Canada¶

  • 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 09:26:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Canada", weeks=5);
2023-01-26T09:26:24.550198 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 20 20 30 30 40 40 50 50 60 60 7-day incidence rate (per 100K people) 33.0 Canada, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 10 20 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.2 0.4 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases) Canada cases daily growth factor Canada cases daily growth factor (rolling mean) Canada estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 1 1 2 2 3 3 R & growth factor (based on deaths) Canada deaths daily growth factor Canada deaths daily growth factor (rolling mean) Canada estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 1000 2000 3000 4000 cases doubling time [days] Canada doubling time cases (rolling mean) Canada doubling time deaths (rolling mean) 0 3825 7649 daily change Canada new cases (rolling 7d mean) Canada new cases 0.0 76.5 153.0 daily change Canada new deaths (rolling 7d mean) Canada new deaths 0 1000 2000 3000 4000 deaths doubling time [days]
In [4]:
overview("Canada");
2023-01-26T09:26:32.741982 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 0 200 200 400 400 600 600 7-day incidence rate (per 100K people) 33.0 Canada, 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 50 100 150 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 0.0 0.2 0.4 0.6 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 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases)