Slovenia¶

  • 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 09:31:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Slovenia", weeks=5);
2023-01-26T09:31:41.175158 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 200 200 400 400 600 600 7-day incidence rate (per 100K people) 83.5 Slovenia, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 25 50 75 100 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.00 0.05 0.10 0.15 0.20 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) Slovenia cases daily growth factor Slovenia cases daily growth factor (rolling mean) Slovenia estimated R (using cases) 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 deaths) Slovenia deaths daily growth factor Slovenia deaths daily growth factor (rolling mean) Slovenia estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 2000 4000 cases doubling time [days] Slovenia doubling time cases (rolling mean) Slovenia doubling time deaths (rolling mean) 0 520 1039 1559 2079 daily change Slovenia new cases (rolling 7d mean) Slovenia new cases 0.000 1.039 2.079 3.118 4.158 daily change Slovenia new deaths (rolling 7d mean) Slovenia new deaths 0 1718 3436 deaths doubling time [days]
In [4]:
overview("Slovenia");
2023-01-26T09:31:49.336177 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 2000 2000 4000 4000 7-day incidence rate (per 100K people) 83.5 Slovenia, 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 0 250 500 750 1000 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 1 2 3 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)