Germany: LK Schmalkalden-Meiningen (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 16:15:56 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schmalkalden-Meiningen", weeks=5);
2023-03-07T16:16:14.505995 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) 71.3 LK Schmalkalden-Meiningen, 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 0.6 0.8 daily change normalised per 100K 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 Schmalkalden-Meiningen cases daily growth factor Germany-LK Schmalkalden-Meiningen cases daily growth factor (rolling mean) Germany-LK Schmalkalden-Meiningen estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Schmalkalden-Meiningen deaths daily growth factor Germany-LK Schmalkalden-Meiningen deaths daily growth factor (rolling mean) Germany-LK Schmalkalden-Meiningen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Schmalkalden-Meiningen doubling time cases (rolling mean) 0.00 12.34 24.68 daily change Germany-LK Schmalkalden-Meiningen new cases (rolling 7d mean) Germany-LK Schmalkalden-Meiningen new cases 0.000 0.247 0.494 0.740 0.987 daily change Germany-LK Schmalkalden-Meiningen new deaths (rolling 7d mean) Germany-LK Schmalkalden-Meiningen new deaths 0.000 0.342 0.684
In [4]:
overview(country="Germany", subregion="LK Schmalkalden-Meiningen");
2023-03-07T16:16:32.459554 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 71.3 LK Schmalkalden-Meiningen, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 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 Schmalkalden-Meiningen cases daily growth factor Germany-LK Schmalkalden-Meiningen cases daily growth factor (rolling mean) Germany-LK Schmalkalden-Meiningen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Schmalkalden-Meiningen deaths daily growth factor Germany-LK Schmalkalden-Meiningen deaths daily growth factor (rolling mean) Germany-LK Schmalkalden-Meiningen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Schmalkalden-Meiningen doubling time cases (rolling mean) Germany-LK Schmalkalden-Meiningen doubling time deaths (rolling mean) 0.0 123.4 246.8 370.2 493.6 daily change Germany-LK Schmalkalden-Meiningen new cases (rolling 7d mean) Germany-LK Schmalkalden-Meiningen new cases 0.00 3.09 6.17 9.26 12.34 daily change Germany-LK Schmalkalden-Meiningen new deaths (rolling 7d mean) Germany-LK Schmalkalden-Meiningen new deaths 0.0 174.2 348.4 522.5 696.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schmalkalden-Meiningen", dates="2020-03-15:");
2023-03-07T16:19:02.136429 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 Schmalkalden-Meiningen LK Schmalkalden-Meiningen 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 Schmalkalden-Meiningen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schmalkalden-Meiningen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schmalkalden-Meiningen")
print(f'Population of country="Germany", subregion="LK Schmalkalden-Meiningen": {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 Schmalkalden-Meiningen": 123404 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 57015 6 765 0
2023-03-05 57009 5 765 0
2023-03-04 57004 3 765 0
2023-03-03 57001 13 765 0
2023-03-02 56988 12 765 0
2023-03-01 56976 19 765 0
2023-02-28 56957 30 765 0
2023-02-27 56927 10 765 0
2023-02-26 56917 3 765 0
2023-02-25 56914 25 765 1
2023-02-24 56889 23 764 0
2023-02-23 56866 19 764 0
2023-02-22 56847 28 764 0
2023-02-21 56819 32 764 0
2023-02-20 56787 9 764 0
2023-02-19 56778 6 764 0
2023-02-18 56772 4 764 0
2023-02-17 56768 13 764 0
2023-02-16 56755 6 764 0
2023-02-15 56749 12 764 0
2023-02-14 56737 25 764 0
2023-02-13 56712 10 764 0
2023-02-12 56702 7 764 0
2023-02-11 56695 4 764 0
2023-02-10 56691 15 764 0
2023-02-09 56676 20 764 0
2023-02-08 56656 8 764 0
2023-02-07 56648 21 764 0
2023-02-06 56627 7 764 0
2023-02-05 56620 8 764 0
2023-02-04 56612 7 764 0
2023-02-03 56605 8 764 0
2023-02-02 56597 12 764 0
2023-02-01 56585 13 764 0
2023-01-31 56572 18 764 0
2023-01-30 56554 11 764 1
2023-01-29 56543 8 763 0
2023-01-28 56535 2 763 0
2023-01-27 56533 12 763 0
2023-01-26 56521 7 763 0
2023-01-25 56514 7 763 0
2023-01-24 56507 13 763 0
2023-01-23 56494 6 763 1
2023-01-22 56488 2 762 1
2023-01-21 56486 4 761 0
2023-01-20 56482 3 761 0
2023-01-19 56479 10 761 0
2023-01-18 56469 3 761 0
2023-01-17 56466 23 761 0
2023-01-16 56443 11 761 0
2023-01-15 56432 2 761 0
2023-01-14 56430 9 761 0
2023-01-13 56421 6 761 1
2023-01-12 56415 7 760 1
2023-01-11 56408 9 759 0
2023-01-10 56399 25 759 0
2023-01-09 56374 13 759 0
2023-01-08 56361 9 759 0
2023-01-07 56352 5 759 0
2023-01-06 56347 21 759 0
2023-01-05 56326 14 759 0
2023-01-04 56312 12 759 0
2023-01-03 56300 24 759 0
2023-01-02 56276 14 759 1
2023-01-01 56262 6 758 0
2022-12-31 56256 9 758 0
2022-12-30 56247 15 758 1
2022-12-29 56232 21 757 1
2022-12-28 56211 28 756 0
2022-12-27 56183 18 756 1
2022-12-26 56165 8 755 0
2022-12-25 56157 7 755 0
2022-12-24 56150 4 755 0
2022-12-23 56146 24 755 0
2022-12-22 56122 20 755 1
2022-12-21 56102 27 754 1
2022-12-20 56075 45 753 0
2022-12-19 56030 37 753 1
2022-12-18 55993 8 752 2
2022-12-17 55985 12 750 1
2022-12-16 55973 17 749 0
2022-12-15 55956 22 749 0
2022-12-14 55934 33 749 1
2022-12-13 55901 49 748 0
2022-12-12 55852 45 748 2
2022-12-11 55807 10 746 1
2022-12-10 55797 12 745 0
2022-12-09 55785 25 745 0
2022-12-08 55760 43 745 0
2022-12-07 55717 24 745 1
2022-12-06 55693 49 744 0
2022-12-05 55644 40 744 1
2022-12-04 55604 7 743 0
2022-12-03 55597 17 743 0
2022-12-02 55580 21 743 0
2022-12-01 55559 31 743 0
2022-11-30 55528 41 743 0
2022-11-29 55487 38 743 1
2022-11-28 55449 26 742 1
2022-11-27 55423 5 741 0
2022-11-26 55418 14 741 1
2022-11-25 55404 24 740 0
2022-11-24 55380 27 740 0
2022-11-23 55353 39 740 0
2022-11-22 55314 58 740 0
2022-11-21 55256 34 740 0
2022-11-20 55222 7 740 0
2022-11-19 55215 14 740 0
2022-11-18 55201 22 740 0
2022-11-17 55179 34 740 0
2022-11-16 55145 17 740 0
2022-11-15 55128 40 740 0
2022-11-14 55088 36 740 0
2022-11-13 55052 6 740 0
2022-11-12 55046 13 740 0
2022-11-11 55033 42 740 1
2022-11-10 54991 26 739 0
2022-11-09 54965 38 739 0
2022-11-08 54927 54 739 0
2022-11-07 54873 25 739 0
2022-11-06 54848 12 739 0
2022-11-05 54836 22 739 0
2022-11-04 54814 46 739 2
2022-11-03 54768 56 737 0
2022-11-02 54712 118 737 0
2022-11-01 54594 41 737 1
2022-10-31 54553 12 736 0
2022-10-30 54541 8 736 0
2022-10-29 54533 23 736 0
2022-10-28 54510 51 736 0
2022-10-27 54459 66 736 0
2022-10-26 54393 67 736 0
2022-10-25 54326 122 736 0
2022-10-24 54204 78 736 1
2022-10-23 54126 13 735 0
2022-10-22 54113 25 735 1
2022-10-21 54088 107 734 2
2022-10-20 53981 128 732 0
2022-10-19 53853 95 732 2
2022-10-18 53758 217 730 3
2022-10-17 53541 151 727 3
2022-10-16 53390 25 724 1
2022-10-15 53365 81 723 0
2022-10-14 53284 152 723 2
2022-10-13 53132 154 721 2
2022-10-12 52978 198 719 1
2022-10-11 52780 224 718 2
2022-10-10 52556 237 716 1
2022-10-09 52319 38 715 1
2022-10-08 52281 171 714 1
2022-10-07 52110 236 713 3
2022-10-06 51874 157 710 1
2022-10-05 51717 279 709 1
2022-10-04 51438 201 708 0
2022-10-03 51237 26 708 0
2022-10-02 51211 19 708 0
2022-10-01 51192 68 708 0
2022-09-30 51124 129 708 0
2022-09-29 50995 112 708 2
2022-09-28 50883 106 706 0
2022-09-27 50777 186 706 0
2022-09-26 50591 94 706 1
2022-09-25 50497 16 705 0
2022-09-24 50481 51 705 0
2022-09-23 50430 114 705 0
2022-09-22 50316 143 705 1
2022-09-21 50173 67 704 0
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2022-09-09 49602 45 703 0
2022-09-08 49557 64 703 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: 0:04:34.701546