Peru¶

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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 09:30:12 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Peru", weeks=5);
2023-01-26T09:30:15.892413 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan −100 −100 0 0 100 100 200 200 7-day incidence rate (per 100K people) 7.6 Peru, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 50 100 150 200 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 0.5 0.5 1.0 1.0 R & growth factor (based on cases) Peru cases daily growth factor Peru cases daily growth factor (rolling mean) Peru 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) Peru deaths daily growth factor Peru deaths daily growth factor (rolling mean) Peru estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 5000 10000 cases doubling time [days] Peru doubling time cases (rolling mean) Peru doubling time deaths (rolling mean) 0 16486 32972 49458 65944 daily change Peru new cases (rolling 7d mean) Peru new cases 0.00 16.49 32.97 49.46 daily change Peru new deaths (rolling 7d mean) Peru new deaths 0 6444 12888 deaths doubling time [days]
In [4]:
overview("Peru");
2023-01-26T09:30:24.857700 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) 7.6 Peru, 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 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 0.5 0.5 1.0 1.0 R & growth factor (based on cases)