Malaysia¶

  • 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 09:29:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Malaysia", weeks=5);
2023-01-26T09:29:26.623580 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 20 20 7-day incidence rate (per 100K people) 4.9 Malaysia, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 1 2 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.00 0.01 0.02 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Malaysia cases daily growth factor Malaysia cases daily growth factor (rolling mean) Malaysia estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Malaysia deaths daily growth factor Malaysia deaths daily growth factor (rolling mean) Malaysia estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 5000 10000 15000 20000 cases doubling time [days] Malaysia doubling time cases (rolling mean) Malaysia doubling time deaths (rolling mean) 0.0 323.7 647.3 daily change Malaysia new cases (rolling 7d mean) Malaysia new cases 0.000 3.237 6.473 daily change Malaysia new deaths (rolling 7d mean) Malaysia new deaths 0 5829 11659 17488 23318 deaths doubling time [days]
In [4]:
overview("Malaysia");
2023-01-26T09:29:34.888617 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) 4.9 Malaysia, 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 25 50 75 100 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 0.5 1.0 1.5 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 1.0 1.0 1.2 1.2 R & growth factor (based on cases)