Lithuania¶

  • 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 09:29:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Lithuania", weeks=5);
2023-01-26T09:29:12.828279 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 60 60 80 80 100 100 7-day incidence rate (per 100K people) 61.6 Lithuania, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 10 20 30 40 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.2 0.4 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) Lithuania cases daily growth factor Lithuania cases daily growth factor (rolling mean) Lithuania estimated R (using cases) 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 deaths) Lithuania deaths daily growth factor Lithuania deaths daily growth factor (rolling mean) Lithuania estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 2000 4000 6000 cases doubling time [days] Lithuania doubling time cases (rolling mean) Lithuania doubling time deaths (rolling mean) 0 272 544 817 1089 daily change Lithuania new cases (rolling 7d mean) Lithuania new cases 0.00 5.44 10.89 daily change Lithuania new deaths (rolling 7d mean) Lithuania new deaths 0 2382 4765 7147 deaths doubling time [days]
In [4]:
overview("Lithuania");
2023-01-26T09:29:21.568935 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 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 61.6 Lithuania, 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 0 200 400 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 0.5 1.0 1.5 2.0 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) Lithuania cases daily growth factor Lithuania cases daily growth factor (rolling mean) Lithuania estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21