Brazil¶

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  • Plots are explained at http://oscovida.github.io/plots.html
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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 09:25:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Brazil", weeks=5);
2023-01-26T09:26:03.413024 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 42.7 Brazil, 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 0.1 0.2 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) Brazil cases daily growth factor Brazil cases daily growth factor (rolling mean) Brazil estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on deaths) Brazil deaths daily growth factor Brazil deaths daily growth factor (rolling mean) Brazil estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 1000 2000 3000 cases doubling time [days] Brazil doubling time cases (rolling mean) Brazil doubling time deaths (rolling mean) 0 21256 42512 63768 85024 daily change Brazil new cases (rolling 7d mean) Brazil new cases 0.0 212.6 425.1 daily change Brazil new deaths (rolling 7d mean) Brazil new deaths 0 3605 7211 10816 deaths doubling time [days]
In [4]:
overview("Brazil");
2023-01-26T09:26:11.191038 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 0 200 200 400 400 600 600 7-day incidence rate (per 100K people) 42.7 Brazil, 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 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 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)