Trinidad and Tobago¶

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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 09:38:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Trinidad and Tobago", weeks=5);
2023-03-07T09:39:03.709514 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 10 10 20 20 30 30 40 40 7-day incidence rate (per 100K people) 39.9 Trinidad and Tobago, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 R & growth factor (based on cases) Trinidad and Tobago cases daily growth factor Trinidad and Tobago cases daily growth factor (rolling mean) Trinidad and Tobago estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 R & growth factor (based on deaths) Trinidad and Tobago deaths daily growth factor Trinidad and Tobago deaths daily growth factor (rolling mean) Trinidad and Tobago 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] Trinidad and Tobago doubling time cases (rolling mean) Trinidad and Tobago doubling time deaths (rolling mean) 0.0 139.9 279.9 419.8 daily change Trinidad and Tobago new cases (rolling 7d mean) Trinidad and Tobago new cases 0.000 2.799 5.598 daily change Trinidad and Tobago new deaths (rolling 7d mean) Trinidad and Tobago new deaths 0 1208 2416 3624 4832 deaths doubling time [days]
In [4]:
overview("Trinidad and Tobago");
2023-03-07T09:39:13.219853 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 0 100 100 200 200 300 300 400 400 7-day incidence rate (per 100K people) 39.9 Trinidad and Tobago, 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 20 40 60 80 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 1 2 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 May 23 2 2 4 4 6 6 R & growth factor (based on cases) Trinidad and Tobago cases daily growth factor Trinidad and Tobago cases daily growth factor (rolling mean) Trinidad and Tobago estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 2 2 4 4 6 6 R & growth factor (based on deaths) Trinidad and Tobago deaths daily growth factor Trinidad and Tobago deaths daily growth factor (rolling mean) Trinidad and Tobago estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 2500 5000 7500 10000 cases doubling time [days] Trinidad and Tobago doubling time cases (rolling mean) Trinidad and Tobago doubling time deaths (rolling mean) 0 280 560 840 1120 daily change Trinidad and Tobago new cases (rolling 7d mean) Trinidad and Tobago new cases 0.00 13.99 27.99 41.98 daily change Trinidad and Tobago new deaths (rolling 7d mean) Trinidad and Tobago new deaths 0 1126 2253 3379 4505 deaths doubling time [days]
In [5]:
compare_plot("Trinidad and Tobago", normalise=True);
2023-03-07T09:39:17.613229 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.001 0.001 0.1 0.1 10 10 1000 1000 daily new cases per 100K people (rolling 7-day mean) Daily cases (top) and deaths (below) for Trinidad and Tobago Trinidad and Tobago Germany Australia Poland Korea, South Belarus Switzerland US 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.0001 0.0001 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths per 100K people (rolling 7-day mean) Trinidad and Tobago Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Trinidad and Tobago")

# get population of the region for future normalisation:
inhabitants = population("Trinidad and Tobago")
print(f'Population of "Trinidad and Tobago": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table

Population of "Trinidad and Tobago": 1399491 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 189296 0 4347 0
2023-03-05 189296 0 4347 0
2023-03-04 189296 0 4347 0
2023-03-03 189296 0 4347 0
2023-03-02 189296 0 4347 0
... ... ... ... ...
2020-01-27 0 0 0 0
2020-01-26 0 0 0 0
2020-01-25 0 0 0 0
2020-01-24 0 0 0 0
2020-01-23 0 0 0 0

1139 rows × 4 columns

Explore the data in your web browser¶

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Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)
In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")

Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")

Notebook execution took: 0:00:18.271965