Mexico¶

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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 09:36:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Mexico", weeks=5);
2023-03-07T09:36:11.025217 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) 15.4 Mexico, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 2.5 5.0 7.5 10.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.000 0.025 0.050 0.075 0.100 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) Mexico cases daily growth factor Mexico cases daily growth factor (rolling mean) Mexico estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Mexico deaths daily growth factor Mexico deaths daily growth factor (rolling mean) Mexico estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Mexico doubling time cases (rolling mean) Mexico doubling time deaths (rolling mean) 0 3195 6390 9584 12779 daily change Mexico new cases (rolling 7d mean) Mexico new cases 0.0 31.9 63.9 95.8 127.8 daily change Mexico new deaths (rolling 7d mean) Mexico new deaths 0 5365 10731 16096 deaths doubling time [days]
In [4]:
overview("Mexico");
2023-03-07T09:36:20.347552 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 0 100 100 200 200 7-day incidence rate (per 100K people) 15.4 Mexico, 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 60 80 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Mexico cases daily growth factor Mexico cases daily growth factor (rolling mean) Mexico 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on deaths) Mexico deaths daily growth factor Mexico deaths daily growth factor (rolling mean) Mexico 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 cases doubling time [days] Mexico doubling time cases (rolling mean) Mexico doubling time deaths (rolling mean) 0 25558 51117 76675 102234 daily change Mexico new cases (rolling 7d mean) Mexico new cases 0 1278 2556 3834 daily change Mexico new deaths (rolling 7d mean) Mexico new deaths 0 214387 428775 643162 deaths doubling time [days]
In [5]:
compare_plot("Mexico", normalise=True);
2023-03-07T09:36:24.718187 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 Mexico Mexico 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) Mexico Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Mexico")

# get population of the region for future normalisation:
inhabitants = population("Mexico")
print(f'Population of "Mexico": {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 "Mexico": 127792286 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 7470653 0 333100 0
2023-03-05 7470653 525 333100 4
2023-03-04 7470128 10268 333096 58
2023-03-03 7459860 0 333038 0
2023-03-02 7459860 0 333038 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: 0:00:18.278582