Germany: LK Heinsberg (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 13:45:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Heinsberg", weeks=5);
2023-03-07T13:45:33.415981 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 300 300 350 350 7-day incidence rate (per 100K people) 157.2 LK Heinsberg, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Heinsberg new deaths (rolling 7d mean) Germany-LK Heinsberg new deaths 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 Heinsberg cases daily growth factor Germany-LK Heinsberg cases daily growth factor (rolling mean) Germany-LK Heinsberg 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 Heinsberg deaths daily growth factor Germany-LK Heinsberg deaths daily growth factor (rolling mean) Germany-LK Heinsberg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] Germany-LK Heinsberg doubling time cases (rolling mean) 0.0 51.7 103.3 155.0 206.6 daily change Germany-LK Heinsberg new cases (rolling 7d mean) Germany-LK Heinsberg new cases 0.000 0.245 0.490 0.735 0.980
In [4]:
overview(country="Germany", subregion="LK Heinsberg");
2023-03-07T13:45:50.669070 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 7-day incidence rate (per 100K people) 157.2 LK Heinsberg, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 3 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Heinsberg cases daily growth factor Germany-LK Heinsberg cases daily growth factor (rolling mean) Germany-LK Heinsberg 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 Heinsberg deaths daily growth factor Germany-LK Heinsberg deaths daily growth factor (rolling mean) Germany-LK Heinsberg 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 Heinsberg doubling time cases (rolling mean) Germany-LK Heinsberg doubling time deaths (rolling mean) 0 258 517 775 daily change Germany-LK Heinsberg new cases (rolling 7d mean) Germany-LK Heinsberg new cases 0.00 2.58 5.17 7.75 10.33 daily change Germany-LK Heinsberg new deaths (rolling 7d mean) Germany-LK Heinsberg new deaths 0.0 196.6 393.2 589.8 786.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Heinsberg", dates="2020-03-15:");
2023-03-07T13:48:04.532604 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 Heinsberg LK Heinsberg 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 Heinsberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Heinsberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Heinsberg")
print(f'Population of country="Germany", subregion="LK Heinsberg": {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 Heinsberg": 258306 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 101698 35 629 0
2023-03-04 101663 5 629 0
2023-03-03 101658 31 629 0
2023-03-02 101627 52 629 0
2023-03-01 101575 91 629 0
2023-02-28 101484 192 629 0
2023-02-27 101292 153 629 0
2023-02-26 101139 14 629 0
2023-02-25 101125 39 629 0
2023-02-24 101086 171 629 0
2023-02-23 100915 212 629 0
2023-02-22 100703 166 629 0
2023-02-21 100537 111 629 0
2023-02-20 100426 18 629 0
2023-02-19 100408 3 629 0
2023-02-18 100405 11 629 0
2023-02-17 100394 88 629 0
2023-02-16 100306 62 629 0
2023-02-15 100244 127 629 0
2023-02-14 100117 102 629 0
2023-02-13 100015 96 629 0
2023-02-12 99919 5 629 0
2023-02-11 99914 11 629 0
2023-02-10 99903 70 629 0
2023-02-09 99833 62 629 0
2023-02-08 99771 83 629 0
2023-02-07 99688 130 629 0
2023-02-06 99558 70 629 0
2023-02-05 99488 9 629 0
2023-02-04 99479 23 629 0
2023-02-03 99456 60 629 0
2023-02-02 99396 70 629 0
2023-02-01 99326 97 629 0
2023-01-31 99229 104 629 0
2023-01-30 99125 59 629 0
2023-01-29 99066 12 629 0
2023-01-28 99054 14 629 0
2023-01-27 99040 71 629 0
2023-01-26 98969 52 629 0
2023-01-25 98917 55 629 0
2023-01-24 98862 60 629 0
2023-01-23 98802 57 629 0
2023-01-22 98745 6 629 0
2023-01-21 98739 7 629 0
2023-01-20 98732 61 629 0
2023-01-19 98671 47 629 0
2023-01-18 98624 66 629 0
2023-01-17 98558 56 629 0
2023-01-16 98502 25 629 0
2023-01-15 98477 2 629 0
2023-01-14 98475 16 629 0
2023-01-13 98459 47 629 1
2023-01-12 98412 66 628 0
2023-01-11 98346 55 628 0
2023-01-10 98291 55 628 0
2023-01-09 98236 59 628 0
2023-01-08 98177 10 628 0
2023-01-07 98167 21 628 0
2023-01-06 98146 67 628 0
2023-01-05 98079 96 628 0
2023-01-04 97983 123 628 0
2023-01-03 97860 293 628 0
2023-01-02 97567 254 628 0
2023-01-01 97313 13 628 0
2022-12-31 97300 18 628 0
2022-12-30 97282 40 628 0
2022-12-29 97242 55 628 0
2022-12-28 97187 63 628 0
2022-12-27 97124 67 628 0
2022-12-26 97057 9 628 0
2022-12-25 97048 18 628 0
2022-12-24 97030 26 628 0
2022-12-23 97004 144 628 1
2022-12-22 96860 169 627 0
2022-12-21 96691 219 627 0
2022-12-20 96472 195 627 1
2022-12-19 96277 169 626 0
2022-12-16 96108 100 626 0
2022-12-15 96008 107 626 0
2022-12-14 95901 124 626 0
2022-12-13 95777 201 626 1
2022-12-12 95576 175 625 0
2022-12-09 95401 107 625 0
2022-12-08 95294 119 625 1
2022-12-07 95175 98 624 0
2022-12-06 95077 124 624 0
2022-12-05 94953 119 624 0
2022-12-02 94834 85 624 2
2022-12-01 94749 98 622 0
2022-11-30 94651 106 622 0
2022-11-29 94545 161 622 0
2022-11-28 94384 113 622 1
2022-11-25 94271 80 621 0
2022-11-24 94191 81 621 0
2022-11-23 94110 94 621 0
2022-11-22 94016 156 621 0
2022-11-21 93860 111 621 0
2022-11-18 93749 114 621 0
2022-11-17 93635 73 621 1
2022-11-16 93562 111 620 0
2022-11-15 93451 137 620 1
2022-11-14 93314 121 619 0
2022-11-11 93193 88 619 0
2022-11-10 93105 94 619 1
2022-11-09 93011 128 618 1
2022-11-08 92883 182 617 1
2022-11-07 92701 173 616 1
2022-11-05 92528 1 615 0
2022-11-04 92527 118 615 0
2022-11-03 92409 260 615 0
2022-11-02 92149 476 615 3
2022-10-28 91673 167 612 0
2022-10-27 91506 198 612 0
2022-10-26 91308 202 612 0
2022-10-25 91106 329 612 0
2022-10-24 90777 350 612 2
2022-10-23 90427 1 610 0
2022-10-21 90426 121 610 1
2022-10-20 90305 265 609 2
2022-10-19 90040 322 607 3
2022-10-18 89718 359 604 0
2022-10-17 89359 361 604 1
2022-10-16 88998 1 603 0
2022-10-14 88997 209 603 0
2022-10-13 88788 449 603 4
2022-10-12 88339 322 599 2
2022-10-11 88017 174 597 0
2022-10-10 87843 285 597 0
2022-10-07 87558 184 597 2
2022-10-06 87374 302 595 0
2022-10-05 87072 361 595 2
2022-10-04 86711 320 593 0
2022-09-30 86391 122 593 2
2022-09-29 86269 130 591 0
2022-09-28 86139 169 591 1
2022-09-27 85970 168 590 0
2022-09-26 85802 165 590 1
2022-09-25 85637 5 589 0
2022-09-24 85632 5 589 0
2022-09-23 85627 62 589 0
2022-09-22 85565 88 589 1
2022-09-21 85477 109 588 0
2022-09-20 85368 144 588 0
2022-09-19 85224 98 588 0
2022-09-16 85126 74 588 0
2022-09-15 85052 65 588 0
2022-09-14 84987 71 588 0
2022-09-13 84916 116 588 1
2022-09-12 84800 99 587 0
2022-09-11 84701 1 587 0
2022-09-09 84700 55 587 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: 0:03:58.850248