Germany: LK Teltow-Fläming (Brandenburg)¶

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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 12:36:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Teltow-Fläming", weeks=5);
2023-03-07T12:36:42.137559 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 41.7 LK Teltow-Fläming, 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 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.00 0.02 0.02 0.04 0.04 0.06 0.06 daily change Germany-LK Teltow-Fläming new deaths (rolling 7d mean) Germany-LK Teltow-Fläming 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 Teltow-Fläming cases daily growth factor Germany-LK Teltow-Fläming cases daily growth factor (rolling mean) Germany-LK Teltow-Fläming 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 Teltow-Fläming deaths daily growth factor Germany-LK Teltow-Fläming deaths daily growth factor (rolling mean) Germany-LK Teltow-Fläming 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 Teltow-Fläming doubling time cases (rolling mean) 0.00 8.63 17.25 25.88 34.51 daily change Germany-LK Teltow-Fläming new cases (rolling 7d mean) Germany-LK Teltow-Fläming new cases 0.000 0.252 0.504 0.757
In [4]:
overview(country="Germany", subregion="LK Teltow-Fläming");
2023-03-07T12:37:00.893786 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) 41.7 LK Teltow-Fläming, 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 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 8 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 Teltow-Fläming cases daily growth factor Germany-LK Teltow-Fläming cases daily growth factor (rolling mean) Germany-LK Teltow-Fläming 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 Teltow-Fläming deaths daily growth factor Germany-LK Teltow-Fläming deaths daily growth factor (rolling mean) Germany-LK Teltow-Fläming 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 Teltow-Fläming doubling time cases (rolling mean) Germany-LK Teltow-Fläming doubling time deaths (rolling mean) 0 345 690 daily change Germany-LK Teltow-Fläming new cases (rolling 7d mean) Germany-LK Teltow-Fläming new cases 0.00 3.45 6.90 10.35 13.80 daily change Germany-LK Teltow-Fläming new deaths (rolling 7d mean) Germany-LK Teltow-Fläming new deaths 0.0 105.6 211.3 316.9 422.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Teltow-Fläming", dates="2020-03-15:");
2023-03-07T12:39:29.795072 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 Teltow-Fläming LK Teltow-Fläming 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 Teltow-Fläming Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Teltow-Fläming")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Teltow-Fläming")
print(f'Population of country="Germany", subregion="LK Teltow-Fläming": {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 Teltow-Fläming": 172545 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 77001 9 386 0
2023-03-03 76992 7 386 0
2023-03-02 76985 13 386 0
2023-03-01 76972 21 386 0
2023-02-28 76951 22 386 0
2023-02-27 76929 36 386 0
2023-02-24 76893 16 386 0
2023-02-23 76877 32 386 0
2023-02-22 76845 20 386 0
2023-02-21 76825 29 386 0
2023-02-20 76796 34 386 0
2023-02-17 76762 17 386 0
2023-02-16 76745 20 386 0
2023-02-15 76725 28 386 0
2023-02-14 76697 25 386 0
2023-02-13 76672 29 386 0
2023-02-10 76643 10 386 0
2023-02-09 76633 24 386 0
2023-02-08 76609 27 386 0
2023-02-07 76582 21 386 0
2023-02-06 76561 27 386 0
2023-02-03 76534 25 386 0
2023-02-02 76509 24 386 0
2023-02-01 76485 25 386 0
2023-01-31 76460 36 386 0
2023-01-30 76424 31 386 0
2023-01-27 76393 16 386 1
2023-01-26 76377 27 385 0
2023-01-25 76350 21 385 0
2023-01-24 76329 37 385 0
2023-01-23 76292 32 385 0
2023-01-20 76260 18 385 0
2023-01-19 76242 25 385 0
2023-01-18 76217 26 385 0
2023-01-17 76191 32 385 0
2023-01-16 76159 29 385 1
2023-01-13 76130 20 384 0
2023-01-12 76110 23 384 0
2023-01-11 76087 33 384 0
2023-01-10 76054 42 384 0
2023-01-09 76012 59 384 0
2023-01-06 75953 29 384 0
2023-01-05 75924 46 384 0
2023-01-04 75878 67 384 1
2023-01-03 75811 64 383 0
2023-01-02 75747 60 383 0
2022-12-31 75687 1 383 0
2022-12-30 75686 42 383 0
2022-12-29 75644 72 383 0
2022-12-28 75572 118 383 1
2022-12-27 75454 122 382 0
2022-12-23 75332 65 382 0
2022-12-22 75267 91 382 0
2022-12-21 75176 102 382 0
2022-12-20 75074 175 382 1
2022-12-19 74899 154 381 0
2022-12-16 74745 89 381 1
2022-12-15 74656 90 380 0
2022-12-14 74566 154 380 0
2022-12-13 74412 167 380 0
2022-12-12 74245 146 380 0
2022-12-09 74099 96 380 0
2022-12-08 74003 109 380 0
2022-12-07 73894 124 380 0
2022-12-06 73770 169 380 0
2022-12-05 73601 162 380 0
2022-12-03 73439 2 380 0
2022-12-02 73437 102 380 0
2022-12-01 73335 85 380 0
2022-11-30 73250 146 380 0
2022-11-29 73104 117 380 0
2022-11-28 72987 116 380 1
2022-11-27 72871 1 379 0
2022-11-25 72870 64 379 0
2022-11-24 72806 72 379 0
2022-11-23 72734 71 379 0
2022-11-22 72663 83 379 0
2022-11-21 72580 64 379 0
2022-11-18 72516 47 379 0
2022-11-17 72469 46 379 0
2022-11-16 72423 60 379 0
2022-11-15 72363 115 379 0
2022-11-14 72248 90 379 0
2022-11-13 72158 2 379 0
2022-11-11 72156 39 379 0
2022-11-10 72117 80 379 0
2022-11-09 72037 82 379 1
2022-11-08 71955 105 378 1
2022-11-07 71850 129 377 0
2022-11-05 71721 2 377 0
2022-11-04 71719 63 377 1
2022-11-03 71656 115 376 0
2022-11-02 71541 259 376 0
2022-11-01 71282 141 376 0
2022-10-31 71141 3 376 0
2022-10-29 71138 1 376 0
2022-10-28 71137 82 376 0
2022-10-27 71055 92 376 1
2022-10-26 70963 146 375 0
2022-10-25 70817 239 375 1
2022-10-24 70578 236 374 0
2022-10-23 70342 1 374 0
2022-10-21 70341 144 374 0
2022-10-20 70197 163 374 0
2022-10-19 70034 270 374 1
2022-10-18 69764 252 373 0
2022-10-17 69512 339 373 3
2022-10-16 69173 4 370 0
2022-10-15 69169 5 370 0
2022-10-14 69164 106 370 0
2022-10-13 69058 195 370 0
2022-10-12 68863 264 370 1
2022-10-11 68599 273 369 0
2022-10-10 68326 263 369 0
2022-10-09 68063 6 369 0
2022-10-08 68057 10 369 0
2022-10-07 68047 260 369 0
2022-10-06 67787 297 369 0
2022-10-05 67490 292 369 0
2022-10-04 67198 256 369 2
2022-10-03 66942 2 367 0
2022-10-02 66940 1 367 0
2022-10-01 66939 2 367 0
2022-09-30 66937 111 367 0
2022-09-29 66826 131 367 0
2022-09-28 66695 206 367 0
2022-09-27 66489 199 367 1
2022-09-26 66290 171 366 0
2022-09-23 66119 120 366 0
2022-09-22 65999 122 366 0
2022-09-21 65877 135 366 0
2022-09-20 65742 117 366 0
2022-09-19 65625 119 366 0
2022-09-18 65506 1 366 0
2022-09-16 65505 81 366 0
2022-09-15 65424 85 366 0
2022-09-14 65339 131 366 0
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2022-09-08 64843 78 364 0
2022-09-07 64765 130 364 0
2022-09-06 64635 150 364 0
2022-09-05 64485 109 364 0
2022-09-02 64376 69 364 0
2022-09-01 64307 77 364 0
2022-08-31 64230 97 364 0
2022-08-30 64133 127 364 0
2022-08-29 64006 110 364 0
2022-08-26 63896 61 364 0
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2022-07-02 57835 11 356 0
2022-07-01 57824 88 356 0
2022-06-30 57736 278 356 0
2022-06-29 57458 30 356 0
2022-06-28 57428 294 356 1
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2022-06-26 56875 18 354 0
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2022-06-19 55839 5 354 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: 0:03:30.471683