Vietnam¶

  • 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 09:32:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Vietnam", weeks=5);
2023-01-26T09:32:46.087616 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.5 0.5 1.0 1.0 1.5 1.5 7-day incidence rate (per 100K people) 0.1 Vietnam, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.1 0.2 0.3 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0000 0.0005 0.0010 0.0015 0.0020 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Vietnam cases daily growth factor Vietnam cases daily growth factor (rolling mean) Vietnam estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Vietnam deaths daily growth factor Vietnam deaths daily growth factor (rolling mean) Vietnam estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 200000 400000 600000 800000 cases doubling time [days] Vietnam doubling time cases (rolling mean) 0.0 97.3 194.7 292.0 daily change Vietnam new cases (rolling 7d mean) Vietnam new cases 0.000 0.487 0.973 1.460 1.947 daily change Vietnam new deaths (rolling 7d mean) Vietnam new deaths 0.000 0.213 0.426 0.639 0.852
In [4]:
overview("Vietnam");
2023-01-26T09:32:53.811761 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 0.1 Vietnam, 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 0 100 200 300 400 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 0.0 0.2 0.4 0.6 0.8 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)