Germany: SK Heilbronn (Baden-Württemberg)¶

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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 10:18:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Heilbronn", weeks=5);
2023-03-07T10:19:09.328036 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 61.3 SK Heilbronn, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 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 Heilbronn cases daily growth factor Germany-SK Heilbronn cases daily growth factor (rolling mean) Germany-SK Heilbronn 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) Germany-SK Heilbronn deaths daily growth factor Germany-SK Heilbronn deaths daily growth factor (rolling mean) Germany-SK Heilbronn estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-SK Heilbronn doubling time cases (rolling mean) 0.00 12.56 25.12 37.68 daily change Germany-SK Heilbronn new cases (rolling 7d mean) Germany-SK Heilbronn new cases 0.000 0.251 0.502 0.754 daily change Germany-SK Heilbronn new deaths (rolling 7d mean) Germany-SK Heilbronn new deaths 0.000 0.323 0.646 0.970
In [4]:
overview(country="Germany", subregion="SK Heilbronn");
2023-03-07T10:19:27.749968 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 2000 2000 7-day incidence rate (per 100K people) 61.3 SK Heilbronn, 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 0 100 200 300 400 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 Heilbronn cases daily growth factor Germany-SK Heilbronn cases daily growth factor (rolling mean) Germany-SK Heilbronn estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Heilbronn deaths daily growth factor Germany-SK Heilbronn deaths daily growth factor (rolling mean) Germany-SK Heilbronn estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-SK Heilbronn doubling time cases (rolling mean) Germany-SK Heilbronn doubling time deaths (rolling mean) 0.0 125.6 251.2 376.8 502.5 daily change Germany-SK Heilbronn new cases (rolling 7d mean) Germany-SK Heilbronn new cases 0.00 2.51 5.02 7.54 daily change Germany-SK Heilbronn new deaths (rolling 7d mean) Germany-SK Heilbronn new deaths 0.0 109.9 219.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Heilbronn", dates="2020-03-15:");
2023-03-07T10:21:37.215231 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 Heilbronn SK Heilbronn 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 Heilbronn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Heilbronn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Heilbronn")
print(f'Population of country="Germany", subregion="SK Heilbronn": {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 Heilbronn": 125613 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 59467 23 301 0
2023-03-03 59444 9 301 0
2023-03-02 59435 9 301 0
2023-03-01 59426 19 301 0
2023-02-28 59407 17 301 0
2023-02-27 59390 29 301 0
2023-02-24 59361 17 301 0
2023-02-23 59344 17 301 0
2023-02-22 59327 14 301 0
2023-02-21 59313 34 301 0
2023-02-20 59279 40 301 0
2023-02-17 59239 29 301 0
2023-02-16 59210 27 301 0
2023-02-15 59183 23 301 0
2023-02-14 59160 44 301 0
2023-02-13 59116 42 301 1
2023-02-10 59074 11 300 0
2023-02-09 59063 21 300 0
2023-02-08 59042 28 300 0
2023-02-07 59014 32 300 0
2023-02-06 58982 39 300 1
2023-02-03 58943 6 299 0
2023-02-02 58937 21 299 0
2023-02-01 58916 17 299 0
2023-01-31 58899 17 299 0
2023-01-30 58882 22 299 0
2023-01-27 58860 6 299 0
2023-01-26 58854 18 299 0
2023-01-25 58836 12 299 1
2023-01-24 58824 22 298 0
2023-01-23 58802 15 298 0
2023-01-20 58787 10 298 1
2023-01-19 58777 13 297 0
2023-01-18 58764 14 297 0
2023-01-17 58750 21 297 0
2023-01-16 58729 17 297 1
2023-01-13 58712 8 296 0
2023-01-12 58704 12 296 1
2023-01-11 58692 17 295 0
2023-01-10 58675 24 295 0
2023-01-09 58651 27 295 0
2023-01-05 58624 22 295 1
2023-01-04 58602 19 294 0
2023-01-03 58583 34 294 0
2023-01-02 58549 37 294 1
2022-12-30 58512 14 293 0
2022-12-29 58498 35 293 0
2022-12-28 58463 49 293 0
2022-12-27 58414 51 293 1
2022-12-23 58363 18 292 0
2022-12-22 58345 39 292 0
2022-12-21 58306 29 292 0
2022-12-20 58277 39 292 1
2022-12-19 58238 34 291 1
2022-12-16 58204 8 290 0
2022-12-15 58196 27 290 0
2022-12-14 58169 34 290 0
2022-12-13 58135 39 290 0
2022-12-12 58096 35 290 0
2022-12-09 58061 28 290 0
2022-12-08 58033 28 290 0
2022-12-07 58005 32 290 0
2022-12-06 57973 35 290 0
2022-12-05 57938 31 290 0
2022-12-02 57907 22 290 0
2022-12-01 57885 20 290 0
2022-11-30 57865 31 290 0
2022-11-29 57834 27 290 0
2022-11-28 57807 25 290 1
2022-11-25 57782 30 289 0
2022-11-24 57752 31 289 0
2022-11-23 57721 22 289 0
2022-11-22 57699 33 289 0
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2022-11-15 57561 36 289 1
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2022-10-25 56943 92 287 0
2022-10-24 56851 108 287 3
2022-10-21 56743 75 284 0
2022-10-20 56668 84 284 0
2022-10-19 56584 124 284 0
2022-10-18 56460 138 284 1
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2022-10-13 56068 87 281 1
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2022-10-11 55769 124 279 0
2022-10-10 55645 290 279 1
2022-10-07 55355 150 278 1
2022-10-06 55205 192 277 1
2022-10-05 55013 161 276 0
2022-10-04 54852 190 276 1
2022-09-30 54662 63 275 1
2022-09-29 54599 99 274 0
2022-09-28 54500 93 274 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: 0:03:02.235143