Germany: LK Tübingen (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 10:19:15 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Tübingen", weeks=5);
2023-03-07T10:19:31.108392 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) 51.8 LK Tübingen, 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Tübingen new deaths (rolling 7d mean) Germany-LK Tübingen new deaths 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-LK Tübingen cases daily growth factor Germany-LK Tübingen cases daily growth factor (rolling mean) Germany-LK Tübingen estimated R (using cases) 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 deaths) Germany-LK Tübingen deaths daily growth factor Germany-LK Tübingen deaths daily growth factor (rolling mean) Germany-LK Tübingen 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-LK Tübingen doubling time cases (rolling mean) 0 23 46 69 92 daily change Germany-LK Tübingen new cases (rolling 7d mean) Germany-LK Tübingen new cases 0.000 0.285 0.571 0.856
In [4]:
overview(country="Germany", subregion="LK Tübingen");
2023-03-07T10:19:50.878247 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 7-day incidence rate (per 100K people) 51.8 LK Tübingen, 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 1 2 3 4 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-LK Tübingen cases daily growth factor Germany-LK Tübingen cases daily growth factor (rolling mean) Germany-LK Tübingen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 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 deaths) Germany-LK Tübingen deaths daily growth factor Germany-LK Tübingen deaths daily growth factor (rolling mean) Germany-LK Tübingen 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 6000 8000 cases doubling time [days] Germany-LK Tübingen doubling time cases (rolling mean) Germany-LK Tübingen doubling time deaths (rolling mean) 0 230 460 689 919 daily change Germany-LK Tübingen new cases (rolling 7d mean) Germany-LK Tübingen new cases 0.0 2.3 4.6 6.9 9.2 daily change Germany-LK Tübingen new deaths (rolling 7d mean) Germany-LK Tübingen new deaths 0.0 61.0 122.0 182.9 243.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Tübingen", dates="2020-03-15:");
2023-03-07T10:22:01.375847 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: LK Tübingen LK Tübingen 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 LK Tübingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Tübingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Tübingen")
print(f'Population of country="Germany", subregion="LK Tübingen": {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="LK Tübingen": 229806 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 103763 21 278 0
2023-03-03 103742 7 278 0
2023-03-02 103735 14 278 0
2023-03-01 103721 42 278 0
2023-02-28 103679 35 278 0
2023-02-27 103644 98 278 0
2023-02-24 103546 36 278 0
2023-02-23 103510 41 278 0
2023-02-22 103469 43 278 0
2023-02-21 103426 54 278 0
2023-02-20 103372 102 278 0
2023-02-17 103270 25 278 0
2023-02-16 103245 30 278 0
2023-02-15 103215 40 278 0
2023-02-14 103175 54 278 0
2023-02-13 103121 54 278 0
2023-02-10 103067 29 278 0
2023-02-09 103038 25 278 0
2023-02-08 103013 47 278 0
2023-02-07 102966 54 278 0
2023-02-06 102912 44 278 0
2023-02-03 102868 22 278 0
2023-02-02 102846 25 278 0
2023-02-01 102821 44 278 0
2023-01-31 102777 26 278 0
2023-01-30 102751 56 278 0
2023-01-27 102695 16 278 0
2023-01-26 102679 44 278 0
2023-01-25 102635 19 278 0
2023-01-24 102616 38 278 0
2023-01-23 102578 31 278 0
2023-01-20 102547 15 278 0
2023-01-19 102532 12 278 0
2023-01-18 102520 17 278 0
2023-01-17 102503 35 278 0
2023-01-16 102468 28 278 0
2023-01-13 102440 20 278 0
2023-01-12 102420 34 278 0
2023-01-11 102386 23 278 0
2023-01-10 102363 72 278 0
2023-01-09 102291 66 278 0
2023-01-05 102225 56 278 0
2023-01-04 102169 56 278 1
2023-01-03 102113 42 277 0
2023-01-02 102071 71 277 0
2022-12-30 102000 37 277 0
2022-12-29 101963 110 277 0
2022-12-28 101853 142 277 0
2022-12-27 101711 10 277 0
2022-12-23 101701 55 277 0
2022-12-22 101646 70 277 0
2022-12-21 101576 96 277 0
2022-12-20 101480 66 277 0
2022-12-19 101414 107 277 0
2022-12-16 101307 48 277 0
2022-12-15 101259 75 277 0
2022-12-14 101184 71 277 0
2022-12-13 101113 67 277 0
2022-12-12 101046 109 277 0
2022-12-09 100937 61 277 0
2022-12-08 100876 69 277 0
2022-12-07 100807 53 277 0
2022-12-06 100754 73 277 0
2022-12-05 100681 104 277 0
2022-12-02 100577 40 277 0
2022-12-01 100537 62 277 0
2022-11-30 100475 64 277 2
2022-11-29 100411 81 275 0
2022-11-28 100330 88 275 0
2022-11-25 100242 56 275 0
2022-11-24 100186 41 275 0
2022-11-23 100145 111 275 0
2022-11-22 100034 81 275 0
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2022-11-15 99634 61 275 0
2022-11-14 99573 140 275 0
2022-11-11 99433 47 275 0
2022-11-10 99386 60 275 0
2022-11-09 99326 93 275 0
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2022-11-04 98918 147 275 0
2022-11-03 98771 111 275 0
2022-11-02 98660 124 275 0
2022-10-31 98536 179 275 0
2022-10-28 98357 118 275 0
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2022-10-25 97963 225 275 0
2022-10-24 97738 305 275 4
2022-10-21 97433 129 271 0
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2022-10-17 96475 482 271 0
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2022-10-13 95734 317 271 0
2022-10-12 95417 428 271 0
2022-10-11 94989 438 271 0
2022-10-10 94551 561 271 0
2022-10-07 93990 390 271 0
2022-10-06 93600 487 271 0
2022-10-05 93113 476 271 0
2022-10-04 92637 586 271 1
2022-09-30 92051 192 270 0
2022-09-29 91859 225 270 1
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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: 0:03:12.458917