Sri Lanka¶

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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 09:38:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Sri Lanka", weeks=5);
2023-03-07T09:38:59.353784 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.03 0.03 0.04 0.04 0.05 0.05 0.06 0.06 7-day incidence rate (per 100K people) 0.0 Sri Lanka, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.005 0.010 0.015 0.020 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.001 0.002 0.003 0.004 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Sri Lanka cases daily growth factor Sri Lanka cases daily growth factor (rolling mean) Sri Lanka estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Sri Lanka deaths daily growth factor Sri Lanka deaths daily growth factor (rolling mean) Sri Lanka estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 200000 400000 600000 cases doubling time [days] Sri Lanka doubling time cases (rolling mean) 0.000 1.071 2.141 3.212 4.283 daily change Sri Lanka new cases (rolling 7d mean) Sri Lanka new cases 0.000 0.214 0.428 0.642 0.857 daily change Sri Lanka new deaths (rolling 7d mean) Sri Lanka new deaths 0.000 0.289 0.578 0.868
In [4]:
overview("Sri Lanka");
2023-03-07T09:39:08.549369 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 50 50 100 100 150 150 200 200 7-day incidence rate (per 100K people) 0.0 Sri Lanka, 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 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 0.0 0.5 1.0 1.5 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Sri Lanka cases daily growth factor Sri Lanka cases daily growth factor (rolling mean) Sri Lanka 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 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Sri Lanka deaths daily growth factor Sri Lanka deaths daily growth factor (rolling mean) Sri Lanka 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 200000 400000 600000 cases doubling time [days] Sri Lanka doubling time cases (rolling mean) Sri Lanka doubling time deaths (rolling mean) 0 4283 8565 daily change Sri Lanka new cases (rolling 7d mean) Sri Lanka new cases 0.0 107.1 214.1 321.2 daily change Sri Lanka new deaths (rolling 7d mean) Sri Lanka new deaths 0 7253 14505 21758 deaths doubling time [days]
In [5]:
compare_plot("Sri Lanka", normalise=True);
2023-03-07T09:39:12.719768 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 Sri Lanka Sri Lanka 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) Sri Lanka Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Sri Lanka")

# get population of the region for future normalisation:
inhabitants = population("Sri Lanka")
print(f'Population of "Sri Lanka": {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 "Sri Lanka": 21413250 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 672034 0 16830 0
2023-03-05 672034 2 16830 0
2023-03-04 672032 0 16830 0
2023-03-03 672032 1 16830 0
2023-03-02 672031 0 16830 0
... ... ... ... ...
2020-01-27 1 1 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: 0:00:17.624076