Germany: LK Limburg-Weilburg (Hessen)¶

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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 12:36:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Limburg-Weilburg", weeks=5);
2023-03-07T12:36:34.892719 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 200 200 400 400 600 600 7-day incidence rate (per 100K people) 367.0 LK Limburg-Weilburg, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 150 200 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 Limburg-Weilburg new deaths (rolling 7d mean) Germany-LK Limburg-Weilburg 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 1.4 1.4 1.6 1.6 R & growth factor (based on cases) Germany-LK Limburg-Weilburg cases daily growth factor Germany-LK Limburg-Weilburg cases daily growth factor (rolling mean) Germany-LK Limburg-Weilburg 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 Limburg-Weilburg deaths daily growth factor Germany-LK Limburg-Weilburg deaths daily growth factor (rolling mean) Germany-LK Limburg-Weilburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-LK Limburg-Weilburg doubling time cases (rolling mean) 0.0 86.4 172.8 259.1 345.5 daily change Germany-LK Limburg-Weilburg new cases (rolling 7d mean) Germany-LK Limburg-Weilburg new cases 0.000 0.399 0.799
In [4]:
overview(country="Germany", subregion="LK Limburg-Weilburg");
2023-03-07T12:36:51.020202 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 1500 1500 7-day incidence rate (per 100K people) 367.0 LK Limburg-Weilburg, 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 2 4 6 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 1.6 1.6 R & growth factor (based on cases) Germany-LK Limburg-Weilburg cases daily growth factor Germany-LK Limburg-Weilburg cases daily growth factor (rolling mean) Germany-LK Limburg-Weilburg 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 Limburg-Weilburg deaths daily growth factor Germany-LK Limburg-Weilburg deaths daily growth factor (rolling mean) Germany-LK Limburg-Weilburg 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 Limburg-Weilburg doubling time cases (rolling mean) Germany-LK Limburg-Weilburg doubling time deaths (rolling mean) 0.0 172.8 345.5 518.3 daily change Germany-LK Limburg-Weilburg new cases (rolling 7d mean) Germany-LK Limburg-Weilburg new cases 0.00 3.46 6.91 10.37 daily change Germany-LK Limburg-Weilburg new deaths (rolling 7d mean) Germany-LK Limburg-Weilburg new deaths 0.0 127.1 254.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Limburg-Weilburg", dates="2020-03-15:");
2023-03-07T12:39:26.113469 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 Limburg-Weilburg LK Limburg-Weilburg 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 Limburg-Weilburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Limburg-Weilburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Limburg-Weilburg")
print(f'Population of country="Germany", subregion="LK Limburg-Weilburg": {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 Limburg-Weilburg": 172759 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 88658 47 385 0
2023-03-03 88611 46 385 0
2023-03-02 88565 54 385 0
2023-03-01 88511 92 385 0
2023-02-28 88419 395 385 0
2023-02-27 88024 257 385 0
2023-02-24 87767 111 385 0
2023-02-23 87656 116 385 0
2023-02-22 87540 149 385 0
2023-02-21 87391 144 385 0
2023-02-20 87247 136 385 0
2023-02-17 87111 79 385 0
2023-02-16 87032 58 385 0
2023-02-15 86974 58 385 0
2023-02-14 86916 60 385 0
2023-02-13 86856 95 385 0
2023-02-10 86761 61 385 0
2023-02-09 86700 61 385 0
2023-02-08 86639 98 385 0
2023-02-07 86541 71 385 0
2023-02-06 86470 96 385 0
2023-02-03 86374 34 385 0
2023-02-02 86340 44 385 0
2023-02-01 86296 25 385 0
2023-01-31 86271 30 385 0
2023-01-30 86241 42 385 0
2023-01-27 86199 39 385 0
2023-01-26 86160 31 385 0
2023-01-25 86129 32 385 0
2023-01-24 86097 47 385 0
2023-01-23 86050 43 385 0
2023-01-20 86007 30 385 0
2023-01-19 85977 17 385 0
2023-01-18 85960 45 385 0
2023-01-17 85915 41 385 0
2023-01-16 85874 55 385 0
2023-01-13 85819 18 385 0
2023-01-12 85801 35 385 1
2023-01-11 85766 47 384 0
2023-01-10 85719 50 384 0
2023-01-09 85669 119 384 0
2023-01-06 85550 72 384 0
2023-01-05 85478 46 384 0
2023-01-04 85432 56 384 0
2023-01-03 85376 66 384 0
2023-01-02 85310 141 384 0
2022-12-30 85169 96 384 0
2022-12-29 85073 1 384 0
2022-12-28 85072 99 384 0
2022-12-27 84973 85 384 0
2022-12-26 84888 34 384 0
2022-12-25 84854 36 384 0
2022-12-24 84818 34 384 0
2022-12-23 84784 98 384 0
2022-12-22 84686 91 384 0
2022-12-21 84595 100 384 0
2022-12-20 84495 102 384 0
2022-12-19 84393 74 384 0
2022-12-18 84319 27 384 0
2022-12-17 84292 57 384 0
2022-12-16 84235 62 384 0
2022-12-15 84173 87 384 0
2022-12-14 84086 94 384 0
2022-12-13 83992 93 384 0
2022-12-12 83899 93 384 0
2022-12-11 83806 25 384 0
2022-12-10 83781 30 384 0
2022-12-09 83751 67 384 0
2022-12-08 83684 43 384 0
2022-12-07 83641 74 384 0
2022-12-06 83567 89 384 0
2022-12-05 83478 51 384 0
2022-12-04 83427 33 384 0
2022-12-03 83394 31 384 0
2022-12-02 83363 46 384 0
2022-12-01 83317 47 384 0
2022-11-30 83270 62 384 0
2022-11-29 83208 71 384 0
2022-11-28 83137 95 384 0
2022-11-27 83042 20 384 1
2022-11-26 83022 29 383 0
2022-11-25 82993 47 383 0
2022-11-24 82946 37 383 0
2022-11-23 82909 54 383 0
2022-11-22 82855 51 383 0
2022-11-21 82804 125 383 0
2022-11-20 82679 15 383 0
2022-11-19 82664 27 383 0
2022-11-18 82637 60 383 0
2022-11-17 82577 78 383 0
2022-11-16 82499 78 383 0
2022-11-15 82421 69 383 0
2022-11-14 82352 58 383 0
2022-11-13 82294 36 383 0
2022-11-12 82258 16 383 0
2022-11-11 82242 83 383 0
2022-11-10 82159 61 383 0
2022-11-09 82098 77 383 0
2022-11-08 82021 85 383 0
2022-11-07 81936 87 383 2
2022-11-06 81849 25 381 0
2022-11-05 81824 48 381 0
2022-11-04 81776 80 381 0
2022-11-03 81696 70 381 0
2022-11-02 81626 115 381 0
2022-11-01 81511 133 381 1
2022-10-31 81378 118 380 0
2022-10-30 81260 51 380 0
2022-10-29 81209 50 380 0
2022-10-28 81159 118 380 0
2022-10-27 81041 143 380 0
2022-10-26 80898 145 380 0
2022-10-25 80753 242 380 0
2022-10-24 80511 195 380 0
2022-10-23 80316 90 380 0
2022-10-22 80226 108 380 0
2022-10-21 80118 327 380 0
2022-10-20 79791 240 380 0
2022-10-19 79551 272 380 0
2022-10-18 79279 459 380 1
2022-10-17 78820 412 379 2
2022-10-16 78408 117 377 1
2022-10-15 78291 202 376 0
2022-10-14 78089 306 376 0
2022-10-13 77783 395 376 1
2022-10-12 77388 315 375 2
2022-10-11 77073 512 373 0
2022-10-10 76561 506 373 2
2022-10-09 76055 116 371 1
2022-10-08 75939 163 370 1
2022-10-07 75776 286 369 0
2022-10-06 75490 526 369 0
2022-10-05 74964 443 369 0
2022-10-04 74521 509 369 2
2022-10-03 74012 124 367 0
2022-10-02 73888 75 367 0
2022-10-01 73813 181 367 1
2022-09-30 73632 295 366 1
2022-09-29 73337 356 365 0
2022-09-28 72981 246 365 0
2022-09-27 72735 389 365 1
2022-09-26 72346 291 364 0
2022-09-25 72055 113 364 0
2022-09-24 71942 169 364 0
2022-09-23 71773 229 364 0
2022-09-22 71544 256 364 0
2022-09-21 71288 217 364 0
2022-09-20 71071 229 364 0
2022-09-19 70842 228 364 0
2022-09-18 70614 71 364 0
2022-09-17 70543 85 364 0
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2022-09-13 70026 164 364 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: 0:03:44.268489