mean) Taiwan* estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 200 400 cases doubling time [days] Taiwan* doubling time cases (rolling mean) Taiwan* doubling time deaths (rolling mean) 0 11908 23817 daily change Taiwan* new cases (rolling 7d mean) Taiwan* new cases 0.00 23.82 47.63 daily change Taiwan* new deaths (rolling 7d mean) Taiwan* new deaths 0.0 229.4 458.7 deaths doubling time [days]
In [4]:
overview("Taiwan*");
2023-01-26T09:31:58.544597 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 1000 1000 2000 2000 7-day incidence rate (per 100K people) 499.3 Taiwan*, 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 0 200 400 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 0.00 0.25 0.50 0.75 1.00 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 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases)