Germany: LK Saale-Orla-Kreis (Thüringen)¶

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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 16:09:22 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Saale-Orla-Kreis", weeks=5);
2023-03-07T16:09:37.361887 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 20 20 30 30 40 40 50 50 7-day incidence rate (per 100K people) 38.0 LK Saale-Orla-Kreis, 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 0.0 0.5 1.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-LK Saale-Orla-Kreis cases daily growth factor Germany-LK Saale-Orla-Kreis cases daily growth factor (rolling mean) Germany-LK Saale-Orla-Kreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Saale-Orla-Kreis deaths daily growth factor Germany-LK Saale-Orla-Kreis deaths daily growth factor (rolling mean) Germany-LK Saale-Orla-Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 15000 cases doubling time [days] Germany-LK Saale-Orla-Kreis doubling time cases (rolling mean) 0.00 3.95 7.90 11.85 daily change Germany-LK Saale-Orla-Kreis new cases (rolling 7d mean) Germany-LK Saale-Orla-Kreis new cases 0.000 0.395 0.790 daily change Germany-LK Saale-Orla-Kreis new deaths (rolling 7d mean) Germany-LK Saale-Orla-Kreis new deaths 0.000 0.271 0.542 0.814
In [4]:
overview(country="Germany", subregion="LK Saale-Orla-Kreis");
2023-03-07T16:09:54.790087 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) 38.0 LK Saale-Orla-Kreis, 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 0 200 400 600 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 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-LK Saale-Orla-Kreis cases daily growth factor Germany-LK Saale-Orla-Kreis cases daily growth factor (rolling mean) Germany-LK Saale-Orla-Kreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Saale-Orla-Kreis deaths daily growth factor Germany-LK Saale-Orla-Kreis deaths daily growth factor (rolling mean) Germany-LK Saale-Orla-Kreis 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 15000 20000 cases doubling time [days] Germany-LK Saale-Orla-Kreis doubling time cases (rolling mean) Germany-LK Saale-Orla-Kreis doubling time deaths (rolling mean) 0.0 158.1 316.1 474.2 daily change Germany-LK Saale-Orla-Kreis new cases (rolling 7d mean) Germany-LK Saale-Orla-Kreis new cases 0.000 1.581 3.161 4.742 daily change Germany-LK Saale-Orla-Kreis new deaths (rolling 7d mean) Germany-LK Saale-Orla-Kreis new deaths 0.0 94.9 189.8 284.6 379.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Saale-Orla-Kreis", dates="2020-03-15:");
2023-03-07T16:11:51.723028 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 Saale-Orla-Kreis LK Saale-Orla-Kreis 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Saale-Orla-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Saale-Orla-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Saale-Orla-Kreis")
print(f'Population of country="Germany", subregion="LK Saale-Orla-Kreis": {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 Saale-Orla-Kreis": 79030 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 34486 2 355 0
2023-03-02 34484 8 355 0
2023-03-01 34476 11 355 1
2023-02-28 34465 9 354 1
2023-02-27 34456 7 353 0
2023-02-26 34449 1 353 0
2023-02-25 34448 2 353 0
2023-02-24 34446 2 353 0
2023-02-23 34444 5 353 0
2023-02-22 34439 4 353 0
2023-02-21 34435 12 353 0
2023-02-20 34423 7 353 0
2023-02-17 34416 7 353 0
2023-02-16 34409 10 353 0
2023-02-15 34399 8 353 0
2023-02-14 34391 8 353 0
2023-02-13 34383 4 353 0
2023-02-12 34379 1 353 0
2023-02-11 34378 1 353 0
2023-02-10 34377 2 353 0
2023-02-09 34375 3 353 0
2023-02-08 34372 5 353 0
2023-02-06 34367 2 353 0
2023-02-05 34365 1 353 0
2023-02-03 34364 3 353 0
2023-02-02 34361 6 353 0
2023-02-01 34355 2 353 0
2023-01-31 34353 6 353 0
2023-01-30 34347 4 353 0
2023-01-29 34343 4 353 0
2023-01-28 34339 1 353 0
2023-01-27 34338 3 353 1
2023-01-26 34335 2 352 0
2023-01-25 34333 3 352 0
2023-01-24 34330 3 352 0
2023-01-23 34327 1 352 0
2023-01-22 34326 1 352 0
2023-01-20 34325 1 352 0
2023-01-19 34324 4 352 0
2023-01-18 34320 4 352 0
2023-01-17 34316 4 352 0
2023-01-16 34312 1 352 0
2023-01-14 34311 2 352 0
2023-01-13 34309 2 352 0
2023-01-12 34307 1 352 0
2023-01-11 34306 6 352 0
2023-01-10 34300 1 352 0
2023-01-09 34299 6 352 0
2023-01-08 34293 2 352 0
2023-01-07 34291 2 352 0
2023-01-06 34289 7 352 0
2023-01-05 34282 5 352 0
2023-01-04 34277 13 352 0
2023-01-03 34264 3 352 1
2023-01-02 34261 6 351 1
2022-12-31 34255 3 350 0
2022-12-30 34252 7 350 0
2022-12-29 34245 10 350 0
2022-12-28 34235 21 350 0
2022-12-27 34214 9 350 0
2022-12-26 34205 2 350 0
2022-12-25 34203 2 350 0
2022-12-24 34201 4 350 0
2022-12-23 34197 10 350 0
2022-12-22 34187 5 350 0
2022-12-21 34182 2 350 0
2022-12-20 34180 18 350 0
2022-12-19 34162 13 350 0
2022-12-18 34149 1 350 0
2022-12-17 34148 4 350 0
2022-12-16 34144 6 350 0
2022-12-15 34138 11 350 1
2022-12-14 34127 12 349 0
2022-12-13 34115 14 349 0
2022-12-12 34101 23 349 0
2022-12-11 34078 8 349 1
2022-12-10 34070 6 348 0
2022-12-09 34064 20 348 0
2022-12-08 34044 12 348 0
2022-12-07 34032 13 348 0
2022-12-06 34019 24 348 0
2022-12-05 33995 10 348 0
2022-12-03 33985 8 348 0
2022-12-02 33977 11 348 0
2022-12-01 33966 22 348 0
2022-11-30 33944 19 348 0
2022-11-29 33925 30 348 0
2022-11-28 33895 14 348 0
2022-11-27 33881 1 348 0
2022-11-26 33880 6 348 1
2022-11-25 33874 16 347 0
2022-11-24 33858 16 347 0
2022-11-23 33842 11 347 0
2022-11-22 33831 15 347 0
2022-11-21 33816 24 347 0
2022-11-20 33792 2 347 0
2022-11-19 33790 7 347 0
2022-11-18 33783 20 347 0
2022-11-17 33763 24 347 0
2022-11-16 33739 18 347 0
2022-11-15 33721 19 347 0
2022-11-14 33702 23 347 0
2022-11-13 33679 1 347 0
2022-11-11 33678 15 347 0
2022-11-10 33663 7 347 0
2022-11-09 33656 17 347 0
2022-11-08 33639 21 347 0
2022-11-07 33618 17 347 0
2022-11-06 33601 1 347 0
2022-11-05 33600 7 347 0
2022-11-04 33593 17 347 0
2022-11-03 33576 29 347 0
2022-11-02 33547 47 347 0
2022-11-01 33500 21 347 0
2022-10-31 33479 6 347 0
2022-10-30 33473 5 347 1
2022-10-29 33468 12 346 0
2022-10-28 33456 29 346 0
2022-10-27 33427 30 346 0
2022-10-26 33397 27 346 0
2022-10-25 33370 58 346 0
2022-10-24 33312 50 346 0
2022-10-23 33262 7 346 0
2022-10-22 33255 3 346 0
2022-10-21 33252 35 346 0
2022-10-20 33217 35 346 0
2022-10-19 33182 41 346 0
2022-10-18 33141 51 346 0
2022-10-17 33090 44 346 0
2022-10-16 33046 8 346 0
2022-10-15 33038 42 346 0
2022-10-14 32996 45 346 0
2022-10-13 32951 48 346 0
2022-10-12 32903 70 346 0
2022-10-11 32833 90 346 0
2022-10-10 32743 121 346 1
2022-10-09 32622 2 345 0
2022-10-08 32620 18 345 1
2022-10-07 32602 103 344 0
2022-10-06 32499 56 344 1
2022-10-05 32443 144 343 0
2022-10-04 32299 31 343 2
2022-10-03 32268 7 341 0
2022-10-02 32261 1 341 0
2022-10-01 32260 11 341 0
2022-09-30 32249 60 341 0
2022-09-29 32189 30 341 0
2022-09-28 32159 50 341 0
2022-09-27 32109 72 341 0
2022-09-26 32037 17 341 0
2022-09-25 32020 3 341 0
2022-09-24 32017 9 341 0
2022-09-23 32008 60 341 0
2022-09-22 31948 37 341 0
2022-09-21 31911 31 341 0
2022-09-20 31880 9 341 0
2022-09-19 31871 24 341 0
2022-09-18 31847 5 341 0
2022-09-17 31842 10 341 0
2022-09-16 31832 30 341 0
2022-09-15 31802 27 341 0
2022-09-14 31775 18 341 0
2022-09-13 31757 28 341 0
2022-09-12 31729 10 341 0
2022-09-10 31719 2 341 0
2022-09-09 31717 23 341 0
2022-09-08 31694 17 341 1
2022-09-07 31677 22 340 0
2022-09-06 31655 46 340 0
2022-09-05 31609 22 340 0
2022-09-02 31587 3 340 0
2022-09-01 31584 14 340 0
2022-08-31 31570 13 340 0
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2022-08-28 31529 1 340 0
2022-08-27 31528 3 340 0
2022-08-26 31525 23 340 0
2022-08-25 31502 13 340 0
2022-08-24 31489 18 340 0
2022-08-23 31471 38 340 0
2022-08-22 31433 36 340 0
2022-08-21 31397 6 340 0
2022-08-20 31391 7 340 0
2022-08-19 31384 26 340 0
2022-08-18 31358 13 340 0
2022-08-17 31345 20 340 0
2022-08-16 31325 28 340 0
2022-08-15 31297 55 340 1
2022-08-13 31242 5 339 0
2022-08-12 31237 28 339 3
2022-08-11 31209 22 336 0
2022-08-10 31187 40 336 0
2022-08-09 31147 29 336 0
2022-08-08 31118 36 336 0
2022-08-07 31082 1 336 0
2022-08-06 31081 5 336 0
2022-08-05 31076 26 336 0
2022-08-04 31050 36 336 0
2022-08-03 31014 43 336 0
2022-08-02 30971 21 336 0
2022-08-01 30950 54 336 0
2022-07-31 30896 12 336 1
2022-07-30 30884 10 335 0
2022-07-29 30874 25 335 0
2022-07-28 30849 32 335 0
2022-07-27 30817 43 335 0
2022-07-26 30774 84 335 0
2022-07-25 30690 78 335 0
2022-07-22 30612 17 335 0
2022-07-21 30595 68 335 0
2022-07-20 30527 40 335 1
2022-07-19 30487 86 334 0
2022-07-18 30401 74 334 0
2022-07-16 30327 1 334 0
2022-07-15 30326 21 334 0
2022-07-14 30305 54 334 0
2022-07-13 30251 47 334 0
2022-07-12 30204 46 334 0
2022-07-11 30158 42 334 0
2022-07-10 30116 6 334 1
2022-07-09 30110 15 333 0
2022-07-08 30095 17 333 0
2022-07-07 30078 26 333 0
2022-07-06 30052 23 333 0
2022-07-05 30029 40 333 0
2022-07-04 29989 24 333 0
2022-07-02 29965 2 333 0
2022-07-01 29963 17 333 0
2022-06-30 29946 22 333 0
2022-06-29 29924 25 333 0
2022-06-28 29899 10 333 0
2022-06-27 29889 31 333 1
2022-06-24 29858 20 332 0
2022-06-23 29838 28 332 0
2022-06-22 29810 12 332 0
2022-06-21 29798 27 332 0
2022-06-20 29771 25 332 0
2022-06-17 29746 12 332 0
2022-06-16 29734 13 332 0
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2022-06-14 29708 20 332 0
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2020-12-31 2057 53 50 0
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2020-12-27 1869 25 45 1
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2020-04-08 76 8 8 1
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2020-03-26 11 1 0 0
2020-03-25 10 2 0 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: 0:02:40.063904