Germany: LK Anhalt-Bitterfeld (Sachsen-Anhalt)¶

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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 15:36:29 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Anhalt-Bitterfeld", weeks=5);
2023-03-07T15:36:44.679444 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 25 25 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 28.2 LK Anhalt-Bitterfeld, 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 Anhalt-Bitterfeld new deaths (rolling 7d mean) Germany-LK Anhalt-Bitterfeld new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Anhalt-Bitterfeld cases daily growth factor Germany-LK Anhalt-Bitterfeld cases daily growth factor (rolling mean) Germany-LK Anhalt-Bitterfeld 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 Anhalt-Bitterfeld deaths daily growth factor Germany-LK Anhalt-Bitterfeld deaths daily growth factor (rolling mean) Germany-LK Anhalt-Bitterfeld estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 cases doubling time [days] Germany-LK Anhalt-Bitterfeld doubling time cases (rolling mean) 0.00 15.59 31.18 46.77 62.36 daily change Germany-LK Anhalt-Bitterfeld new cases (rolling 7d mean) Germany-LK Anhalt-Bitterfeld new cases 0.000 0.389 0.778
In [4]:
overview(country="Germany", subregion="LK Anhalt-Bitterfeld");
2023-03-07T15:36:59.474157 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) 28.2 LK Anhalt-Bitterfeld, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Anhalt-Bitterfeld cases daily growth factor Germany-LK Anhalt-Bitterfeld cases daily growth factor (rolling mean) Germany-LK Anhalt-Bitterfeld 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 Anhalt-Bitterfeld deaths daily growth factor Germany-LK Anhalt-Bitterfeld deaths daily growth factor (rolling mean) Germany-LK Anhalt-Bitterfeld 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-LK Anhalt-Bitterfeld doubling time cases (rolling mean) Germany-LK Anhalt-Bitterfeld doubling time deaths (rolling mean) 0 312 624 935 daily change Germany-LK Anhalt-Bitterfeld new cases (rolling 7d mean) Germany-LK Anhalt-Bitterfeld new cases 0.000 3.118 6.236 daily change Germany-LK Anhalt-Bitterfeld new deaths (rolling 7d mean) Germany-LK Anhalt-Bitterfeld new deaths 0.0 163.3 326.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Anhalt-Bitterfeld", dates="2020-03-15:");
2023-03-07T15:39:32.726100 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 Anhalt-Bitterfeld LK Anhalt-Bitterfeld 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 Anhalt-Bitterfeld Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Anhalt-Bitterfeld")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Anhalt-Bitterfeld")
print(f'Population of country="Germany", subregion="LK Anhalt-Bitterfeld": {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 Anhalt-Bitterfeld": 155900 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 70796 13 473 0
2023-03-03 70783 4 473 0
2023-03-02 70779 13 473 0
2023-02-28 70766 14 473 0
2023-02-27 70752 34 473 0
2023-02-24 70718 26 473 0
2023-02-23 70692 25 473 0
2023-02-22 70667 39 473 0
2023-02-21 70628 42 473 0
2023-02-20 70586 66 473 0
2023-02-17 70520 11 473 0
2023-02-16 70509 22 473 0
2023-02-15 70487 17 473 0
2023-02-14 70470 42 473 0
2023-02-13 70428 14 473 0
2023-02-10 70414 19 473 0
2023-02-09 70395 16 473 0
2023-02-08 70379 31 473 0
2023-02-07 70348 24 473 0
2023-02-06 70324 28 473 0
2023-02-05 70296 3 473 0
2023-02-03 70293 11 473 0
2023-02-02 70282 19 473 0
2023-02-01 70263 27 473 0
2023-01-31 70236 46 473 0
2023-01-30 70190 25 473 0
2023-01-29 70165 1 473 0
2023-01-28 70164 3 473 0
2023-01-27 70161 8 473 0
2023-01-26 70153 9 473 0
2023-01-25 70144 27 473 0
2023-01-24 70117 24 473 0
2023-01-23 70093 39 473 0
2023-01-20 70054 15 473 0
2023-01-19 70039 16 473 0
2023-01-18 70023 22 473 0
2023-01-17 70001 23 473 0
2023-01-16 69978 20 473 0
2023-01-15 69958 1 473 0
2023-01-14 69957 1 473 0
2023-01-13 69956 11 473 0
2023-01-12 69945 19 473 0
2023-01-11 69926 50 473 0
2023-01-10 69876 34 473 0
2023-01-09 69842 56 473 1
2023-01-05 69786 24 472 0
2023-01-04 69762 26 472 0
2023-01-03 69736 33 472 0
2023-01-02 69703 47 472 0
2023-01-01 69656 1 472 0
2022-12-31 69655 15 472 0
2022-12-30 69640 19 472 0
2022-12-29 69621 44 472 0
2022-12-28 69577 65 472 0
2022-12-27 69512 75 472 2
2022-12-25 69437 3 470 0
2022-12-24 69434 2 470 0
2022-12-23 69432 93 470 1
2022-12-22 69339 45 469 0
2022-12-21 69294 40 469 1
2022-12-20 69254 54 468 0
2022-12-19 69200 64 468 1
2022-12-18 69136 7 467 0
2022-12-17 69129 8 467 0
2022-12-16 69121 54 467 0
2022-12-15 69067 34 467 0
2022-12-14 69033 75 467 0
2022-12-13 68958 73 467 0
2022-12-12 68885 88 467 1
2022-12-11 68797 13 466 0
2022-12-10 68784 25 466 0
2022-12-09 68759 70 466 0
2022-12-08 68689 48 466 0
2022-12-07 68641 67 466 0
2022-12-06 68574 104 466 0
2022-12-05 68470 65 466 0
2022-12-04 68405 19 466 1
2022-12-03 68386 26 465 0
2022-12-02 68360 71 465 1
2022-12-01 68289 63 464 0
2022-11-30 68226 109 464 1
2022-11-29 68117 113 463 0
2022-11-28 68004 93 463 0
2022-11-27 67911 21 463 0
2022-11-26 67890 47 463 0
2022-11-25 67843 56 463 0
2022-11-24 67787 67 463 0
2022-11-23 67720 58 463 0
2022-11-22 67662 100 463 0
2022-11-21 67562 97 463 1
2022-11-20 67465 10 462 1
2022-11-19 67455 19 461 0
2022-11-18 67436 52 461 0
2022-11-17 67384 59 461 0
2022-11-16 67325 45 461 1
2022-11-15 67280 79 460 1
2022-11-14 67201 60 459 0
2022-11-13 67141 23 459 0
2022-11-12 67118 14 459 1
2022-11-11 67104 71 458 1
2022-11-10 67033 47 457 0
2022-11-09 66986 56 457 0
2022-11-08 66930 87 457 0
2022-11-07 66843 81 457 1
2022-11-06 66762 4 456 0
2022-11-05 66758 17 456 0
2022-11-04 66741 62 456 0
2022-11-03 66679 49 456 0
2022-11-02 66630 136 456 0
2022-11-01 66494 106 456 0
2022-10-31 66388 8 456 0
2022-10-30 66380 28 456 0
2022-10-29 66352 18 456 2
2022-10-28 66334 80 454 0
2022-10-27 66254 86 454 1
2022-10-26 66168 136 453 1
2022-10-25 66032 180 452 2
2022-10-24 65852 135 450 1
2022-10-23 65717 19 449 0
2022-10-22 65698 73 449 0
2022-10-21 65625 130 449 2
2022-10-20 65495 156 447 1
2022-10-19 65339 221 446 1
2022-10-18 65118 304 445 3
2022-10-17 64814 235 442 1
2022-10-16 64579 26 441 0
2022-10-15 64553 84 441 0
2022-10-14 64469 167 441 0
2022-10-13 64302 196 441 0
2022-10-12 64106 191 441 0
2022-10-11 63915 254 441 1
2022-10-10 63661 214 440 0
2022-10-09 63447 58 440 1
2022-10-08 63389 59 439 1
2022-10-07 63330 195 438 0
2022-10-06 63135 176 438 1
2022-10-05 62959 243 437 0
2022-10-04 62716 220 437 1
2022-10-03 62496 31 436 0
2022-10-02 62465 20 436 0
2022-10-01 62445 62 436 0
2022-09-30 62383 137 436 0
2022-09-29 62246 117 436 0
2022-09-28 62129 119 436 0
2022-09-27 62010 165 436 1
2022-09-26 61845 122 435 1
2022-09-25 61723 34 434 0
2022-09-24 61689 31 434 1
2022-09-23 61658 98 433 0
2022-09-22 61560 42 433 0
2022-09-21 61518 61 433 0
2022-09-20 61457 59 433 0
2022-09-19 61398 49 433 0
2022-09-18 61349 7 433 1
2022-09-17 61342 23 432 0
2022-09-16 61319 28 432 0
2022-09-15 61291 40 432 0
2022-09-14 61251 40 432 0
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2022-09-11 61121 11 432 0
2022-09-10 61110 21 432 1
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2022-09-08 61024 58 430 0
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2022-08-12 59679 76 427 2
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2022-08-06 59179 18 424 0
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2022-07-29 58489 105 419 0
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2022-07-27 58299 121 418 3
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2022-07-20 57540 178 414 2
2022-07-19 57362 224 412 0
2022-07-18 57138 180 412 0
2022-07-17 56958 17 412 0
2022-07-16 56941 66 412 0
2022-07-15 56875 150 412 1
2022-07-14 56725 147 411 1
2022-07-13 56578 164 410 0
2022-07-12 56414 207 410 0
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2022-07-10 55971 9 409 1
2022-07-09 55962 58 408 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: 0:03:34.710363