Germany: SK Dresden (Sachsen)¶

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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 15:27:25 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Dresden", weeks=5);
2023-03-07T15:27:40.877318 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 7-day incidence rate (per 100K people) 33.5 SK Dresden, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 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 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 R & growth factor (based on cases) Germany-SK Dresden cases daily growth factor Germany-SK Dresden cases daily growth factor (rolling mean) Germany-SK Dresden estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Dresden deaths daily growth factor Germany-SK Dresden deaths daily growth factor (rolling mean) Germany-SK Dresden estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Dresden doubling time cases (rolling mean) 0.0 27.8 55.5 83.3 daily change Germany-SK Dresden new cases (rolling 7d mean) Germany-SK Dresden new cases 0.000 0.278 0.555 0.833 daily change Germany-SK Dresden new deaths (rolling 7d mean) Germany-SK Dresden new deaths 0.000 0.218 0.436 0.654 0.872
In [4]:
overview(country="Germany", subregion="SK Dresden");
2023-03-07T15:27:57.169885 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 33.5 SK Dresden, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Dresden cases daily growth factor Germany-SK Dresden cases daily growth factor (rolling mean) Germany-SK Dresden estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Dresden deaths daily growth factor Germany-SK Dresden deaths daily growth factor (rolling mean) Germany-SK Dresden estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Dresden doubling time cases (rolling mean) Germany-SK Dresden doubling time deaths (rolling mean) 0 1111 2221 daily change Germany-SK Dresden new cases (rolling 7d mean) Germany-SK Dresden new cases 0.00 11.11 22.21 33.32 daily change Germany-SK Dresden new deaths (rolling 7d mean) Germany-SK Dresden new deaths 0 560 1119 1679 2238 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Dresden", dates="2020-03-15:");
2023-03-07T15:30:49.988737 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: SK Dresden SK Dresden Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people SK Dresden Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Dresden")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Dresden")
print(f'Population of country="Germany", subregion="SK Dresden": {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 country="Germany", subregion="SK Dresden": 555351 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 281181 12 1893 0
2023-03-04 281169 8 1893 0
2023-03-03 281161 34 1893 0
2023-03-02 281127 30 1893 0
2023-03-01 281097 21 1893 0
... ... ... ... ...
2020-03-13 22 7 0 0
2020-03-12 15 6 0 0
2020-03-11 9 4 0 0
2020-03-10 5 2 0 0
2020-03-09 3 1 0 0

1015 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: 0:04:33.559417