Latvia¶

  • 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 09:29:03 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Latvia", weeks=5);
2023-01-26T09:29:06.492000 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 25 25 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 8.0 Latvia, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 10 20 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Latvia cases daily growth factor Latvia cases daily growth factor (rolling mean) Latvia 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) Latvia deaths daily growth factor Latvia deaths daily growth factor (rolling mean) Latvia estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 10000 20000 30000 cases doubling time [days] Latvia doubling time cases (rolling mean) Latvia doubling time deaths (rolling mean) 0.0 188.6 377.2 daily change Latvia new cases (rolling 7d mean) Latvia new cases 0.00 3.77 7.54 daily change Latvia new deaths (rolling 7d mean) Latvia new deaths 0 1521 3041 4562 deaths doubling time [days]
In [4]:
overview("Latvia");
2023-01-26T09:29:14.899260 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) 8.0 Latvia, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)