Germany: LK Nordsachsen (Sachsen)¶

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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 15:27:22 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Nordsachsen", weeks=5);
2023-03-07T15:27:40.504752 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 7-day incidence rate (per 100K people) 59.2 LK Nordsachsen, 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 0.0 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 Nordsachsen cases daily growth factor Germany-LK Nordsachsen cases daily growth factor (rolling mean) Germany-LK Nordsachsen estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Nordsachsen deaths daily growth factor Germany-LK Nordsachsen deaths daily growth factor (rolling mean) Germany-LK Nordsachsen 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 Nordsachsen doubling time cases (rolling mean) 0.00 9.88 19.75 29.63 39.51 daily change Germany-LK Nordsachsen new cases (rolling 7d mean) Germany-LK Nordsachsen new cases 0.000 0.395 0.790 daily change Germany-LK Nordsachsen new deaths (rolling 7d mean) Germany-LK Nordsachsen new deaths 0.000 0.255 0.510 0.765
In [4]:
overview(country="Germany", subregion="LK Nordsachsen");
2023-03-07T15:27:56.512752 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) 59.2 LK Nordsachsen, 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 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 Nordsachsen cases daily growth factor Germany-LK Nordsachsen cases daily growth factor (rolling mean) Germany-LK Nordsachsen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Nordsachsen deaths daily growth factor Germany-LK Nordsachsen deaths daily growth factor (rolling mean) Germany-LK Nordsachsen 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 20000 cases doubling time [days] Germany-LK Nordsachsen doubling time cases (rolling mean) Germany-LK Nordsachsen doubling time deaths (rolling mean) 0 395 790 1185 daily change Germany-LK Nordsachsen new cases (rolling 7d mean) Germany-LK Nordsachsen new cases 0.00 3.95 7.90 11.85 daily change Germany-LK Nordsachsen new deaths (rolling 7d mean) Germany-LK Nordsachsen new deaths 0.0 188.8 377.7 566.5 755.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Nordsachsen", dates="2020-03-15:");
2023-03-07T15:30:48.561987 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 Nordsachsen LK Nordsachsen 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 Nordsachsen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Nordsachsen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Nordsachsen")
print(f'Population of country="Germany", subregion="LK Nordsachsen": {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 Nordsachsen": 197529 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 90350 20 719 0
2023-03-03 90330 35 719 0
2023-03-02 90295 18 719 0
2023-03-01 90277 9 719 0
2023-02-28 90268 35 719 0
2023-02-27 90233 34 719 0
2023-02-24 90199 8 719 0
2023-02-23 90191 22 719 0
2023-02-22 90169 12 719 0
2023-02-21 90157 41 719 0
2023-02-20 90116 26 719 1
2023-02-17 90090 10 718 0
2023-02-16 90080 14 718 0
2023-02-15 90066 26 718 0
2023-02-14 90040 26 718 0
2023-02-13 90014 18 718 0
2023-02-10 89996 16 718 1
2023-02-09 89980 16 717 0
2023-02-08 89964 9 717 0
2023-02-07 89955 32 717 0
2023-02-06 89923 25 717 0
2023-02-03 89898 15 717 0
2023-02-02 89883 21 717 0
2023-02-01 89862 22 717 0
2023-01-31 89840 32 717 1
2023-01-30 89808 23 716 0
2023-01-27 89785 11 716 0
2023-01-26 89774 10 716 0
2023-01-25 89764 9 716 0
2023-01-24 89755 13 716 0
2023-01-23 89742 15 716 0
2023-01-20 89727 11 716 0
2023-01-19 89716 21 716 0
2023-01-18 89695 12 716 0
2023-01-17 89683 35 716 0
2023-01-16 89648 18 716 0
2023-01-13 89630 5 716 0
2023-01-12 89625 9 716 0
2023-01-11 89616 13 716 0
2023-01-10 89603 48 716 0
2023-01-09 89555 32 716 0
2023-01-06 89523 16 716 0
2023-01-05 89507 17 716 0
2023-01-04 89490 18 716 0
2023-01-03 89472 72 716 0
2023-01-02 89400 42 716 1
2022-12-30 89358 20 715 0
2022-12-29 89338 35 715 0
2022-12-28 89303 45 715 0
2022-12-27 89258 62 715 3
2022-12-23 89196 34 712 0
2022-12-22 89162 37 712 0
2022-12-21 89125 61 712 0
2022-12-20 89064 80 712 1
2022-12-19 88984 54 711 1
2022-12-16 88930 43 710 0
2022-12-15 88887 60 710 0
2022-12-14 88827 71 710 0
2022-12-13 88756 64 710 1
2022-12-12 88692 73 709 1
2022-12-09 88619 55 708 1
2022-12-08 88564 57 707 0
2022-12-07 88507 67 707 0
2022-12-06 88440 83 707 0
2022-12-05 88357 48 707 0
2022-12-02 88309 41 707 1
2022-12-01 88268 39 706 0
2022-11-30 88229 41 706 0
2022-11-29 88188 84 706 0
2022-11-28 88104 56 706 1
2022-11-25 88048 50 705 0
2022-11-24 87998 45 705 0
2022-11-23 87953 55 705 1
2022-11-22 87898 83 704 0
2022-11-21 87815 61 704 0
2022-11-18 87754 66 704 1
2022-11-17 87688 76 703 0
2022-11-15 87612 52 703 0
2022-11-14 87560 57 703 0
2022-11-11 87503 49 703 0
2022-11-10 87454 51 703 0
2022-11-09 87403 53 703 0
2022-11-08 87350 81 703 0
2022-11-07 87269 62 703 0
2022-11-04 87207 75 703 0
2022-11-03 87132 58 703 0
2022-11-02 87074 136 703 0
2022-11-01 86938 129 703 1
2022-10-28 86809 70 702 1
2022-10-27 86739 79 701 0
2022-10-26 86660 121 701 0
2022-10-25 86539 221 701 0
2022-10-24 86318 126 701 1
2022-10-23 86192 1 700 0
2022-10-21 86191 126 700 0
2022-10-20 86065 147 700 1
2022-10-19 85918 139 699 0
2022-10-18 85779 323 699 0
2022-10-17 85456 235 699 3
2022-10-14 85221 167 696 1
2022-10-13 85054 197 695 1
2022-10-12 84857 138 694 0
2022-10-11 84719 347 694 1
2022-10-10 84372 347 693 2
2022-10-07 84025 245 691 0
2022-10-06 83780 289 691 0
2022-10-05 83491 274 691 2
2022-10-04 83217 247 689 6
2022-10-03 82970 2 683 0
2022-09-30 82968 132 683 0
2022-09-29 82836 178 683 1
2022-09-28 82658 151 682 0
2022-09-27 82507 234 682 0
2022-09-26 82273 172 682 4
2022-09-24 82101 2 678 0
2022-09-23 82099 64 678 1
2022-09-22 82035 90 677 0
2022-09-21 81945 96 677 0
2022-09-20 81849 164 677 0
2022-09-19 81685 146 677 2
2022-09-16 81539 47 675 0
2022-09-15 81492 78 675 0
2022-09-14 81414 76 675 0
2022-09-13 81338 107 675 0
2022-09-12 81231 91 675 0
2022-09-10 81140 2 675 0
2022-09-09 81138 60 675 0
2022-09-08 81078 47 675 0
2022-09-07 81031 97 675 0
2022-09-06 80934 106 675 0
2022-09-05 80828 80 675 0
2022-09-02 80748 62 675 1
2022-09-01 80686 90 674 1
2022-08-31 80596 33 673 1
2022-08-30 80563 139 672 0
2022-08-29 80424 56 672 1
2022-08-26 80368 79 671 1
2022-08-25 80289 34 670 0
2022-08-24 80255 73 670 1
2022-08-23 80182 110 669 0
2022-08-22 80072 83 669 2
2022-08-19 79989 71 667 0
2022-08-18 79918 54 667 0
2022-08-17 79864 118 667 0
2022-08-16 79746 143 667 0
2022-08-15 79603 101 667 1
2022-08-12 79502 71 666 0
2022-08-11 79431 66 666 0
2022-08-10 79365 82 666 0
2022-08-09 79283 129 666 1
2022-08-08 79154 115 665 1
2022-08-05 79039 84 664 1
2022-08-04 78955 77 663 1
2022-08-03 78878 128 662 1
2022-08-02 78750 180 661 1
2022-08-01 78570 167 660 0
2022-07-29 78403 64 660 0
2022-07-28 78339 124 660 0
2022-07-27 78215 127 660 0
2022-07-26 78088 307 660 6
2022-07-25 77781 194 654 2
2022-07-22 77587 127 652 0
2022-07-21 77460 161 652 1
2022-07-20 77299 239 651 2
2022-07-19 77060 249 649 0
2022-07-18 76811 239 649 1
2022-07-17 76572 9 648 0
2022-07-16 76563 4 648 0
2022-07-15 76559 174 648 1
2022-07-14 76385 181 647 0
2022-07-13 76204 161 647 0
2022-07-12 76043 224 647 0
2022-07-11 75819 147 647 1
2022-07-10 75672 18 646 0
2022-07-09 75654 31 646 0
2022-07-08 75623 172 646 0
2022-07-07 75451 141 646 0
2022-07-06 75310 165 646 0
2022-07-05 75145 189 646 0
2022-07-04 74956 203 646 1
2022-07-03 74753 16 645 0
2022-07-02 74737 18 645 0
2022-07-01 74719 46 645 0
2022-06-30 74673 167 645 1
2022-06-29 74506 218 644 0
2022-06-28 74288 239 644 0
2022-06-27 74049 168 644 1
2022-06-26 73881 33 643 0
2022-06-25 73848 38 643 0
2022-06-24 73810 123 643 0
2022-06-23 73687 129 643 0
2022-06-22 73558 94 643 0
2022-06-21 73464 119 643 0
2022-06-20 73345 221 643 1
2022-06-19 73124 12 642 0
2022-06-18 73112 26 642 0
2022-06-17 73086 55 642 0
2022-06-16 73031 86 642 0
2022-06-15 72945 102 642 1
2022-06-14 72843 118 641 0
2022-06-13 72725 58 641 0
2022-06-12 72667 20 641 0
2022-06-11 72647 31 641 0
2022-06-10 72616 73 641 0
2022-06-09 72543 82 641 0
2022-06-08 72461 112 641 1
2022-06-07 72349 118 640 0
2022-06-06 72231 13 640 0
2022-06-05 72218 14 640 1
2022-06-04 72204 5 639 0
2022-06-03 72199 23 639 0
2022-06-02 72176 37 639 0
2022-06-01 72139 48 639 0
2022-05-31 72091 111 639 0
2022-05-30 71980 52 639 0
2022-05-29 71928 10 639 0
2022-05-28 71918 15 639 0
2022-05-27 71903 28 639 0
2022-05-26 71875 16 639 0
2022-05-25 71859 54 639 0
2022-05-24 71805 82 639 0
2022-05-23 71723 22 639 1
2022-05-22 71701 2 638 0
2022-05-21 71699 14 638 0
2022-05-20 71685 66 638 0
2022-05-19 71619 47 638 0
2022-05-18 71572 58 638 0
2022-05-17 71514 112 638 0
2022-05-16 71402 39 638 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: 0:04:35.266236