Germany: LK Wittenberg (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 15:36:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wittenberg", weeks=5);
2023-03-07T15:36:58.552047 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 60 60 7-day incidence rate (per 100K people) 47.0 LK Wittenberg, 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 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 Wittenberg new deaths (rolling 7d mean) Germany-LK Wittenberg 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 1.4 1.4 1.6 1.6 R & growth factor (based on cases) Germany-LK Wittenberg cases daily growth factor Germany-LK Wittenberg cases daily growth factor (rolling mean) Germany-LK Wittenberg 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 Wittenberg deaths daily growth factor Germany-LK Wittenberg deaths daily growth factor (rolling mean) Germany-LK Wittenberg 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 Wittenberg doubling time cases (rolling mean) 0.00 6.17 12.33 daily change Germany-LK Wittenberg new cases (rolling 7d mean) Germany-LK Wittenberg new cases 0.000 0.364 0.728
In [4]:
overview(country="Germany", subregion="LK Wittenberg");
2023-03-07T15:37:17.159791 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) 47.0 LK Wittenberg, 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 1.6 1.6 R & growth factor (based on cases) Germany-LK Wittenberg cases daily growth factor Germany-LK Wittenberg cases daily growth factor (rolling mean) Germany-LK Wittenberg 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 Wittenberg deaths daily growth factor Germany-LK Wittenberg deaths daily growth factor (rolling mean) Germany-LK Wittenberg 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 Wittenberg doubling time cases (rolling mean) Germany-LK Wittenberg doubling time deaths (rolling mean) 0.0 246.7 493.4 740.0 daily change Germany-LK Wittenberg new cases (rolling 7d mean) Germany-LK Wittenberg new cases 0.00 6.17 12.33 daily change Germany-LK Wittenberg new deaths (rolling 7d mean) Germany-LK Wittenberg new deaths 0.0 167.9 335.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wittenberg", dates="2020-03-15:");
2023-03-07T15:39:32.316020 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 Wittenberg LK Wittenberg 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 Wittenberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wittenberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wittenberg")
print(f'Population of country="Germany", subregion="LK Wittenberg": {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 Wittenberg": 123341 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 51926 3 455 0
2023-03-04 51923 4 455 0
2023-03-03 51919 12 455 0
2023-03-02 51907 11 455 0
2023-03-01 51896 14 455 0
2023-02-28 51882 14 455 0
2023-02-27 51868 15 455 0
2023-02-26 51853 1 455 0
2023-02-25 51852 2 455 0
2023-02-24 51850 7 455 0
2023-02-23 51843 8 455 0
2023-02-22 51835 9 455 0
2023-02-21 51826 15 455 0
2023-02-20 51811 17 455 0
2023-02-19 51794 1 455 0
2023-02-18 51793 8 455 0
2023-02-17 51785 14 455 0
2023-02-16 51771 6 455 0
2023-02-15 51765 10 455 0
2023-02-14 51755 10 455 0
2023-02-13 51745 6 455 0
2023-02-11 51739 6 455 0
2023-02-10 51733 1 455 0
2023-02-09 51732 5 455 0
2023-02-08 51727 4 455 0
2023-02-07 51723 12 455 0
2023-02-06 51711 3 455 0
2023-02-05 51708 1 455 0
2023-02-04 51707 1 455 0
2023-02-03 51706 6 455 0
2023-02-02 51700 4 455 0
2023-02-01 51696 3 455 0
2023-01-31 51693 9 455 0
2023-01-30 51684 2 455 0
2023-01-28 51682 4 455 0
2023-01-27 51678 2 455 0
2023-01-26 51676 6 455 0
2023-01-25 51670 10 455 0
2023-01-24 51660 13 455 1
2023-01-23 51647 5 454 0
2023-01-22 51642 2 454 0
2023-01-21 51640 2 454 0
2023-01-20 51638 2 454 0
2023-01-19 51636 12 454 0
2023-01-18 51624 8 454 0
2023-01-17 51616 11 454 0
2023-01-16 51605 17 454 0
2023-01-14 51588 3 454 0
2023-01-13 51585 12 454 0
2023-01-12 51573 5 454 0
2023-01-11 51568 16 454 0
2023-01-10 51552 27 454 0
2023-01-09 51525 15 454 0
2023-01-08 51510 1 454 0
2023-01-07 51509 3 454 0
2023-01-06 51506 7 454 2
2023-01-05 51499 9 452 0
2023-01-04 51490 29 452 0
2023-01-03 51461 27 452 0
2023-01-02 51434 15 452 1
2023-01-01 51419 1 451 0
2022-12-31 51418 7 451 0
2022-12-30 51411 29 451 0
2022-12-29 51382 38 451 1
2022-12-28 51344 37 450 0
2022-12-27 51307 26 450 0
2022-12-26 51281 18 450 0
2022-12-25 51263 4 450 0
2022-12-24 51259 16 450 0
2022-12-23 51243 29 450 0
2022-12-22 51214 18 450 0
2022-12-21 51196 42 450 0
2022-12-20 51154 44 450 0
2022-12-19 51110 27 450 0
2022-12-18 51083 7 450 0
2022-12-17 51076 10 450 0
2022-12-16 51066 31 450 1
2022-12-15 51035 23 449 0
2022-12-14 51012 39 449 0
2022-12-13 50973 71 449 0
2022-12-12 50902 38 449 0
2022-12-11 50864 5 449 0
2022-12-10 50859 11 449 0
2022-12-09 50848 34 449 1
2022-12-08 50814 36 448 0
2022-12-07 50778 51 448 1
2022-12-06 50727 81 447 0
2022-12-05 50646 26 447 0
2022-12-04 50620 4 447 0
2022-12-03 50616 19 447 0
2022-12-02 50597 36 447 0
2022-12-01 50561 46 447 2
2022-11-30 50515 46 445 0
2022-11-29 50469 84 445 0
2022-11-28 50385 27 445 0
2022-11-27 50358 3 445 1
2022-11-26 50355 21 444 0
2022-11-25 50334 21 444 0
2022-11-24 50313 27 444 0
2022-11-23 50286 32 444 0
2022-11-22 50254 55 444 0
2022-11-21 50199 26 444 0
2022-11-20 50173 7 444 0
2022-11-19 50166 9 444 0
2022-11-18 50157 27 444 0
2022-11-17 50130 23 444 0
2022-11-16 50107 29 444 0
2022-11-15 50078 59 444 1
2022-11-14 50019 16 443 0
2022-11-12 50003 11 443 0
2022-11-11 49992 32 443 0
2022-11-10 49960 50 443 0
2022-11-09 49910 33 443 0
2022-11-08 49877 72 443 1
2022-11-07 49805 32 442 0
2022-11-06 49773 3 442 0
2022-11-05 49770 19 442 0
2022-11-04 49751 36 442 0
2022-11-03 49715 31 442 0
2022-11-02 49684 84 442 0
2022-11-01 49600 57 442 0
2022-10-31 49543 10 442 0
2022-10-30 49533 5 442 0
2022-10-29 49528 27 442 0
2022-10-28 49501 60 442 0
2022-10-27 49441 59 442 0
2022-10-26 49382 82 442 0
2022-10-25 49300 108 442 0
2022-10-24 49192 88 442 3
2022-10-23 49104 5 439 0
2022-10-22 49099 31 439 0
2022-10-21 49068 107 439 0
2022-10-20 48961 125 439 0
2022-10-19 48836 142 439 0
2022-10-18 48694 162 439 0
2022-10-17 48532 108 439 0
2022-10-16 48424 12 439 1
2022-10-15 48412 33 438 0
2022-10-14 48379 134 438 0
2022-10-13 48245 102 438 0
2022-10-12 48143 162 438 0
2022-10-11 47981 191 438 1
2022-10-10 47790 79 437 0
2022-10-09 47711 36 437 0
2022-10-08 47675 89 437 0
2022-10-07 47586 151 437 0
2022-10-06 47435 113 437 1
2022-10-05 47322 184 436 1
2022-10-04 47138 113 435 1
2022-10-03 47025 19 434 0
2022-10-02 47006 5 434 0
2022-10-01 47001 40 434 0
2022-09-30 46961 98 434 0
2022-09-29 46863 69 434 0
2022-09-28 46794 82 434 0
2022-09-27 46712 131 434 0
2022-09-26 46581 65 434 1
2022-09-25 46516 8 433 0
2022-09-24 46508 31 433 0
2022-09-23 46477 56 433 0
2022-09-22 46421 56 433 0
2022-09-21 46365 69 433 0
2022-09-20 46296 85 433 0
2022-09-19 46211 29 433 0
2022-09-18 46182 4 433 0
2022-09-17 46178 26 433 0
2022-09-16 46152 51 433 0
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2022-09-03 45710 5 431 0
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2022-08-28 45501 2 430 0
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2022-08-22 45307 38 430 0
2022-08-21 45269 1 430 0
2022-08-20 45268 16 430 0
2022-08-19 45252 77 430 0
2022-08-18 45175 45 430 0
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2022-08-16 45095 81 430 0
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2022-08-13 44959 24 430 1
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2022-08-07 44682 9 429 0
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2022-08-02 44504 99 429 2
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2022-07-30 44364 24 427 0
2022-07-29 44340 51 427 1
2022-07-28 44289 57 426 1
2022-07-27 44232 76 425 0
2022-07-26 44156 129 425 0
2022-07-25 44027 33 425 2
2022-07-24 43994 9 423 0
2022-07-23 43985 24 423 0
2022-07-22 43961 124 423 1
2022-07-21 43837 67 422 0
2022-07-20 43770 84 422 0
2022-07-19 43686 154 422 1
2022-07-18 43532 51 421 0
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2022-07-16 43468 55 421 0
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2022-07-11 43023 83 420 1
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2022-07-09 42935 37 419 0
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2020-12-31 2262 50 115 4
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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: 0:02:57.873796