United States: South Carolina¶

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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 16:45:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="South Carolina", 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()
2023-01-26T16:45:22.073505 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 0 100 100 200 200 300 300 400 400 7-day incidence rate (per 100K people) 293.0 South Carolina, US, 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 1 2 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases) United States: South Carolina cases daily growth factor United States: South Carolina cases daily growth factor (rolling mean) United States: South Carolina estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) United States: South Carolina deaths daily growth factor United States: South Carolina deaths daily growth factor (rolling mean) United States: South Carolina estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 500 1000 cases doubling time [days] United States: South Carolina doubling time cases (rolling mean) United States: South Carolina doubling time deaths (rolling mean) 0 2574 5149 7723 10297 daily change United States: South Carolina new cases (rolling 7d mean) United States: South Carolina new cases 0.0 51.5 103.0 daily change United States: South Carolina new deaths (rolling 7d mean) United States: South Carolina new deaths 0 2770 5539 deaths doubling time [days]
In [4]:
overview(country="US", region="South Carolina");

/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()
2023-01-26T16:45:30.428188 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 1000 1000 2000 2000 7-day incidence rate (per 100K people) 293.0 South Carolina, US, 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 0 500 1000 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 4 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 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on cases) United States: South Carolina cases daily growth factor United States: South Carolina cases daily growth factor (rolling mean) United States: South Carolina estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21