United States: Connecticut¶

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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 16:42:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="Connecticut", 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()
2023-01-26T16:42:47.600746 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 100 100 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 99.2 Connecticut, US, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 20 40 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.5 1.0 1.5 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 R & growth factor (based on cases) United States: Connecticut cases daily growth factor United States: Connecticut cases daily growth factor (rolling mean) United States: Connecticut estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 1 1 2 2 3 3 R & growth factor (based on deaths) United States: Connecticut deaths daily growth factor United States: Connecticut deaths daily growth factor (rolling mean) United States: Connecticut estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 500 1000 1500 2000 cases doubling time [days] United States: Connecticut doubling time cases (rolling mean) United States: Connecticut doubling time deaths (rolling mean) 0 713 1426 daily change United States: Connecticut new cases (rolling 7d mean) United States: Connecticut new cases 0.00 17.83 35.65 53.48 daily change United States: Connecticut new deaths (rolling 7d mean) United States: Connecticut new deaths 0 2085 4170 6254 8339 deaths doubling time [days]
In [4]:
overview(country="US", region="Connecticut");

/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()
2023-01-26T16:42:55.681329 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 1500 1500 2000 2000 7-day incidence rate (per 100K people) 99.2 Connecticut, 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 0 200 400 600 800 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 0 2 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)