Germany: SK Heidelberg (Baden-Württemberg)¶

  • Homepage of project: https://oscovida.github.io
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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 10:18:56 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Heidelberg", weeks=5);
2023-03-07T10:19:10.723488 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 89.2 SK Heidelberg, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 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 Heidelberg cases daily growth factor Germany-SK Heidelberg cases daily growth factor (rolling mean) Germany-SK Heidelberg 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 Heidelberg deaths daily growth factor Germany-SK Heidelberg deaths daily growth factor (rolling mean) Germany-SK Heidelberg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-SK Heidelberg doubling time cases (rolling mean) 0.00 31.85 63.70 daily change Germany-SK Heidelberg new cases (rolling 7d mean) Germany-SK Heidelberg new cases 0.000 0.318 0.637 0.955 daily change Germany-SK Heidelberg new deaths (rolling 7d mean) Germany-SK Heidelberg new deaths 0.000 0.303 0.606 0.910
In [4]:
overview(country="Germany", subregion="SK Heidelberg");
2023-03-07T10:19:33.692931 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 89.2 SK Heidelberg, 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 0 100 200 300 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 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 Heidelberg cases daily growth factor Germany-SK Heidelberg cases daily growth factor (rolling mean) Germany-SK Heidelberg 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 Heidelberg deaths daily growth factor Germany-SK Heidelberg deaths daily growth factor (rolling mean) Germany-SK Heidelberg estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 cases doubling time [days] Germany-SK Heidelberg doubling time cases (rolling mean) Germany-SK Heidelberg doubling time deaths (rolling mean) 0.0 159.2 318.5 477.7 daily change Germany-SK Heidelberg new cases (rolling 7d mean) Germany-SK Heidelberg new cases 0.000 1.592 3.185 daily change Germany-SK Heidelberg new deaths (rolling 7d mean) Germany-SK Heidelberg new deaths 0.0 68.7 137.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Heidelberg", dates="2020-03-15:");
2023-03-07T10:21:43.387402 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 Heidelberg SK Heidelberg 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 Heidelberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Heidelberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Heidelberg")
print(f'Population of country="Germany", subregion="SK Heidelberg": {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 Heidelberg": 159245 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 62309 22 182 0
2023-03-03 62287 4 182 0
2023-03-02 62283 19 182 0
2023-03-01 62264 92 182 0
2023-02-28 62172 5 182 0
2023-02-27 62167 40 182 0
2023-02-24 62127 38 182 0
2023-02-23 62089 38 182 0
2023-02-22 62051 57 182 0
2023-02-21 61994 32 182 0
2023-02-20 61962 65 182 0
2023-02-17 61897 43 182 0
2023-02-16 61854 21 182 0
2023-02-15 61833 41 182 0
2023-02-14 61792 38 182 0
2023-02-13 61754 59 182 1
2023-02-10 61695 26 181 0
2023-02-09 61669 19 181 0
2023-02-08 61650 24 181 0
2023-02-07 61626 33 181 0
2023-02-06 61593 45 181 0
2023-02-03 61548 23 181 0
2023-02-02 61525 27 181 0
2023-02-01 61498 32 181 0
2023-01-31 61466 26 181 0
2023-01-30 61440 41 181 0
2023-01-27 61399 13 181 0
2023-01-26 61386 21 181 0
2023-01-25 61365 18 181 0
2023-01-24 61347 18 181 1
2023-01-23 61329 28 180 1
2023-01-20 61301 10 179 0
2023-01-19 61291 18 179 0
2023-01-18 61273 16 179 0
2023-01-17 61257 12 179 0
2023-01-16 61245 33 179 0
2023-01-13 61212 22 179 0
2023-01-12 61190 24 179 0
2023-01-11 61166 30 179 0
2023-01-10 61136 34 179 0
2023-01-09 61102 58 179 0
2023-01-05 61044 26 179 0
2023-01-04 61018 65 179 0
2023-01-03 60953 40 179 0
2023-01-02 60913 57 179 0
2022-12-30 60856 57 179 0
2022-12-29 60799 50 179 0
2022-12-28 60749 80 179 0
2022-12-27 60669 126 179 0
2022-12-23 60543 81 179 0
2022-12-22 60462 51 179 0
2022-12-21 60411 70 179 1
2022-12-20 60341 85 178 0
2022-12-19 60256 70 178 1
2022-12-16 60186 37 177 0
2022-12-15 60149 62 177 1
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2022-12-13 59995 57 175 0
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2022-12-10 59856 1 175 0
2022-12-09 59855 44 175 1
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2022-12-04 59602 1 174 0
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

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: 0:03:03.213380