Chile¶

  • 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 09:26:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Chile", weeks=5);
2023-01-26T09:26:25.059409 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 80 80 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 78.6 Chile, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 10 20 30 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.1 0.2 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) Chile cases daily growth factor Chile cases daily growth factor (rolling mean) Chile 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) Chile deaths daily growth factor Chile deaths daily growth factor (rolling mean) Chile estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 1000 2000 cases doubling time [days] Chile doubling time cases (rolling mean) Chile doubling time deaths (rolling mean) 0 1912 3823 5735 daily change Chile new cases (rolling 7d mean) Chile new cases 0.00 19.12 38.23 daily change Chile new deaths (rolling 7d mean) Chile new deaths 0 1349 2699 deaths doubling time [days]
In [4]:
overview("Chile");
2023-01-26T09:26:32.618208 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 0 500 500 1000 1000 7-day incidence rate (per 100K people) 78.6 Chile, 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 50 100 150 200 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 20 40 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)