Lebanon¶

  • 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 09:28:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Lebanon", weeks=5);
2023-01-26T09:28:57.532976 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 25 25 7-day incidence rate (per 100K people) Lebanon, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 2 4 6 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.00 0.02 0.04 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Lebanon cases daily growth factor Lebanon cases daily growth factor (rolling mean) Lebanon 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 2.5 2.5 R & growth factor (based on deaths) Lebanon deaths daily growth factor Lebanon deaths daily growth factor (rolling mean) Lebanon estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 5000 10000 15000 cases doubling time [days] Lebanon doubling time cases (rolling mean) Lebanon doubling time deaths (rolling mean) 0.0 136.5 273.0 409.5 daily change Lebanon new cases (rolling 7d mean) Lebanon new cases 0.000 1.365 2.730 daily change Lebanon new deaths (rolling 7d mean) Lebanon new deaths 0 2170 4341 6511 deaths doubling time [days]
In [4]:
overview("Lebanon");
2023-01-26T09:29:05.686506 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 0 200 200 400 400 600 600 800 800 7-day incidence rate (per 100K people) 25.6 Lebanon, 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 2 4 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases)