Peru¶

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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 09:37:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Peru", weeks=5);
2023-03-07T09:37:11.526852 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 5 5 10 10 7-day incidence rate (per 100K people) 3.6 Peru, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2 4 daily change normalised per 100K 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 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Peru cases daily growth factor Peru cases daily growth factor (rolling mean) Peru estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.25 0.25 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on deaths) Peru deaths daily growth factor Peru deaths daily growth factor (rolling mean) Peru estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20000 40000 60000 cases doubling time [days] Peru doubling time cases (rolling mean) Peru doubling time deaths (rolling mean) 0 659 1319 daily change Peru new cases (rolling 7d mean) Peru new cases 0.00 16.49 32.97 49.46 65.94 daily change Peru new deaths (rolling 7d mean) Peru new deaths 0 20000 40000 60000 deaths doubling time [days]
In [4]:
overview("Peru");
2023-03-07T09:37:20.851125 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 0 500 500 1000 1000 7-day incidence rate (per 100K people) 3.6 Peru, 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 50 100 150 200 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 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Peru cases daily growth factor Peru cases daily growth factor (rolling mean) Peru 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 0.25 0.25 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 R & growth factor (based on deaths) Peru deaths daily growth factor Peru deaths daily growth factor (rolling mean) Peru 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 20000 40000 60000 cases doubling time [days] Peru doubling time cases (rolling mean) Peru doubling time deaths (rolling mean) 0 16486 32972 49458 65944 daily change Peru new cases (rolling 7d mean) Peru new cases 0 330 659 989 daily change Peru new deaths (rolling 7d mean) Peru new deaths 0 49565 99129 148694 deaths doubling time [days]
In [5]:
compare_plot("Peru", normalise=True);
2023-03-07T09:37:25.097548 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 Peru Peru 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) Peru Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Peru")

# get population of the region for future normalisation:
inhabitants = population("Peru")
print(f'Population of "Peru": {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 "Peru": 32971846 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 4486934 102 219513 20
2023-03-05 4486832 0 219493 0
2023-03-04 4486832 167 219493 8
2023-03-03 4486665 1896 219485 7
2023-03-02 4484769 -1513 219478 30
... ... ... ... ...
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: 0:00:17.536503