United States: Washington¶

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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: 07/03/2023 16:41:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="Washington", weeks=5);

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-03-07T16:41:11.268514 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 50 50 100 100 150 150 7-day incidence rate (per 100K people) 59.0 Washington, US, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) United States: Washington cases daily growth factor United States: Washington cases daily growth factor (rolling mean) United States: Washington estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) United States: Washington deaths daily growth factor United States: Washington deaths daily growth factor (rolling mean) United States: Washington estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] United States: Washington doubling time cases (rolling mean) United States: Washington doubling time deaths (rolling mean) 0 1523 3046 4569 daily change United States: Washington new cases (rolling 7d mean) United States: Washington new cases 0.00 15.23 30.46 45.69 60.92 daily change United States: Washington new deaths (rolling 7d mean) United States: Washington new deaths 0 440 880 1320 1760 deaths doubling time [days]
In [4]:
overview(country="US", region="Washington");

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-03-07T16:41:19.921938 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 59.0 Washington, US, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200 400 600 800 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 May 23 0.0 0.5 1.0 1.5 2.0 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 May 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)