Germany: LK Märkischer Kreis (Nordrhein-Westfalen)¶

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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 13:53:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Märkischer Kreis", weeks=5);
2023-03-07T13:53:59.974034 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 7-day incidence rate (per 100K people) LK Märkischer Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 80 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.05 0.10 0.15 0.20 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Märkischer Kreis cases daily growth factor Germany-LK Märkischer Kreis cases daily growth factor (rolling mean) Germany-LK Märkischer Kreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Märkischer Kreis deaths daily growth factor Germany-LK Märkischer Kreis deaths daily growth factor (rolling mean) Germany-LK Märkischer Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Märkischer Kreis doubling time cases (rolling mean) 0.0 81.4 162.7 244.1 325.4 daily change Germany-LK Märkischer Kreis new cases (rolling 7d mean) Germany-LK Märkischer Kreis new cases 0.000 0.203 0.407 0.610 0.814 daily change Germany-LK Märkischer Kreis new deaths (rolling 7d mean) Germany-LK Märkischer Kreis new deaths 0.000 0.270 0.539 0.809
In [4]:
overview(country="Germany", subregion="LK Märkischer Kreis");
2023-03-07T13:54:19.224448 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 151.2 LK Märkischer Kreis, Germany, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 100 200 300 400 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 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 Märkischer Kreis cases daily growth factor Germany-LK Märkischer Kreis cases daily growth factor (rolling mean) Germany-LK Märkischer Kreis estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Märkischer Kreis deaths daily growth factor Germany-LK Märkischer Kreis deaths daily growth factor (rolling mean) Germany-LK Märkischer Kreis estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Märkischer Kreis doubling time cases (rolling mean) Germany-LK Märkischer Kreis doubling time deaths (rolling mean) 0 407 814 1220 1627 daily change Germany-LK Märkischer Kreis new cases (rolling 7d mean) Germany-LK Märkischer Kreis new cases 0.00 2.03 4.07 6.10 8.14 daily change Germany-LK Märkischer Kreis new deaths (rolling 7d mean) Germany-LK Märkischer Kreis new deaths 0 237 473 710 947 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Märkischer Kreis", dates="2020-03-15:");
2023-03-07T13:56:18.986582 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 Märkischer Kreis LK Märkischer Kreis 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 Märkischer Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Märkischer Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Märkischer Kreis")
print(f'Population of country="Germany", subregion="LK Märkischer Kreis": {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 Märkischer Kreis": 406793 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 201579 39 818 0
2023-03-03 201540 22 818 0
2023-03-02 201518 49 818 0
2023-03-01 201469 117 818 0
2023-02-28 201352 388 818 0
2023-02-27 200964 1 818 0
2023-02-23 200963 112 818 0
2023-02-22 200851 105 818 0
2023-02-21 200746 124 818 0
2023-02-20 200622 126 818 0
2023-02-17 200496 66 818 0
2023-02-16 200430 54 818 0
2023-02-15 200376 127 818 0
2023-02-14 200249 126 818 0
2023-02-13 200123 118 818 0
2023-02-10 200005 71 818 0
2023-02-09 199934 83 818 0
2023-02-08 199851 69 818 1
2023-02-07 199782 109 817 0
2023-02-06 199673 114 817 0
2023-02-03 199559 51 817 1
2023-02-02 199508 77 816 0
2023-02-01 199431 103 816 0
2023-01-31 199328 132 816 0
2023-01-30 199196 156 816 1
2023-01-27 199040 63 815 0
2023-01-26 198977 77 815 0
2023-01-25 198900 96 815 0
2023-01-24 198804 124 815 0
2023-01-23 198680 147 815 0
2023-01-20 198533 37 815 0
2023-01-19 198496 95 815 1
2023-01-18 198401 61 814 0
2023-01-17 198340 117 814 0
2023-01-16 198223 139 814 0
2023-01-13 198084 51 814 1
2023-01-12 198033 59 813 0
2023-01-11 197974 113 813 0
2023-01-10 197861 142 813 1
2023-01-09 197719 331 812 4
2023-01-06 197388 62 808 1
2023-01-05 197326 177 807 1
2023-01-04 197149 157 806 0
2023-01-03 196992 287 806 0
2023-01-02 196705 405 806 3
2022-12-30 196300 138 803 0
2022-12-29 196162 322 803 2
2022-12-28 195840 338 801 3
2022-12-27 195502 579 798 2
2022-12-23 194923 180 796 0
2022-12-22 194743 387 796 1
2022-12-21 194356 332 795 0
2022-12-20 194024 449 795 4
2022-12-19 193575 663 791 7
2022-12-16 192912 189 784 2
2022-12-15 192723 276 782 3
2022-12-14 192447 436 779 3
2022-12-13 192011 347 776 0
2022-12-12 191664 711 776 3
2022-12-09 190953 151 773 0
2022-12-08 190802 288 773 0
2022-12-07 190514 242 773 4
2022-12-06 190272 303 769 2
2022-12-05 189969 469 767 0
2022-12-02 189500 212 767 2
2022-12-01 189288 239 765 0
2022-11-30 189049 291 765 1
2022-11-29 188758 332 764 1
2022-11-28 188426 533 763 3
2022-11-25 187893 141 760 0
2022-11-24 187752 133 760 0
2022-11-23 187619 205 760 0
2022-11-22 187414 247 760 1
2022-11-21 187167 409 759 1
2022-11-18 186758 258 758 0
2022-11-17 186500 218 758 0
2022-11-16 186282 277 758 0
2022-11-15 186005 140 758 1
2022-11-14 185865 357 757 0
2022-11-11 185508 121 757 2
2022-11-10 185387 181 755 0
2022-11-09 185206 221 755 1
2022-11-08 184985 322 754 2
2022-11-07 184663 536 752 2
2022-11-04 184127 196 750 0
2022-11-03 183931 383 750 0
2022-11-02 183548 475 750 1
2022-10-31 183073 688 749 4
2022-10-28 182385 226 745 0
2022-10-27 182159 402 745 0
2022-10-26 181757 349 745 0
2022-10-25 181408 644 745 0
2022-10-24 180764 914 745 5
2022-10-21 179850 298 740 1
2022-10-20 179552 478 739 0
2022-10-19 179074 579 739 2
2022-10-18 178495 746 737 4
2022-10-17 177749 1237 733 9
2022-10-14 176512 407 724 2
2022-10-13 176105 562 722 1
2022-10-12 175543 621 721 1
2022-10-11 174922 725 720 2
2022-10-10 174197 1186 718 4
2022-10-07 173011 457 714 2
2022-10-06 172554 643 712 0
2022-10-05 171911 699 712 0
2022-10-04 171212 1176 712 5
2022-09-30 170036 241 707 0
2022-09-29 169795 517 707 2
2022-09-28 169278 348 705 4
2022-09-27 168930 416 701 0
2022-09-26 168514 604 701 1
2022-09-23 167910 194 700 0
2022-09-22 167716 250 700 0
2022-09-21 167466 206 700 2
2022-09-20 167260 238 698 0
2022-09-19 167022 352 698 0
2022-09-16 166670 134 698 0
2022-09-15 166536 206 698 0
2022-09-14 166330 236 698 0
2022-09-13 166094 218 698 1
2022-09-12 165876 293 697 1
2022-09-09 165583 136 696 0
2022-09-08 165447 156 696 1
2022-09-07 165291 228 695 0
2022-09-06 165063 205 695 0
2022-09-05 164858 428 695 2
2022-09-02 164430 186 693 2
2022-09-01 164244 239 691 0
2022-08-31 164005 223 691 0
2022-08-30 163782 343 691 1
2022-08-29 163439 542 690 1
2022-08-26 162897 261 689 0
2022-08-25 162636 262 689 1
2022-08-24 162374 252 688 0
2022-08-23 162122 426 688 0
2022-08-22 161696 540 688 1
2022-08-19 161156 247 687 0
2022-08-18 160909 308 687 0
2022-08-17 160601 273 687 0
2022-08-16 160328 397 687 0
2022-08-15 159931 659 687 2
2022-08-12 159272 195 685 3
2022-08-11 159077 361 682 0
2022-08-10 158716 345 682 2
2022-08-09 158371 338 680 1
2022-08-08 158033 637 679 0
2022-08-05 157396 204 679 1
2022-08-04 157192 324 678 0
2022-08-03 156868 352 678 0
2022-08-02 156516 419 678 0
2022-08-01 156097 705 678 0
2022-07-29 155392 212 678 2
2022-07-28 155180 339 676 1
2022-07-27 154841 376 675 1
2022-07-26 154465 448 674 0
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2022-07-20 152262 422 669 0
2022-07-19 151840 590 669 1
2022-07-18 151250 967 668 4
2022-07-15 150283 420 664 1
2022-07-14 149863 544 663 0
2022-07-13 149319 603 663 1
2022-07-12 148716 750 662 0
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2022-06-30 142907 698 652 0
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2022-06-28 141317 958 651 2
2022-06-27 140359 1382 649 0
2022-06-24 138977 555 649 1
2022-06-23 138422 925 648 0
2022-06-22 137497 1031 648 0
2022-06-21 136466 780 648 0
2022-06-20 135686 1148 648 1
2022-06-17 134538 869 647 0
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2022-06-14 133162 661 647 0
2022-06-13 132501 732 647 0
2022-06-10 131769 380 647 0
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2022-06-08 130926 454 647 0
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2022-06-02 129629 233 645 0
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2022-05-31 129181 261 645 0
2022-05-30 128920 395 645 0
2022-05-27 128525 202 645 0
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2022-05-24 128167 220 645 0
2022-05-23 127947 486 645 2
2022-05-20 127461 185 643 0
2022-05-19 127276 294 643 1
2022-05-18 126982 310 642 1
2022-05-17 126672 341 641 0
2022-05-16 126331 804 641 0
2022-05-13 125527 318 641 0
2022-05-12 125209 340 641 0
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2022-05-09 123889 764 641 3
2022-05-06 123125 346 638 0
2022-05-05 122779 403 638 0
2022-05-04 122376 439 638 0
2022-05-03 121937 470 638 1
2022-05-02 121467 904 637 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: 0:04:02.240488