Germany: LK Altenburger Land (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 16:01:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Altenburger Land", weeks=5);
2023-03-07T16:02:03.897256 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 40 40 50 50 60 60 70 70 7-day incidence rate (per 100K people) 30.7 LK Altenburger Land, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Altenburger Land new deaths (rolling 7d mean) Germany-LK Altenburger Land new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Altenburger Land cases daily growth factor Germany-LK Altenburger Land cases daily growth factor (rolling mean) Germany-LK Altenburger Land 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 Altenburger Land deaths daily growth factor Germany-LK Altenburger Land deaths daily growth factor (rolling mean) Germany-LK Altenburger Land estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Altenburger Land doubling time cases (rolling mean) 0.00 4.39 8.78 13.17 17.56 daily change Germany-LK Altenburger Land new cases (rolling 7d mean) Germany-LK Altenburger Land new cases 0.000 0.223 0.445 0.668 0.890
In [4]:
overview(country="Germany", subregion="LK Altenburger Land");
2023-03-07T16:02:24.855292 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) 30.7 LK Altenburger Land, 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 5 10 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Altenburger Land cases daily growth factor Germany-LK Altenburger Land cases daily growth factor (rolling mean) Germany-LK Altenburger Land 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 Altenburger Land deaths daily growth factor Germany-LK Altenburger Land deaths daily growth factor (rolling mean) Germany-LK Altenburger Land estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Altenburger Land doubling time cases (rolling mean) Germany-LK Altenburger Land doubling time deaths (rolling mean) 0.0 175.6 351.2 526.8 daily change Germany-LK Altenburger Land new cases (rolling 7d mean) Germany-LK Altenburger Land new cases 0.00 4.39 8.78 daily change Germany-LK Altenburger Land new deaths (rolling 7d mean) Germany-LK Altenburger Land new deaths 0.0 149.7 299.5 449.2 598.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Altenburger Land", dates="2020-03-15:");
2023-03-07T16:04:35.200594 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 Altenburger Land LK Altenburger Land 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 Altenburger Land Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Altenburger Land")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Altenburger Land")
print(f'Population of country="Germany", subregion="LK Altenburger Land": {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 Altenburger Land": 87807 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 40429 3 530 0
2023-03-05 40426 2 530 0
2023-03-03 40424 4 530 0
2023-03-02 40420 6 530 0
2023-03-01 40414 5 530 0
2023-02-28 40409 7 530 0
2023-02-27 40402 7 530 0
2023-02-26 40395 2 530 0
2023-02-25 40393 3 530 0
2023-02-24 40390 4 530 0
2023-02-23 40386 13 530 0
2023-02-22 40373 8 530 0
2023-02-21 40365 21 530 0
2023-02-20 40344 9 530 0
2023-02-19 40335 4 530 0
2023-02-18 40331 4 530 0
2023-02-17 40327 4 530 0
2023-02-16 40323 7 530 0
2023-02-15 40316 13 530 0
2023-02-14 40303 10 530 0
2023-02-13 40293 2 530 0
2023-02-12 40291 1 530 0
2023-02-11 40290 2 530 0
2023-02-10 40288 4 530 0
2023-02-09 40284 5 530 0
2023-02-08 40279 10 530 0
2023-02-07 40269 10 530 0
2023-02-06 40259 7 530 0
2023-02-05 40252 2 530 0
2023-02-04 40250 4 530 0
2023-02-03 40246 5 530 0
2023-02-02 40241 7 530 0
2023-02-01 40234 12 530 0
2023-01-31 40222 11 530 0
2023-01-30 40211 9 530 0
2023-01-29 40202 2 530 0
2023-01-28 40200 3 530 0
2023-01-27 40197 4 530 0
2023-01-26 40193 6 530 0
2023-01-25 40187 9 530 0
2023-01-24 40178 5 530 0
2023-01-23 40173 9 530 0
2023-01-22 40164 1 530 0
2023-01-21 40163 1 530 0
2023-01-20 40162 7 530 0
2023-01-19 40155 2 530 0
2023-01-18 40153 5 530 0
2023-01-17 40148 10 530 0
2023-01-16 40138 6 530 0
2023-01-15 40132 9 530 0
2023-01-13 40123 3 530 0
2023-01-12 40120 5 530 0
2023-01-11 40115 15 530 0
2023-01-10 40100 6 530 0
2023-01-09 40094 5 530 0
2023-01-08 40089 1 530 0
2023-01-07 40088 11 530 0
2023-01-06 40077 4 530 0
2023-01-05 40073 10 530 0
2023-01-04 40063 13 530 0
2023-01-03 40050 21 530 0
2023-01-02 40029 9 530 0
2023-01-01 40020 6 530 0
2022-12-31 40014 8 530 0
2022-12-30 40006 14 530 0
2022-12-29 39992 18 530 0
2022-12-28 39974 25 530 0
2022-12-27 39949 15 530 1
2022-12-26 39934 9 529 0
2022-12-25 39925 2 529 0
2022-12-24 39923 13 529 0
2022-12-23 39910 14 529 0
2022-12-22 39896 19 529 0
2022-12-21 39877 34 529 0
2022-12-20 39843 32 529 0
2022-12-19 39811 21 529 1
2022-12-18 39790 8 528 0
2022-12-17 39782 11 528 0
2022-12-16 39771 16 528 0
2022-12-15 39755 21 528 0
2022-12-14 39734 47 528 2
2022-12-13 39687 60 526 0
2022-12-12 39627 32 526 0
2022-12-11 39595 8 526 0
2022-12-10 39587 17 526 0
2022-12-09 39570 43 526 2
2022-12-08 39527 26 524 1
2022-12-07 39501 42 523 0
2022-12-06 39459 50 523 2
2022-12-05 39409 31 521 1
2022-12-04 39378 12 520 0
2022-12-03 39366 19 520 0
2022-12-02 39347 34 520 0
2022-12-01 39313 44 520 1
2022-11-30 39269 26 519 1
2022-11-29 39243 54 518 1
2022-11-28 39189 29 517 2
2022-11-27 39160 11 515 0
2022-11-26 39149 25 515 0
2022-11-25 39124 39 515 0
2022-11-24 39085 31 515 1
2022-11-23 39054 30 514 2
2022-11-22 39024 48 512 0
2022-11-21 38976 26 512 1
2022-11-20 38950 20 511 1
2022-11-19 38930 16 510 0
2022-11-18 38914 22 510 0
2022-11-17 38892 35 510 0
2022-11-16 38857 26 510 0
2022-11-15 38831 35 510 0
2022-11-14 38796 28 510 0
2022-11-13 38768 4 510 0
2022-11-12 38764 18 510 0
2022-11-11 38746 35 510 0
2022-11-10 38711 28 510 0
2022-11-09 38683 38 510 0
2022-11-08 38645 35 510 1
2022-11-07 38610 38 509 0
2022-11-06 38572 5 509 0
2022-11-05 38567 24 509 0
2022-11-04 38543 43 509 0
2022-11-03 38500 21 509 0
2022-11-02 38479 69 509 0
2022-11-01 38410 45 509 1
2022-10-31 38365 25 508 0
2022-10-30 38340 17 508 1
2022-10-29 38323 45 507 1
2022-10-28 38278 43 506 1
2022-10-27 38235 48 505 1
2022-10-26 38187 65 504 0
2022-10-25 38122 97 504 1
2022-10-24 38025 60 503 1
2022-10-23 37965 27 502 0
2022-10-22 37938 29 502 0
2022-10-21 37909 112 502 0
2022-10-20 37797 49 502 0
2022-10-19 37748 93 502 0
2022-10-18 37655 80 502 1
2022-10-17 37575 115 501 1
2022-10-16 37460 66 500 0
2022-10-15 37394 28 500 0
2022-10-14 37366 93 500 1
2022-10-13 37273 82 499 0
2022-10-12 37191 108 499 0
2022-10-11 37083 79 499 0
2022-10-10 37004 74 499 1
2022-10-09 36930 67 498 0
2022-10-08 36863 32 498 1
2022-10-07 36831 124 497 0
2022-10-06 36707 122 497 0
2022-10-05 36585 181 497 1
2022-10-04 36404 66 496 1
2022-10-03 36338 45 495 0
2022-10-02 36293 39 495 0
2022-10-01 36254 32 495 1
2022-09-30 36222 100 494 2
2022-09-29 36122 46 492 0
2022-09-28 36076 106 492 1
2022-09-27 35970 74 491 0
2022-09-26 35896 82 491 0
2022-09-25 35814 18 491 0
2022-09-24 35796 35 491 2
2022-09-23 35761 76 489 1
2022-09-22 35685 64 488 1
2022-09-21 35621 81 487 0
2022-09-20 35540 47 487 0
2022-09-19 35493 14 487 0
2022-09-18 35479 18 487 0
2022-09-17 35461 13 487 0
2022-09-16 35448 53 487 0
2022-09-15 35395 21 487 0
2022-09-14 35374 29 487 0
2022-09-13 35345 69 487 0
2022-09-12 35276 18 487 0
2022-09-11 35258 25 487 0
2022-09-10 35233 6 487 0
2022-09-09 35227 35 487 0
2022-09-08 35192 10 487 0
2022-09-07 35182 38 487 0
2022-09-06 35144 32 487 0
2022-09-05 35112 33 487 0
2022-09-04 35079 11 487 0
2022-09-03 35068 8 487 0
2022-09-02 35060 25 487 0
2022-09-01 35035 27 487 0
2022-08-31 35008 25 487 0
2022-08-30 34983 47 487 0
2022-08-29 34936 35 487 0
2022-08-28 34901 21 487 0
2022-08-27 34880 12 487 0
2022-08-26 34868 46 487 0
2022-08-25 34822 7 487 0
2022-08-24 34815 34 487 0
2022-08-23 34781 46 487 0
2022-08-22 34735 39 487 0
2022-08-21 34696 14 487 0
2022-08-20 34682 18 487 0
2022-08-19 34664 28 487 0
2022-08-18 34636 43 487 0
2022-08-17 34593 54 487 0
2022-08-16 34539 54 487 0
2022-08-15 34485 43 487 1
2022-08-14 34442 30 486 0
2022-08-13 34412 8 486 0
2022-08-12 34404 27 486 0
2022-08-11 34377 42 486 0
2022-08-10 34335 37 486 0
2022-08-09 34298 32 486 0
2022-08-08 34266 42 486 1
2022-08-07 34224 31 485 0
2022-08-06 34193 24 485 0
2022-08-05 34169 30 485 0
2022-08-04 34139 49 485 0
2022-08-03 34090 50 485 1
2022-08-02 34040 52 484 1
2022-08-01 33988 55 483 0
2022-07-31 33933 36 483 1
2022-07-30 33897 3 482 0
2022-07-29 33894 55 482 0
2022-07-28 33839 39 482 0
2022-07-27 33800 83 482 0
2022-07-26 33717 81 482 1
2022-07-25 33636 83 481 1
2022-07-24 33553 24 480 0
2022-07-23 33529 22 480 0
2022-07-22 33507 64 480 0
2022-07-21 33443 75 480 0
2022-07-20 33368 57 480 1
2022-07-19 33311 116 479 1
2022-07-18 33195 72 478 1
2022-07-17 33123 68 477 0
2022-07-16 33055 24 477 0
2022-07-15 33031 92 477 0
2022-07-14 32939 60 477 0
2022-07-13 32879 77 477 0
2022-07-12 32802 83 477 0
2022-07-11 32719 55 477 2
2022-07-10 32664 36 475 0
2022-07-09 32628 15 475 1
2022-07-08 32613 51 474 1
2022-07-07 32562 40 473 1
2022-07-06 32522 40 472 0
2022-07-05 32482 57 472 0
2022-07-04 32425 76 472 0
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2022-06-26 32058 38 472 0
2022-06-25 32020 4 472 0
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2021-01-04 3108 12 173 1
2021-01-03 3096 57 172 12
2021-01-02 3039 23 160 5
2021-01-01 3016 40 155 6
2020-12-31 2976 86 149 1
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2020-12-29 2794 92 143 2
2020-12-28 2702 17 141 3
2020-12-27 2685 19 138 2
2020-12-25 2666 105 136 8
2020-12-24 2561 124 128 3
2020-12-23 2437 67 125 4
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2020-12-20 2237 1 114 0
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2020-12-15 1853 65 104 3
2020-12-14 1788 17 101 1
2020-12-13 1771 7 100 0
2020-12-12 1764 75 100 2
2020-12-11 1689 84 98 5
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2020-12-04 1296 54 67 3
2020-12-03 1242 99 64 7
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2020-12-01 1042 24 44 2
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2020-10-22 188 9 7 0
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2020-10-08 119 1 5 0
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2020-10-02 113 1 5 0
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2020-09-29 110 1 5 0
2020-09-24 109 1 5 0
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2020-09-15 105 1 5 0
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2020-08-28 92 10 5 0
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2020-06-29 76 1 5 0
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2020-06-08 74 1 5 0
2020-05-29 73 1 5 0
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2020-05-27 68 1 3 0
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2020-05-11 59 5 3 0
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2020-05-06 53 1 3 0
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2020-04-29 51 1 3 0
2020-04-28 50 2 3 0
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2020-04-23 47 1 3 0
2020-04-22 46 1 3 0
2020-04-21 45 1 3 0
2020-04-20 44 3 3 0
2020-04-16 41 3 3 0
2020-04-15 38 1 3 0
2020-04-14 37 1 3 0
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2020-04-09 34 1 3 1
2020-04-07 33 2 2 1
2020-04-05 31 1 1 0
2020-04-02 30 2 1 0
2020-04-01 28 4 1 0
2020-03-31 24 4 1 0
2020-03-28 20 1 1 0
2020-03-27 19 2 1 0
2020-03-25 17 1 1 0
2020-03-24 16 8 1 1
2020-03-22 8 1 0 0
2020-03-21 7 2 0 0
2020-03-17 5 2 0 0
2020-03-16 3 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: 0:02:51.409871