Philippines¶

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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 09:37:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Philippines", weeks=5);
2023-03-07T09:37:23.972932 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 7-day incidence rate (per 100K people) 0.8 Philippines, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.05 0.10 0.15 0.20 daily change normalised per 100K 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Philippines cases daily growth factor Philippines cases daily growth factor (rolling mean) Philippines estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Philippines deaths daily growth factor Philippines deaths daily growth factor (rolling mean) Philippines estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 30000 40000 cases doubling time [days] Philippines doubling time cases (rolling mean) Philippines doubling time deaths (rolling mean) 0.0 54.8 109.6 164.4 219.2 daily change Philippines new cases (rolling 7d mean) Philippines new cases 0.00 5.48 10.96 16.44 21.92 daily change Philippines new deaths (rolling 7d mean) Philippines new deaths 0 5463 10925 16388 21851 deaths doubling time [days]
In [4]:
overview("Philippines");
2023-03-07T09:37:33.204699 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) 0.8 Philippines, 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 10 20 30 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 0.1 0.2 0.3 0.4 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 May 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Philippines cases daily growth factor Philippines cases daily growth factor (rolling mean) Philippines 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) Philippines deaths daily growth factor Philippines deaths daily growth factor (rolling mean) Philippines 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 10000 20000 30000 40000 cases doubling time [days] Philippines doubling time cases (rolling mean) Philippines doubling time deaths (rolling mean) 0 10958 21916 32874 daily change Philippines new cases (rolling 7d mean) Philippines new cases 0.0 109.6 219.2 328.7 438.3 daily change Philippines new deaths (rolling 7d mean) Philippines new deaths 0 16788 33576 50364 67152 deaths doubling time [days]
In [5]:
compare_plot("Philippines", normalise=True);
2023-03-07T09:37:37.123947 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 Philippines Philippines 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) Philippines Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Philippines")

# get population of the region for future normalisation:
inhabitants = population("Philippines")
print(f'Population of "Philippines": {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 "Philippines": 109581085 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4077109 107 66160 7
2023-03-05 4077002 136 66153 10
2023-03-04 4076866 162 66143 10
2023-03-03 4076704 134 66133 7
2023-03-02 4076570 152 66126 8
... ... ... ... ...
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: 0:00:17.511176