Germany: LK Saalekreis (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 15:36:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Saalekreis", weeks=5);
2023-03-07T15:36:30.834989 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 79.9 LK Saalekreis, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 daily change normalised per 100K 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 Saalekreis cases daily growth factor Germany-LK Saalekreis cases daily growth factor (rolling mean) Germany-LK Saalekreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Saalekreis deaths daily growth factor Germany-LK Saalekreis deaths daily growth factor (rolling mean) Germany-LK Saalekreis 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 Saalekreis doubling time cases (rolling mean) 0.00 18.28 36.56 daily change Germany-LK Saalekreis new cases (rolling 7d mean) Germany-LK Saalekreis new cases 0.000 0.366 0.731 daily change Germany-LK Saalekreis new deaths (rolling 7d mean) Germany-LK Saalekreis new deaths 0.000 0.293 0.586 0.879
In [4]:
overview(country="Germany", subregion="LK Saalekreis");
2023-03-07T15:36:51.364797 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) 79.9 LK Saalekreis, 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 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 Saalekreis cases daily growth factor Germany-LK Saalekreis cases daily growth factor (rolling mean) Germany-LK Saalekreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Saalekreis deaths daily growth factor Germany-LK Saalekreis deaths daily growth factor (rolling mean) Germany-LK Saalekreis 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 cases doubling time [days] Germany-LK Saalekreis doubling time cases (rolling mean) Germany-LK Saalekreis doubling time deaths (rolling mean) 0 366 731 1097 daily change Germany-LK Saalekreis new cases (rolling 7d mean) Germany-LK Saalekreis new cases 0.00 3.66 7.31 10.97 14.63 daily change Germany-LK Saalekreis new deaths (rolling 7d mean) Germany-LK Saalekreis new deaths 0.0 202.7 405.5 608.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Saalekreis", dates="2020-03-15:");
2023-03-07T15:39:29.988855 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 Saalekreis LK Saalekreis 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 Saalekreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Saalekreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Saalekreis")
print(f'Population of country="Germany", subregion="LK Saalekreis": {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 Saalekreis": 182814 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 88543 21 645 0
2023-03-03 88522 21 645 0
2023-03-02 88501 10 645 0
2023-03-01 88491 53 645 0
2023-02-28 88438 41 645 0
2023-02-27 88397 34 645 0
2023-02-24 88363 25 645 0
2023-02-23 88338 42 645 0
2023-02-22 88296 44 645 0
2023-02-21 88252 24 645 0
2023-02-20 88228 37 645 0
2023-02-17 88191 17 645 1
2023-02-16 88174 31 644 0
2023-02-15 88143 27 644 1
2023-02-14 88116 36 643 0
2023-02-13 88080 28 643 0
2023-02-10 88052 14 643 0
2023-02-09 88038 13 643 0
2023-02-08 88025 11 643 0
2023-02-07 88014 34 643 0
2023-02-06 87980 37 643 0
2023-02-03 87943 8 643 0
2023-02-02 87935 17 643 0
2023-02-01 87918 21 643 0
2023-01-31 87897 52 643 1
2023-01-30 87845 35 642 0
2023-01-27 87810 15 642 0
2023-01-26 87795 26 642 0
2023-01-25 87769 25 642 0
2023-01-24 87744 26 642 0
2023-01-23 87718 24 642 0
2023-01-20 87694 11 642 0
2023-01-19 87683 19 642 0
2023-01-18 87664 27 642 1
2023-01-17 87637 25 641 0
2023-01-16 87612 23 641 0
2023-01-13 87589 14 641 0
2023-01-12 87575 17 641 0
2023-01-11 87558 78 641 1
2023-01-10 87480 24 640 0
2023-01-09 87456 34 640 0
2023-01-05 87422 24 640 0
2023-01-04 87398 27 640 0
2023-01-03 87371 42 640 0
2023-01-02 87329 31 640 2
2022-12-30 87298 53 638 1
2022-12-29 87245 53 637 3
2022-12-28 87192 49 634 0
2022-12-27 87143 28 634 0
2022-12-23 87115 46 634 1
2022-12-22 87069 52 633 1
2022-12-21 87017 80 632 0
2022-12-20 86937 102 632 1
2022-12-19 86835 67 631 3
2022-12-16 86768 50 628 2
2022-12-15 86718 67 626 0
2022-12-14 86651 90 626 0
2022-12-13 86561 108 626 0
2022-12-12 86453 94 626 0
2022-12-09 86359 90 626 0
2022-12-08 86269 55 626 0
2022-12-07 86214 83 626 0
2022-12-06 86131 108 626 0
2022-12-05 86023 76 626 0
2022-12-02 85947 45 626 0
2022-12-01 85902 87 626 1
2022-11-30 85815 118 625 0
2022-11-29 85697 149 625 0
2022-11-28 85548 109 625 0
2022-11-25 85439 60 625 0
2022-11-24 85379 79 625 0
2022-11-23 85300 76 625 0
2022-11-22 85224 153 625 0
2022-11-21 85071 73 625 0
2022-11-18 84998 73 625 1
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2022-11-16 84862 81 624 0
2022-11-15 84781 127 624 0
2022-11-14 84654 72 624 1
2022-11-11 84582 52 623 1
2022-11-10 84530 71 622 0
2022-11-09 84459 73 622 2
2022-11-08 84386 152 620 0
2022-11-07 84234 106 620 0
2022-11-04 84128 72 620 1
2022-11-03 84056 121 619 1
2022-11-02 83935 161 618 2
2022-11-01 83774 124 616 0
2022-10-28 83650 67 616 0
2022-10-27 83583 142 616 2
2022-10-26 83441 121 614 1
2022-10-25 83320 266 613 2
2022-10-24 83054 204 611 1
2022-10-21 82850 143 610 1
2022-10-20 82707 278 609 1
2022-10-19 82429 236 608 0
2022-10-18 82193 369 608 3
2022-10-17 81824 304 605 2
2022-10-14 81520 143 603 0
2022-10-13 81377 304 603 0
2022-10-12 81073 206 603 0
2022-10-11 80867 402 603 0
2022-10-10 80465 471 603 2
2022-10-07 79994 166 601 0
2022-10-06 79828 393 601 0
2022-10-05 79435 354 601 0
2022-10-04 79081 323 601 3
2022-09-30 78758 151 598 0
2022-09-29 78607 122 598 0
2022-09-28 78485 193 598 0
2022-09-27 78292 287 598 1
2022-09-26 78005 129 597 0
2022-09-24 77876 1 597 0
2022-09-23 77875 108 597 0
2022-09-22 77767 102 597 0
2022-09-21 77665 158 597 0
2022-09-20 77507 156 597 2
2022-09-19 77351 151 595 0
2022-09-16 77200 58 595 0
2022-09-15 77142 75 595 0
2022-09-14 77067 109 595 0
2022-09-13 76958 137 595 1
2022-09-12 76821 101 594 0
2022-09-09 76720 71 594 0
2022-09-08 76649 113 594 1
2022-09-07 76536 108 593 0
2022-09-06 76428 170 593 0
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2022-08-30 75937 147 593 0
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2022-08-25 75626 71 592 0
2022-08-24 75555 88 592 0
2022-08-23 75467 169 592 0
2022-08-22 75298 100 592 0
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2022-08-18 75139 76 592 1
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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: 0:03:42.614549