Costa Rica¶

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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 09:33:17 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Costa Rica", weeks=5);
2023-03-07T09:33:21.650747 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 25 25 50 50 75 75 100 100 7-day incidence rate (per 100K people) Costa Rica, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 25 50 75 100 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 0.4 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 1.4 1.4 R & growth factor (based on cases) Costa Rica cases daily growth factor Costa Rica cases daily growth factor (rolling mean) Costa Rica estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Costa Rica deaths daily growth factor Costa Rica deaths daily growth factor (rolling mean) Costa Rica estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 cases doubling time [days] Costa Rica doubling time cases (rolling mean) Costa Rica doubling time deaths (rolling mean) 0 1274 2547 3821 5094 daily change Costa Rica new cases (rolling 7d mean) Costa Rica new cases 0.00 5.09 10.19 15.28 20.38 daily change Costa Rica new deaths (rolling 7d mean) Costa Rica new deaths 0 1002 2004 3007 deaths doubling time [days]
In [4]:
overview("Costa Rica");
2023-03-07T09:33:30.504178 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 1000 1000 2000 2000 7-day incidence rate (per 100K people) 100.3 Costa Rica, 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 0 250 500 750 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 May 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 May 23 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Costa Rica cases daily growth factor Costa Rica cases daily growth factor (rolling mean) Costa Rica 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 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Costa Rica deaths daily growth factor Costa Rica deaths daily growth factor (rolling mean) Costa Rica 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 2000 4000 6000 8000 cases doubling time [days] Costa Rica doubling time cases (rolling mean) Costa Rica doubling time deaths (rolling mean) 0 12735 25471 38206 50941 daily change Costa Rica new cases (rolling 7d mean) Costa Rica new cases 0.0 50.9 101.9 daily change Costa Rica new deaths (rolling 7d mean) Costa Rica new deaths 0 5066 10132 15197 20263 deaths doubling time [days]
In [5]:
compare_plot("Costa Rica", normalise=True);
2023-03-07T09:33:34.570476 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 Costa Rica Costa Rica 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) Costa Rica Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Costa Rica")

# get population of the region for future normalisation:
inhabitants = population("Costa Rica")
print(f'Population of "Costa Rica": {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 "Costa Rica": 5094114 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 1204164 0 9230 0
2023-03-05 1204164 0 9230 0
2023-03-04 1204164 0 9230 0
2023-03-03 1204164 0 9230 0
2023-03-02 1204164 0 9230 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: 0:00:17.783645