Germany: LK Bodenseekreis (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 Bodenseekreis", weeks=5);
2023-03-07T09:44:18.267876 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 7-day incidence rate (per 100K people) 50.7 LK Bodenseekreis, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Bodenseekreis cases daily growth factor Germany-LK Bodenseekreis cases daily growth factor (rolling mean) Germany-LK Bodenseekreis 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 R & growth factor (based on deaths) Germany-LK Bodenseekreis deaths daily growth factor Germany-LK Bodenseekreis deaths daily growth factor (rolling mean) Germany-LK Bodenseekreis 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 Bodenseekreis doubling time cases (rolling mean) 0.00 21.89 43.78 daily change Germany-LK Bodenseekreis new cases (rolling 7d mean) Germany-LK Bodenseekreis new cases 0.000 0.219 0.438 0.657 0.876 daily change Germany-LK Bodenseekreis new deaths (rolling 7d mean) Germany-LK Bodenseekreis new deaths 0.000 0.315 0.631 0.946
In [4]:
overview(country="Germany", subregion="LK Bodenseekreis");
2023-03-07T09:44:39.325646 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) 50.7 LK Bodenseekreis, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Bodenseekreis cases daily growth factor Germany-LK Bodenseekreis cases daily growth factor (rolling mean) Germany-LK Bodenseekreis 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 R & growth factor (based on deaths) Germany-LK Bodenseekreis deaths daily growth factor Germany-LK Bodenseekreis deaths daily growth factor (rolling mean) Germany-LK Bodenseekreis estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Bodenseekreis doubling time cases (rolling mean) Germany-LK Bodenseekreis doubling time deaths (rolling mean) 0 219 438 657 876 daily change Germany-LK Bodenseekreis new cases (rolling 7d mean) Germany-LK Bodenseekreis new cases 0.00 4.38 8.76 daily change Germany-LK Bodenseekreis new deaths (rolling 7d mean) Germany-LK Bodenseekreis new deaths 0.0 98.1 196.2 294.3 392.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bodenseekreis", dates="2020-03-15:");
2023-03-07T09:46:59.925401 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 Bodenseekreis LK Bodenseekreis 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 Bodenseekreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bodenseekreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bodenseekreis")
print(f'Population of country="Germany", subregion="LK Bodenseekreis": {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 Bodenseekreis": 218885 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 92413 20 397 0
2023-03-02 92393 41 397 0
2023-02-28 92352 50 397 0
2023-02-27 92302 39 397 0
2023-02-24 92263 49 397 0
2023-02-23 92214 35 397 0
2023-02-22 92179 37 397 0
2023-02-21 92142 47 397 0
2023-02-20 92095 45 397 0
2023-02-17 92050 29 397 0
2023-02-16 92021 34 397 0
2023-02-15 91987 44 397 0
2023-02-14 91943 61 397 0
2023-02-13 91882 24 397 0
2023-02-10 91858 25 397 1
2023-02-09 91833 30 396 0
2023-02-08 91803 29 396 0
2023-02-07 91774 24 396 1
2023-02-06 91750 32 395 0
2023-02-03 91718 10 395 0
2023-02-02 91708 24 395 0
2023-02-01 91684 28 395 0
2023-01-31 91656 27 395 0
2023-01-30 91629 16 395 0
2023-01-27 91613 13 395 0
2023-01-26 91600 22 395 1
2023-01-25 91578 21 394 0
2023-01-24 91557 19 394 0
2023-01-23 91538 19 394 2
2023-01-20 91519 11 392 0
2023-01-19 91508 6 392 0
2023-01-18 91502 10 392 0
2023-01-17 91492 15 392 0
2023-01-16 91477 24 392 1
2023-01-13 91453 14 391 1
2023-01-12 91439 23 390 0
2023-01-11 91416 9 390 0
2023-01-10 91407 23 390 0
2023-01-09 91384 39 390 0
2023-01-05 91345 15 390 0
2023-01-04 91330 24 390 0
2023-01-03 91306 35 390 0
2023-01-02 91271 27 390 3
2022-12-30 91244 20 387 0
2022-12-29 91224 35 387 0
2022-12-28 91189 46 387 0
2022-12-27 91143 59 387 0
2022-12-23 91084 38 387 0
2022-12-22 91046 45 387 0
2022-12-21 91001 57 387 0
2022-12-20 90944 54 387 0
2022-12-19 90890 53 387 0
2022-12-16 90837 22 387 0
2022-12-15 90815 43 387 0
2022-12-14 90772 62 387 0
2022-12-13 90710 45 387 0
2022-12-12 90665 56 387 0
2022-12-09 90609 35 387 0
2022-12-08 90574 36 387 0
2022-12-07 90538 52 387 0
2022-12-06 90486 55 387 0
2022-12-05 90431 39 387 0
2022-12-02 90392 34 387 0
2022-12-01 90358 31 387 0
2022-11-30 90327 40 387 0
2022-11-29 90287 72 387 1
2022-11-28 90215 49 386 0
2022-11-25 90166 27 386 0
2022-11-24 90139 31 386 0
2022-11-23 90108 46 386 0
2022-11-22 90062 76 386 0
2022-11-21 89986 59 386 1
2022-11-18 89927 36 385 0
2022-11-17 89891 54 385 0
2022-11-16 89837 42 385 0
2022-11-15 89795 68 385 1
2022-11-14 89727 63 384 0
2022-11-11 89664 42 384 0
2022-11-10 89622 44 384 0
2022-11-09 89578 83 384 0
2022-11-08 89495 110 384 0
2022-11-07 89385 79 384 0
2022-11-04 89306 111 384 1
2022-11-03 89195 95 383 0
2022-11-02 89100 73 383 2
2022-10-31 89027 113 381 0
2022-10-28 88914 88 381 1
2022-10-27 88826 96 380 0
2022-10-26 88730 168 380 0
2022-10-25 88562 117 380 1
2022-10-24 88445 165 379 0
2022-10-21 88280 200 379 0
2022-10-20 88080 331 379 2
2022-10-19 87749 149 377 0
2022-10-18 87600 113 377 0
2022-10-17 87487 218 377 3
2022-10-14 87269 397 374 2
2022-10-13 86872 404 372 2
2022-10-12 86468 232 370 0
2022-10-11 86236 361 370 1
2022-10-10 85875 502 369 2
2022-10-07 85373 63 367 0
2022-10-06 85310 289 367 0
2022-10-05 85021 143 367 1
2022-10-04 84878 169 366 1
2022-09-30 84709 193 365 0
2022-09-29 84516 154 365 2
2022-09-28 84362 295 363 1
2022-09-27 84067 205 362 1
2022-09-26 83862 111 361 1
2022-09-23 83751 90 360 0
2022-09-22 83661 137 360 1
2022-09-21 83524 120 359 0
2022-09-20 83404 74 359 0
2022-09-19 83330 88 359 0
2022-09-16 83242 72 359 0
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2022-09-02 82652 37 359 0
2022-09-01 82615 32 359 1
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2022-08-30 82516 49 358 0
2022-08-29 82467 93 358 0
2022-08-26 82374 38 358 0
2022-08-25 82336 44 358 0
2022-08-24 82292 62 358 1
2022-08-23 82230 87 357 0
2022-08-22 82143 89 357 0
2022-08-19 82054 52 357 0
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2022-08-16 81882 128 357 0
2022-08-15 81754 99 357 0
2022-08-12 81655 80 357 0
2022-08-11 81575 91 357 0
2022-08-10 81484 101 357 0
2022-08-09 81383 149 357 0
2022-08-08 81234 124 357 1
2022-08-05 81110 260 356 1
2022-08-04 80850 45 355 0
2022-08-03 80805 137 355 2
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2022-08-01 80539 182 353 1
2022-07-29 80357 144 352 1
2022-07-28 80213 224 351 1
2022-07-27 79989 98 350 3
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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: 0:04:41.920954