Guatemala¶

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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 09:34:24 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Guatemala", weeks=5);
2023-03-07T09:34:28.931998 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 10 10 15 15 20 20 7-day incidence rate (per 100K people) 8.8 Guatemala, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1 2 3 4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.01 0.02 0.03 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Guatemala cases daily growth factor Guatemala cases daily growth factor (rolling mean) Guatemala estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Guatemala deaths daily growth factor Guatemala deaths daily growth factor (rolling mean) Guatemala estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Guatemala doubling time cases (rolling mean) Guatemala doubling time deaths (rolling mean) 0.0 179.2 358.3 537.5 716.6 daily change Guatemala new cases (rolling 7d mean) Guatemala new cases 0.000 1.792 3.583 5.375 daily change Guatemala new deaths (rolling 7d mean) Guatemala new deaths 0 5199 10398 deaths doubling time [days]
In [4]:
overview("Guatemala");
2023-03-07T09:34:37.111178 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) 8.8 Guatemala, 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 0.2 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Guatemala cases daily growth factor Guatemala cases daily growth factor (rolling mean) Guatemala 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 0.50 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Guatemala deaths daily growth factor Guatemala deaths daily growth factor (rolling mean) Guatemala 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 cases doubling time [days] Guatemala doubling time cases (rolling mean) Guatemala doubling time deaths (rolling mean) 0 3583 7166 daily change Guatemala new cases (rolling 7d mean) Guatemala new cases 0.0 35.8 71.7 daily change Guatemala new deaths (rolling 7d mean) Guatemala new deaths 0 5262 10523 deaths doubling time [days]
In [5]:
compare_plot("Guatemala", normalise=True);
2023-03-07T09:34:41.684410 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 Guatemala Guatemala 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) Guatemala Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Guatemala")

# get population of the region for future normalisation:
inhabitants = population("Guatemala")
print(f'Population of "Guatemala": {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 "Guatemala": 17915567 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 1237575 11 20178 0
2023-03-05 1237564 14 20178 0
2023-03-04 1237550 215 20178 1
2023-03-03 1237335 249 20177 1
2023-03-02 1237086 308 20176 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: 0:00:17.504853