Germany: LK Alb-Donau-Kreis (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 09:44:03 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Alb-Donau-Kreis", weeks=5);
2023-03-07T09:44:18.345913 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 80 100 100 120 120 7-day incidence rate (per 100K people) 49.1 LK Alb-Donau-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 10 20 30 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 Alb-Donau-Kreis new deaths (rolling 7d mean) Germany-LK Alb-Donau-Kreis new deaths 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 Alb-Donau-Kreis cases daily growth factor Germany-LK Alb-Donau-Kreis cases daily growth factor (rolling mean) Germany-LK Alb-Donau-Kreis 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 Alb-Donau-Kreis deaths daily growth factor Germany-LK Alb-Donau-Kreis deaths daily growth factor (rolling mean) Germany-LK Alb-Donau-Kreis 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 Alb-Donau-Kreis doubling time cases (rolling mean) 0.00 19.97 39.95 59.92 daily change Germany-LK Alb-Donau-Kreis new cases (rolling 7d mean) Germany-LK Alb-Donau-Kreis new cases 0.000 0.399 0.799
In [4]:
overview(country="Germany", subregion="LK Alb-Donau-Kreis");
2023-03-07T09:44:39.984149 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) 49.1 LK Alb-Donau-Kreis, 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 Alb-Donau-Kreis cases daily growth factor Germany-LK Alb-Donau-Kreis cases daily growth factor (rolling mean) Germany-LK Alb-Donau-Kreis 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 Alb-Donau-Kreis deaths daily growth factor Germany-LK Alb-Donau-Kreis deaths daily growth factor (rolling mean) Germany-LK Alb-Donau-Kreis 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 cases doubling time [days] Germany-LK Alb-Donau-Kreis doubling time cases (rolling mean) Germany-LK Alb-Donau-Kreis doubling time deaths (rolling mean) 0 399 799 daily change Germany-LK Alb-Donau-Kreis new cases (rolling 7d mean) Germany-LK Alb-Donau-Kreis new cases 0.000 1.997 3.995 5.992 7.989 daily change Germany-LK Alb-Donau-Kreis new deaths (rolling 7d mean) Germany-LK Alb-Donau-Kreis new deaths 0.0 101.6 203.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Alb-Donau-Kreis", dates="2020-03-15:");
2023-03-07T09:47:03.006033 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 Alb-Donau-Kreis LK Alb-Donau-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 Alb-Donau-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Alb-Donau-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Alb-Donau-Kreis")
print(f'Population of country="Germany", subregion="LK Alb-Donau-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 Alb-Donau-Kreis": 199732 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 90921 14 307 0
2023-03-03 90907 14 307 0
2023-03-02 90893 16 307 0
2023-03-01 90877 22 307 0
2023-02-28 90855 32 307 0
2023-02-27 90823 66 307 0
2023-02-24 90757 19 307 0
2023-02-23 90738 40 307 0
2023-02-22 90698 42 307 0
2023-02-21 90656 44 307 0
2023-02-20 90612 65 307 0
2023-02-17 90547 29 307 0
2023-02-16 90518 43 307 0
2023-02-15 90475 34 307 0
2023-02-14 90441 53 307 0
2023-02-13 90388 64 307 0
2023-02-10 90324 44 307 0
2023-02-09 90280 33 307 0
2023-02-08 90247 30 307 0
2023-02-07 90217 33 307 0
2023-02-06 90184 51 307 0
2023-02-03 90133 29 307 0
2023-02-02 90104 24 307 0
2023-02-01 90080 34 307 0
2023-01-31 90046 27 307 0
2023-01-30 90019 32 307 0
2023-01-27 89987 28 307 0
2023-01-26 89959 22 307 0
2023-01-25 89937 21 307 0
2023-01-24 89916 32 307 0
2023-01-23 89884 35 307 0
2023-01-20 89849 24 307 0
2023-01-19 89825 21 307 0
2023-01-18 89804 19 307 0
2023-01-17 89785 24 307 0
2023-01-16 89761 27 307 0
2023-01-13 89734 19 307 0
2023-01-12 89715 26 307 0
2023-01-11 89689 29 307 0
2023-01-10 89660 24 307 0
2023-01-09 89636 60 307 2
2023-01-05 89576 13 305 0
2023-01-04 89563 18 305 1
2023-01-03 89545 25 304 0
2023-01-02 89520 62 304 0
2022-12-30 89458 24 304 0
2022-12-29 89434 39 304 0
2022-12-28 89395 38 304 0
2022-12-27 89357 83 304 1
2022-12-24 89274 1 303 0
2022-12-23 89273 35 303 0
2022-12-22 89238 44 303 0
2022-12-21 89194 44 303 0
2022-12-20 89150 59 303 0
2022-12-19 89091 64 303 1
2022-12-16 89027 28 302 0
2022-12-15 88999 31 302 0
2022-12-14 88968 63 302 0
2022-12-13 88905 38 302 1
2022-12-12 88867 46 301 1
2022-12-09 88821 36 300 0
2022-12-08 88785 32 300 0
2022-12-07 88753 45 300 0
2022-12-06 88708 88 300 0
2022-12-05 88620 51 300 1
2022-12-02 88569 15 299 1
2022-12-01 88554 28 298 0
2022-11-30 88526 46 298 0
2022-11-29 88480 42 298 0
2022-11-28 88438 46 298 0
2022-11-27 88392 1 298 0
2022-11-25 88391 25 298 0
2022-11-24 88366 39 298 0
2022-11-23 88327 28 298 0
2022-11-22 88299 56 298 0
2022-11-21 88243 47 298 0
2022-11-19 88196 1 298 0
2022-11-18 88195 52 298 0
2022-11-17 88143 40 298 0
2022-11-16 88103 52 298 0
2022-11-15 88051 69 298 0
2022-11-14 87982 68 298 0
2022-11-11 87914 37 298 0
2022-11-10 87877 38 298 0
2022-11-09 87839 53 298 0
2022-11-08 87786 83 298 1
2022-11-07 87703 105 297 0
2022-11-04 87598 61 297 0
2022-11-03 87537 71 297 0
2022-11-02 87466 102 297 0
2022-10-31 87364 72 297 0
2022-10-29 87292 45 297 0
2022-10-28 87247 81 297 0
2022-10-27 87166 108 297 1
2022-10-26 87058 134 296 1
2022-10-25 86924 199 295 1
2022-10-24 86725 183 294 0
2022-10-22 86542 2 294 0
2022-10-21 86540 131 294 0
2022-10-20 86409 160 294 0
2022-10-19 86249 266 294 0
2022-10-18 85983 248 294 0
2022-10-17 85735 357 294 1
2022-10-16 85378 1 293 0
2022-10-14 85377 240 293 3
2022-10-13 85137 280 290 0
2022-10-12 84857 337 290 0
2022-10-11 84520 301 290 0
2022-10-10 84219 414 290 0
2022-10-07 83805 274 290 1
2022-10-06 83531 330 289 1
2022-10-05 83201 248 288 0
2022-10-04 82953 322 288 0
2022-10-01 82631 3 288 0
2022-09-30 82628 163 288 0
2022-09-29 82465 173 288 0
2022-09-28 82292 163 288 0
2022-09-27 82129 174 288 0
2022-09-26 81955 218 288 0
2022-09-25 81737 5 288 0
2022-09-24 81732 2 288 0
2022-09-23 81730 70 288 0
2022-09-22 81660 102 288 0
2022-09-21 81558 80 288 0
2022-09-20 81478 87 288 0
2022-09-19 81391 105 288 0
2022-09-18 81286 1 288 0
2022-09-16 81285 46 288 0
2022-09-15 81239 52 288 0
2022-09-14 81187 64 288 0
2022-09-13 81123 63 288 0
2022-09-12 81060 54 288 0
2022-09-11 81006 2 288 0
2022-09-10 81004 3 288 0
2022-09-09 81001 50 288 0
2022-09-08 80951 44 288 0
2022-09-07 80907 43 288 0
2022-09-06 80864 62 288 0
2022-09-05 80802 78 288 0
2022-09-04 80724 1 288 0
2022-09-02 80723 28 288 0
2022-09-01 80695 42 288 0
2022-08-31 80653 63 288 0
2022-08-30 80590 69 288 0
2022-08-29 80521 75 288 0
2022-08-28 80446 1 288 0
2022-08-26 80445 52 288 1
2022-08-25 80393 48 287 0
2022-08-24 80345 60 287 0
2022-08-23 80285 69 287 0
2022-08-22 80216 56 287 0
2022-08-19 80160 57 287 0
2022-08-18 80103 34 287 0
2022-08-17 80069 76 287 0
2022-08-16 79993 88 287 0
2022-08-15 79905 67 287 0
2022-08-14 79838 2 287 0
2022-08-13 79836 2 287 0
2022-08-12 79834 57 287 0
2022-08-11 79777 61 287 0
2022-08-10 79716 76 287 0
2022-08-09 79640 111 287 0
2022-08-08 79529 128 287 0
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2022-08-05 79394 69 287 1
2022-08-04 79325 136 286 0
2022-08-03 79189 96 286 0
2022-08-02 79093 172 286 1
2022-08-01 78921 215 285 0
2022-07-31 78706 3 285 0
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2022-07-29 78701 119 285 0
2022-07-28 78582 215 285 0
2022-07-27 78367 236 285 0
2022-07-26 78131 291 285 0
2022-07-25 77840 374 285 3
2022-07-24 77466 1 282 0
2022-07-22 77465 213 282 0
2022-07-21 77252 286 282 1
2022-07-20 76966 369 281 0
2022-07-19 76597 372 281 0
2022-07-18 76225 476 281 0
2022-07-17 75749 3 281 0
2022-07-16 75746 1 281 0
2022-07-15 75745 394 281 2
2022-07-14 75351 251 279 0
2022-07-13 75100 358 279 0
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2022-07-11 74346 419 278 0
2022-07-09 73927 2 278 0
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2022-07-01 72378 152 278 0
2022-06-30 72226 204 278 0
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2022-06-27 71449 205 278 0
2022-06-26 71244 1 278 0
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2022-06-19 70316 3 278 0
2022-06-18 70313 2 278 0
2022-06-17 70311 155 278 0
2022-06-15 70156 150 278 1
2022-06-14 70006 172 277 0
2022-06-13 69834 153 277 2
2022-06-11 69681 1 275 0
2022-06-10 69680 114 275 0
2022-06-09 69566 88 275 0
2022-06-08 69478 109 275 0
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2022-06-05 69260 61 275 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: 0:04:42.441454