Bangladesh¶

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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 09:31:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Bangladesh", weeks=5);
2023-03-07T09:31:51.430109 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.02 0.02 0.04 0.04 0.06 0.06 7-day incidence rate (per 100K people) 0.0 Bangladesh, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.01 0.02 0.03 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0000 0.0002 0.0004 0.0006 daily change normalised per 100K 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 cases) Bangladesh cases daily growth factor Bangladesh cases daily growth factor (rolling mean) Bangladesh estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Bangladesh deaths daily growth factor Bangladesh deaths daily growth factor (rolling mean) Bangladesh estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 100000 200000 300000 cases doubling time [days] Bangladesh doubling time cases (rolling mean) 0.00 16.47 32.94 49.41 daily change Bangladesh new cases (rolling 7d mean) Bangladesh new cases 0.000 0.329 0.659 0.988 daily change Bangladesh new deaths (rolling 7d mean) Bangladesh new deaths 0.000 0.303 0.606 0.909
In [4]:
overview("Bangladesh");
2023-03-07T09:31:59.900561 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 20 20 40 40 60 60 7-day incidence rate (per 100K people) 0.0 Bangladesh, 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 2 4 6 8 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 0.00 0.05 0.10 0.15 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Bangladesh cases daily growth factor Bangladesh cases daily growth factor (rolling mean) Bangladesh 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 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Bangladesh deaths daily growth factor Bangladesh deaths daily growth factor (rolling mean) Bangladesh 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 100000 200000 300000 cases doubling time [days] Bangladesh doubling time cases (rolling mean) Bangladesh doubling time deaths (rolling mean) 0 3294 6588 9881 13175 daily change Bangladesh new cases (rolling 7d mean) Bangladesh new cases 0.0 82.3 164.7 247.0 daily change Bangladesh new deaths (rolling 7d mean) Bangladesh new deaths 0 9700 19401 29101 deaths doubling time [days]
In [5]:
compare_plot("Bangladesh", normalise=True);
2023-03-07T09:32:04.459973 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 Bangladesh Bangladesh 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) Bangladesh Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Bangladesh")

# get population of the region for future normalisation:
inhabitants = population("Bangladesh")
print(f'Population of "Bangladesh": {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 "Bangladesh": 164689383 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 2037829 0 29445 0
2023-03-05 2037829 0 29445 0
2023-03-04 2037829 0 29445 0
2023-03-03 2037829 0 29445 0
2023-03-02 2037829 0 29445 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: 0:00:17.973955