Germany: LK Calw (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 09:44:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Calw", weeks=5);
2023-03-07T09:44:39.861206 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 100 100 7-day incidence rate (per 100K people) 52.9 LK Calw, 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 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-LK Calw cases daily growth factor Germany-LK Calw cases daily growth factor (rolling mean) Germany-LK Calw 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-LK Calw deaths daily growth factor Germany-LK Calw deaths daily growth factor (rolling mean) Germany-LK Calw estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Calw doubling time cases (rolling mean) 0.00 16.07 32.14 daily change Germany-LK Calw new cases (rolling 7d mean) Germany-LK Calw new cases 0.000 0.321 0.643 0.964 daily change Germany-LK Calw new deaths (rolling 7d mean) Germany-LK Calw new deaths 0.000 0.353 0.705
In [4]:
overview(country="Germany", subregion="LK Calw");
2023-03-07T09:44:57.768139 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) 52.9 LK Calw, 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 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 Calw cases daily growth factor Germany-LK Calw cases daily growth factor (rolling mean) Germany-LK Calw 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-LK Calw deaths daily growth factor Germany-LK Calw deaths daily growth factor (rolling mean) Germany-LK Calw 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 Calw doubling time cases (rolling mean) Germany-LK Calw doubling time deaths (rolling mean) 0.0 160.7 321.4 482.1 642.7 daily change Germany-LK Calw new cases (rolling 7d mean) Germany-LK Calw new cases 0.000 3.214 6.427 daily change Germany-LK Calw new deaths (rolling 7d mean) Germany-LK Calw new deaths 0.0 63.2 126.3 189.5 252.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Calw", dates="2020-03-15:");
2023-03-07T09:50:06.910501 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 Calw LK Calw 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 Calw Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Calw")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Calw")
print(f'Population of country="Germany", subregion="LK Calw": {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 Calw": 160686 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 70853 10 363 0
2023-03-03 70843 9 363 0
2023-03-02 70834 23 363 0
2023-03-01 70811 19 363 0
2023-02-28 70792 24 363 0
2023-02-27 70768 31 363 0
2023-02-24 70737 29 363 0
2023-02-23 70708 25 363 0
2023-02-22 70683 27 363 0
2023-02-21 70656 25 363 0
2023-02-20 70631 42 363 0
2023-02-17 70589 32 363 0
2023-02-16 70557 24 363 0
2023-02-15 70533 28 363 0
2023-02-14 70505 32 363 0
2023-02-13 70473 40 363 1
2023-02-10 70433 24 362 0
2023-02-09 70409 16 362 0
2023-02-08 70393 23 362 0
2023-02-07 70370 34 362 0
2023-02-06 70336 29 362 0
2023-02-03 70307 18 362 0
2023-02-02 70289 20 362 0
2023-02-01 70269 28 362 0
2023-01-31 70241 15 362 0
2023-01-30 70226 23 362 0
2023-01-27 70203 13 362 0
2023-01-26 70190 26 362 0
2023-01-25 70164 14 362 0
2023-01-24 70150 16 362 0
2023-01-23 70134 13 362 0
2023-01-20 70121 8 362 0
2023-01-19 70113 10 362 0
2023-01-18 70103 9 362 0
2023-01-17 70094 16 362 0
2023-01-16 70078 17 362 0
2023-01-13 70061 19 362 0
2023-01-12 70042 19 362 0
2023-01-11 70023 13 362 1
2023-01-10 70010 24 361 2
2023-01-09 69986 43 359 0
2023-01-05 69943 25 359 0
2023-01-04 69918 37 359 0
2023-01-03 69881 35 359 0
2023-01-02 69846 29 359 0
2023-01-01 69817 3 359 1
2022-12-31 69814 1 358 0
2022-12-30 69813 18 358 1
2022-12-29 69795 30 357 1
2022-12-28 69765 41 356 1
2022-12-27 69724 54 355 0
2022-12-23 69670 36 355 1
2022-12-22 69634 68 354 1
2022-12-21 69566 67 353 1
2022-12-20 69499 46 352 1
2022-12-19 69453 78 351 0
2022-12-16 69375 54 351 0
2022-12-15 69321 64 351 0
2022-12-14 69257 69 351 1
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2022-12-12 69105 85 349 2
2022-12-09 69020 68 347 0
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2022-12-07 68898 82 347 1
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2022-12-01 68688 45 345 0
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2022-10-20 66759 135 339 1
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2022-10-17 66115 164 337 2
2022-10-16 65951 1 335 0
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2020-03-22 150 18 3 1
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2020-03-19 92 15 2 0
2020-03-18 77 16 2 0
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2020-03-15 43 9 2 0
2020-03-14 34 8 2 0
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2020-03-09 2 1 0 0

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: 0:06:29.495233