Germany: LK Karlsruhe (Baden-Württemberg)¶

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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 09:55:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Karlsruhe", weeks=5);
2023-03-07T09:55:20.303856 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 104.4 LK Karlsruhe, 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 0.00 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 R & growth factor (based on cases) Germany-LK Karlsruhe cases daily growth factor Germany-LK Karlsruhe cases daily growth factor (rolling mean) Germany-LK Karlsruhe 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Karlsruhe deaths daily growth factor Germany-LK Karlsruhe deaths daily growth factor (rolling mean) Germany-LK Karlsruhe 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 Karlsruhe doubling time cases (rolling mean) 0.0 44.8 89.7 134.5 daily change Germany-LK Karlsruhe new cases (rolling 7d mean) Germany-LK Karlsruhe new cases 0.000 0.224 0.448 0.673 0.897 daily change Germany-LK Karlsruhe new deaths (rolling 7d mean) Germany-LK Karlsruhe new deaths 0.000 0.264 0.528 0.792
In [4]:
overview(country="Germany", subregion="LK Karlsruhe");
2023-03-07T09:55:37.604089 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) 104.4 LK Karlsruhe, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Karlsruhe cases daily growth factor Germany-LK Karlsruhe cases daily growth factor (rolling mean) Germany-LK Karlsruhe estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Karlsruhe deaths daily growth factor Germany-LK Karlsruhe deaths daily growth factor (rolling mean) Germany-LK Karlsruhe 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 cases doubling time [days] Germany-LK Karlsruhe doubling time cases (rolling mean) Germany-LK Karlsruhe doubling time deaths (rolling mean) 0 897 1794 daily change Germany-LK Karlsruhe new cases (rolling 7d mean) Germany-LK Karlsruhe new cases 0.00 4.48 8.97 13.45 17.94 daily change Germany-LK Karlsruhe new deaths (rolling 7d mean) Germany-LK Karlsruhe new deaths 0 299 598 897 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Karlsruhe", dates="2020-03-15:");
2023-03-07T09:57:46.879421 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 Karlsruhe LK Karlsruhe 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 Karlsruhe Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Karlsruhe")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Karlsruhe")
print(f'Population of country="Germany", subregion="LK Karlsruhe": {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 Karlsruhe": 448487 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 210553 56 776 0
2023-03-03 210497 40 776 0
2023-03-02 210457 50 776 0
2023-03-01 210407 146 776 0
2023-02-28 210261 176 776 0
2023-02-27 210085 170 776 0
2023-02-24 209915 81 776 0
2023-02-23 209834 102 776 1
2023-02-22 209732 96 775 0
2023-02-21 209636 128 775 0
2023-02-20 209508 122 775 0
2023-02-17 209386 76 775 0
2023-02-16 209310 91 775 0
2023-02-15 209219 71 775 0
2023-02-14 209148 141 775 1
2023-02-13 209007 114 774 0
2023-02-10 208893 55 774 0
2023-02-09 208838 76 774 0
2023-02-08 208762 83 774 1
2023-02-07 208679 125 773 0
2023-02-06 208554 104 773 0
2023-02-03 208450 58 773 0
2023-02-02 208392 62 773 0
2023-02-01 208330 89 773 0
2023-01-31 208241 105 773 0
2023-01-30 208136 112 773 1
2023-01-27 208024 37 772 0
2023-01-26 207987 39 772 0
2023-01-25 207948 53 772 0
2023-01-24 207895 73 772 0
2023-01-23 207822 57 772 1
2023-01-20 207765 34 771 0
2023-01-19 207731 43 771 0
2023-01-18 207688 40 771 0
2023-01-17 207648 48 771 1
2023-01-16 207600 91 770 0
2023-01-13 207509 44 770 0
2023-01-12 207465 28 770 0
2023-01-11 207437 64 770 0
2023-01-10 207373 121 770 0
2023-01-09 207252 135 770 0
2023-01-05 207117 109 770 0
2023-01-04 207008 183 770 0
2023-01-03 206825 170 770 1
2023-01-02 206655 154 769 0
2022-12-30 206501 104 769 0
2022-12-29 206397 125 769 0
2022-12-28 206272 181 769 1
2022-12-27 206091 301 768 1
2022-12-23 205790 167 767 1
2022-12-22 205623 154 766 1
2022-12-21 205469 167 765 0
2022-12-20 205302 243 765 0
2022-12-19 205059 204 765 0
2022-12-17 204855 3 765 0
2022-12-16 204852 116 765 0
2022-12-15 204736 136 765 0
2022-12-14 204600 167 765 1
2022-12-13 204433 196 764 0
2022-12-12 204237 204 764 0
2022-12-09 204033 119 764 0
2022-12-08 203914 131 764 0
2022-12-07 203783 127 764 0
2022-12-06 203656 177 764 0
2022-12-05 203479 210 764 0
2022-12-02 203269 95 764 1
2022-12-01 203174 123 763 0
2022-11-30 203051 127 763 0
2022-11-29 202924 206 763 1
2022-11-28 202718 174 762 0
2022-11-25 202544 117 762 1
2022-11-24 202427 152 761 0
2022-11-23 202275 141 761 1
2022-11-22 202134 207 760 0
2022-11-21 201927 212 760 0
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2022-11-16 201453 160 760 3
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2022-11-13 200838 1 757 0
2022-11-12 200837 2 757 0
2022-11-11 200835 146 757 0
2022-11-10 200689 177 757 1
2022-11-09 200512 218 756 0
2022-11-08 200294 249 756 0
2022-11-07 200045 323 756 1
2022-11-04 199722 213 755 0
2022-11-03 199509 287 755 0
2022-11-02 199222 297 755 0
2022-11-01 198925 1 755 0
2022-10-31 198924 394 755 0
2022-10-28 198530 273 755 1
2022-10-27 198257 312 754 0
2022-10-26 197945 442 754 0
2022-10-25 197503 531 754 0
2022-10-24 196972 695 754 2
2022-10-23 196277 2 752 0
2022-10-22 196275 2 752 0
2022-10-21 196273 424 752 1
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2022-10-16 192835 1 748 0
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2022-10-14 192829 600 748 1
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2022-10-09 188542 4 741 0
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2022-10-02 184861 1 738 0
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2022-09-29 184410 460 738 1
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2022-09-27 183429 729 736 0
2022-09-26 182700 505 736 0
2022-09-23 182195 331 736 1
2022-09-22 181864 298 735 1
2022-09-21 181566 257 734 2
2022-09-20 181309 335 732 1
2022-09-19 180974 326 731 0
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2022-09-15 180490 214 731 0
2022-09-14 180276 190 731 0
2022-09-13 180086 316 731 0
2022-09-12 179770 244 731 1
2022-09-09 179526 157 730 0
2022-09-08 179369 150 730 0
2022-09-07 179219 171 730 1
2022-09-06 179048 265 729 0
2022-09-05 178783 213 729 0
2022-09-04 178570 2 729 0
2022-09-03 178568 3 729 0
2022-09-02 178565 143 729 0
2022-09-01 178422 147 729 0
2022-08-31 178275 141 729 0
2022-08-30 178134 205 729 0
2022-08-29 177929 153 729 0
2022-08-27 177776 69 729 0
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2022-07-28 171551 575 720 0
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2022-06-28 153620 850 715 0
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2022-06-25 152387 268 715 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: 0:03:01.888099