Japan¶

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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 09:28:25 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Japan", weeks=5);
2023-01-26T09:28:29.509266 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 400 400 600 600 800 800 1000 1000 7-day incidence rate (per 100K people) 417.6 Japan, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 50 100 150 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.1 0.2 0.3 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 1.4 1.4 R & growth factor (based on cases) Japan cases daily growth factor Japan cases daily growth factor (rolling mean) Japan 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) Japan deaths daily growth factor Japan deaths daily growth factor (rolling mean) Japan estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 100 200 300 400 cases doubling time [days] Japan doubling time cases (rolling mean) Japan doubling time deaths (rolling mean) 0 63238 126476 189715 daily change Japan new cases (rolling 7d mean) Japan new cases 0.0 126.5 253.0 379.4 daily change Japan new deaths (rolling 7d mean) Japan new deaths 0.0 43.4 86.8 130.2 173.6 deaths doubling time [days]
In [4]:
overview("Japan");
2023-01-26T09:28:37.376870 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) 417.6 Japan, 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 0.0 0.1 0.2 0.3 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 1.4 1.4 R & growth factor (based on cases)