Malaysia¶

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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 09:36:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("Malaysia", weeks=5);
2023-03-07T09:36:04.909308 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 4.5 4.5 5.0 5.0 5.5 5.5 6.0 6.0 7-day incidence rate (per 100K people) 4.3 Malaysia, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 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 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) Malaysia cases daily growth factor Malaysia cases daily growth factor (rolling mean) Malaysia estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 R & growth factor (based on deaths) Malaysia deaths daily growth factor Malaysia deaths daily growth factor (rolling mean) Malaysia estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 15000 20000 cases doubling time [days] Malaysia doubling time cases (rolling mean) Malaysia doubling time deaths (rolling mean) 0.0 80.9 161.8 242.7 323.7 daily change Malaysia new cases (rolling 7d mean) Malaysia new cases 0.000 1.618 3.237 4.855 daily change Malaysia new deaths (rolling 7d mean) Malaysia new deaths 0 11302 22603 33905 45206 deaths doubling time [days]
In [4]:
overview("Malaysia");
2023-03-07T09:36:13.850281 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 0 200 200 400 400 600 600 7-day incidence rate (per 100K people) 4.3 Malaysia, 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 25 50 75 100 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 0.5 1.0 1.5 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) Malaysia cases daily growth factor Malaysia cases daily growth factor (rolling mean) Malaysia 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 2 2 4 4 6 6 R & growth factor (based on deaths) Malaysia deaths daily growth factor Malaysia deaths daily growth factor (rolling mean) Malaysia 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 cases doubling time [days] Malaysia doubling time cases (rolling mean) Malaysia doubling time deaths (rolling mean) 0 8091 16183 24274 32366 daily change Malaysia new cases (rolling 7d mean) Malaysia new cases 0.0 161.8 323.7 485.5 daily change Malaysia new deaths (rolling 7d mean) Malaysia new deaths 0 14903 29806 deaths doubling time [days]
In [5]:
compare_plot("Malaysia", normalise=True);
2023-03-07T09:36:18.102803 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 Malaysia Malaysia 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) Malaysia Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("Malaysia")

# get population of the region for future normalisation:
inhabitants = population("Malaysia")
print(f'Population of "Malaysia": {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 "Malaysia": 32365998 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 5043978 188 36966 0
2023-03-05 5043790 164 36966 1
2023-03-04 5043626 170 36965 0
2023-03-03 5043456 204 36965 0
2023-03-02 5043252 244 36965 5
... ... ... ... ...
2020-01-27 4 0 0 0
2020-01-26 4 0 0 0
2020-01-25 4 4 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: 0:00:17.168421