United States: New Hampshire¶

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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 16:44:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="New Hampshire", 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()
2023-01-26T16:44:54.367231 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 78.5 New Hampshire, US, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 20 40 60 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.2 0.4 0.6 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) United States: New Hampshire cases daily growth factor United States: New Hampshire cases daily growth factor (rolling mean) United States: New Hampshire estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 R & growth factor (based on deaths) United States: New Hampshire deaths daily growth factor United States: New Hampshire deaths daily growth factor (rolling mean) United States: New Hampshire 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: New Hampshire doubling time cases (rolling mean) United States: New Hampshire doubling time deaths (rolling mean) 0 272 544 816 daily change United States: New Hampshire new cases (rolling 7d mean) United States: New Hampshire new cases 0.00 2.72 5.44 8.16 daily change United States: New Hampshire new deaths (rolling 7d mean) United States: New Hampshire new deaths 0 594 1187 1781 2375 deaths doubling time [days]
In [4]:
overview(country="US", region="New Hampshire");

/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()
2023-01-26T16:45:03.242203 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) 78.5 New Hampshire, 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 0 250 500 750 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 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)