Czechia¶

  • 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 09:26:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Czechia", weeks=5);
2023-01-26T09:26:45.986516 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 20 20 30 30 40 40 7-day incidence rate (per 100K people) 18.3 Czechia, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 2 4 6 8 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.00 0.05 0.10 0.15 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) Czechia cases daily growth factor Czechia cases daily growth factor (rolling mean) Czechia 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) Czechia deaths daily growth factor Czechia deaths daily growth factor (rolling mean) Czechia estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 5000 10000 15000 20000 cases doubling time [days] Czechia doubling time cases (rolling mean) Czechia doubling time deaths (rolling mean) 0 214 428 643 857 daily change Czechia new cases (rolling 7d mean) Czechia new cases 0.00 5.35 10.71 16.06 daily change Czechia new deaths (rolling 7d mean) Czechia new deaths 0 5000 10000 15000 20000 deaths doubling time [days]
In [4]:
overview("Czechia");
2023-01-26T09:26:54.386067 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) 18.3 Czechia, 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 0 200 400 600 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)