Burma¶

  • 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 09:32:31 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Burma", weeks=5);
2023-03-07T09:32:35.659662 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.02 0.02 0.04 0.04 0.06 0.06 7-day incidence rate (per 100K people) 0.1 Burma, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.01 0.02 0.03 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Burma new deaths (rolling 7d mean) Burma new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Burma cases daily growth factor Burma cases daily growth factor (rolling mean) Burma estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 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 deaths) Burma deaths daily growth factor Burma deaths daily growth factor (rolling mean) Burma estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 100000 200000 300000 cases doubling time [days] Burma doubling time cases (rolling mean) 0.00 5.44 10.88 16.32 daily change Burma new cases (rolling 7d mean) Burma new cases 0.000 0.266 0.533 0.799
In [4]:
overview("Burma");
2023-03-07T09:32:44.336836 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 20 20 40 40 60 60 7-day incidence rate (per 100K people) 0.1 Burma, 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 5 10 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 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 May 23 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases)