Germany: LK Salzlandkreis (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 15:45:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Salzlandkreis", weeks=5);
2023-03-07T15:46:09.504602 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) 66.3 LK Salzlandkreis, 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 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 Salzlandkreis new deaths (rolling 7d mean) Germany-LK Salzlandkreis 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 R & growth factor (based on cases) Germany-LK Salzlandkreis cases daily growth factor Germany-LK Salzlandkreis cases daily growth factor (rolling mean) Germany-LK Salzlandkreis 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 Salzlandkreis deaths daily growth factor Germany-LK Salzlandkreis deaths daily growth factor (rolling mean) Germany-LK Salzlandkreis 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 Salzlandkreis doubling time cases (rolling mean) 0.00 18.55 37.10 55.65 daily change Germany-LK Salzlandkreis new cases (rolling 7d mean) Germany-LK Salzlandkreis new cases 0.000 0.312 0.625 0.937
In [4]:
overview(country="Germany", subregion="LK Salzlandkreis");
2023-03-07T15:46:32.256208 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) 66.3 LK Salzlandkreis, 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Salzlandkreis cases daily growth factor Germany-LK Salzlandkreis cases daily growth factor (rolling mean) Germany-LK Salzlandkreis 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 Salzlandkreis deaths daily growth factor Germany-LK Salzlandkreis deaths daily growth factor (rolling mean) Germany-LK Salzlandkreis 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 Salzlandkreis doubling time cases (rolling mean) Germany-LK Salzlandkreis doubling time deaths (rolling mean) 0 371 742 1113 daily change Germany-LK Salzlandkreis new cases (rolling 7d mean) Germany-LK Salzlandkreis new cases 0.00 3.71 7.42 daily change Germany-LK Salzlandkreis new deaths (rolling 7d mean) Germany-LK Salzlandkreis new deaths 0.0 133.3 266.6 399.9 533.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Salzlandkreis", dates="2020-03-15:");
2023-03-07T15:49:03.040759 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 Salzlandkreis LK Salzlandkreis 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 Salzlandkreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Salzlandkreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Salzlandkreis")
print(f'Population of country="Germany", subregion="LK Salzlandkreis": {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 Salzlandkreis": 185495 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 77350 14 461 0
2023-03-03 77336 66 461 0
2023-03-02 77270 4 461 0
2023-03-01 77266 10 461 0
2023-02-28 77256 29 461 0
2023-02-27 77227 27 461 0
2023-02-24 77200 11 461 0
2023-02-23 77189 20 461 0
2023-02-22 77169 11 461 0
2023-02-21 77158 23 461 0
2023-02-20 77135 30 461 0
2023-02-17 77105 16 461 0
2023-02-16 77089 20 461 0
2023-02-15 77069 23 461 0
2023-02-14 77046 27 461 0
2023-02-13 77019 31 461 0
2023-02-10 76988 19 461 0
2023-02-09 76969 11 461 0
2023-02-08 76958 12 461 0
2023-02-07 76946 21 461 0
2023-02-06 76925 29 461 0
2023-02-03 76896 14 461 0
2023-02-02 76882 20 461 0
2023-02-01 76862 10 461 0
2023-01-31 76852 21 461 0
2023-01-30 76831 15 461 0
2023-01-27 76816 4 461 0
2023-01-26 76812 6 461 0
2023-01-25 76806 18 461 0
2023-01-24 76788 27 461 0
2023-01-23 76761 22 461 0
2023-01-20 76739 4 461 0
2023-01-19 76735 11 461 0
2023-01-18 76724 9 461 0
2023-01-17 76715 24 461 0
2023-01-16 76691 27 461 0
2023-01-13 76664 11 461 0
2023-01-12 76653 13 461 0
2023-01-11 76640 18 461 0
2023-01-10 76622 47 461 0
2023-01-09 76575 42 461 1
2023-01-05 76533 20 460 0
2023-01-04 76513 39 460 0
2023-01-03 76474 47 460 0
2023-01-02 76427 35 460 0
2022-12-30 76392 26 460 0
2022-12-29 76366 25 460 0
2022-12-28 76341 56 460 0
2022-12-27 76285 68 460 1
2022-12-23 76217 44 459 1
2022-12-22 76173 50 458 0
2022-12-21 76123 77 458 2
2022-12-20 76046 89 456 1
2022-12-19 75957 94 455 1
2022-12-16 75863 72 454 0
2022-12-15 75791 81 454 1
2022-12-14 75710 65 453 1
2022-12-13 75645 145 452 2
2022-12-12 75500 106 450 0
2022-12-09 75394 75 450 0
2022-12-08 75319 81 450 0
2022-12-07 75238 84 450 0
2022-12-06 75154 134 450 1
2022-12-05 75020 135 449 1
2022-12-02 74885 92 448 1
2022-12-01 74793 67 447 0
2022-11-30 74726 96 447 2
2022-11-29 74630 159 445 0
2022-11-28 74471 84 445 0
2022-11-25 74387 49 445 0
2022-11-24 74338 67 445 0
2022-11-23 74271 67 445 0
2022-11-22 74204 107 445 0
2022-11-21 74097 92 445 0
2022-11-18 74005 50 445 0
2022-11-17 73955 50 445 0
2022-11-16 73905 48 445 0
2022-11-15 73857 107 445 0
2022-11-14 73750 117 445 1
2022-11-11 73633 59 444 0
2022-11-10 73574 72 444 0
2022-11-09 73502 75 444 0
2022-11-08 73427 140 444 0
2022-11-07 73287 103 444 0
2022-11-04 73184 67 444 0
2022-11-03 73117 97 444 0
2022-11-02 73020 166 444 0
2022-11-01 72854 168 444 0
2022-10-28 72686 96 444 0
2022-10-27 72590 110 444 0
2022-10-26 72480 121 444 0
2022-10-25 72359 252 444 0
2022-10-24 72107 205 444 0
2022-10-21 71902 156 444 0
2022-10-20 71746 171 444 0
2022-10-19 71575 239 444 0
2022-10-18 71336 397 444 0
2022-10-17 70939 119 444 0
2022-10-16 70820 3 444 0
2022-10-15 70817 42 444 0
2022-10-14 70775 142 444 0
2022-10-13 70633 306 444 0
2022-10-12 70327 194 444 0
2022-10-11 70133 290 444 0
2022-10-10 69843 247 444 0
2022-10-07 69596 191 444 1
2022-10-06 69405 363 443 0
2022-10-05 69042 231 443 0
2022-10-04 68811 165 443 0
2022-09-30 68646 111 443 0
2022-09-29 68535 111 443 0
2022-09-28 68424 116 443 0
2022-09-27 68308 205 443 0
2022-09-26 68103 122 443 0
2022-09-23 67981 70 443 0
2022-09-22 67911 66 443 0
2022-09-21 67845 92 443 0
2022-09-20 67753 159 443 0
2022-09-19 67594 96 443 0
2022-09-16 67498 75 443 0
2022-09-15 67423 65 443 0
2022-09-14 67358 61 443 0
2022-09-13 67297 95 443 0
2022-09-12 67202 76 443 0
2022-09-09 67126 54 443 0
2022-09-08 67072 147 443 0
2022-09-07 66925 40 443 0
2022-09-06 66885 98 443 0
2022-09-05 66787 39 443 0
2022-09-02 66748 45 443 0
2022-09-01 66703 41 443 0
2022-08-31 66662 31 443 0
2022-08-30 66631 101 443 0
2022-08-29 66530 74 443 0
2022-08-26 66456 52 443 0
2022-08-25 66404 45 443 0
2022-08-24 66359 61 443 0
2022-08-23 66298 97 443 0
2022-08-22 66201 115 443 0
2022-08-19 66086 58 443 0
2022-08-18 66028 78 443 0
2022-08-17 65950 77 443 0
2022-08-16 65873 196 443 0
2022-08-15 65677 111 443 0
2022-08-12 65566 74 443 0
2022-08-11 65492 71 443 0
2022-08-10 65421 85 443 0
2022-08-09 65336 172 443 0
2022-08-08 65164 101 443 0
2022-08-05 65063 69 443 0
2022-08-04 64994 65 443 0
2022-08-03 64929 108 443 0
2022-08-02 64821 154 443 0
2022-08-01 64667 122 443 1
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2020-12-31 2843 57 99 3
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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: 0:03:19.071115