Germany: LK Wartburgkreis (Thüringen)¶

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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 16:24:58 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wartburgkreis", weeks=5);
2023-03-07T16:25:05.867433 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 7-day incidence rate (per 100K people) 55.4 LK Wartburgkreis, 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 0.0 0.2 0.4 0.6 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 Wartburgkreis cases daily growth factor Germany-LK Wartburgkreis cases daily growth factor (rolling mean) Germany-LK Wartburgkreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Wartburgkreis deaths daily growth factor Germany-LK Wartburgkreis deaths daily growth factor (rolling mean) Germany-LK Wartburgkreis 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 Wartburgkreis doubling time cases (rolling mean) 0.00 15.89 31.78 47.67 daily change Germany-LK Wartburgkreis new cases (rolling 7d mean) Germany-LK Wartburgkreis new cases 0.000 0.318 0.636 0.953 daily change Germany-LK Wartburgkreis new deaths (rolling 7d mean) Germany-LK Wartburgkreis new deaths 0.000 0.313 0.627 0.940
In [4]:
overview(country="Germany", subregion="LK Wartburgkreis");
2023-03-07T16:25:17.302887 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) 55.4 LK Wartburgkreis, 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 6 8 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 Wartburgkreis cases daily growth factor Germany-LK Wartburgkreis cases daily growth factor (rolling mean) Germany-LK Wartburgkreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Wartburgkreis deaths daily growth factor Germany-LK Wartburgkreis deaths daily growth factor (rolling mean) Germany-LK Wartburgkreis 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 Wartburgkreis doubling time cases (rolling mean) Germany-LK Wartburgkreis doubling time deaths (rolling mean) 0.0 158.9 317.8 476.7 635.6 daily change Germany-LK Wartburgkreis new cases (rolling 7d mean) Germany-LK Wartburgkreis new cases 0.00 3.18 6.36 9.53 12.71 daily change Germany-LK Wartburgkreis new deaths (rolling 7d mean) Germany-LK Wartburgkreis new deaths 0.0 72.9 145.7 218.6 291.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wartburgkreis", dates="2020-03-15:");
2023-03-07T16:25:59.137937 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 Wartburgkreis LK Wartburgkreis 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 Wartburgkreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wartburgkreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wartburgkreis")
print(f'Population of country="Germany", subregion="LK Wartburgkreis": {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 Wartburgkreis": 158900 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 64766 7 470 0
2023-03-02 64759 9 470 0
2023-03-01 64750 32 470 0
2023-02-28 64718 40 470 0
2023-02-27 64678 50 470 0
2023-02-24 64628 11 470 0
2023-02-23 64617 41 470 0
2023-02-22 64576 18 470 0
2023-02-21 64558 31 470 0
2023-02-20 64527 38 470 0
2023-02-17 64489 8 470 0
2023-02-16 64481 21 470 1
2023-02-15 64460 27 469 0
2023-02-14 64433 27 469 0
2023-02-13 64406 28 469 0
2023-02-10 64378 11 469 0
2023-02-09 64367 6 469 0
2023-02-08 64361 18 469 0
2023-02-07 64343 16 469 0
2023-02-06 64327 21 469 0
2023-02-03 64306 11 469 0
2023-02-02 64295 12 469 0
2023-02-01 64283 22 469 0
2023-01-31 64261 25 469 0
2023-01-30 64236 10 469 1
2023-01-27 64226 9 468 0
2023-01-26 64217 13 468 0
2023-01-25 64204 19 468 0
2023-01-24 64185 4 468 0
2023-01-23 64181 11 468 0
2023-01-20 64170 11 468 0
2023-01-19 64159 3 468 0
2023-01-18 64156 8 468 0
2023-01-17 64148 16 468 0
2023-01-16 64132 13 468 0
2023-01-13 64119 14 468 0
2023-01-12 64105 9 468 0
2023-01-11 64096 24 468 0
2023-01-10 64072 45 468 0
2023-01-09 64027 49 468 0
2023-01-06 63978 14 468 0
2023-01-05 63964 30 468 0
2023-01-04 63934 30 468 1
2023-01-03 63904 52 467 0
2023-01-02 63852 26 467 0
2022-12-30 63826 20 467 0
2022-12-29 63806 26 467 0
2022-12-28 63780 29 467 0
2022-12-27 63751 60 467 1
2022-12-23 63691 23 466 0
2022-12-22 63668 18 466 0
2022-12-21 63650 36 466 0
2022-12-20 63614 22 466 0
2022-12-19 63592 48 466 0
2022-12-16 63544 26 466 1
2022-12-15 63518 20 465 0
2022-12-14 63498 48 465 0
2022-12-13 63450 33 465 0
2022-12-12 63417 36 465 0
2022-12-09 63381 26 465 0
2022-12-08 63355 29 465 0
2022-12-07 63326 28 465 0
2022-12-06 63298 35 465 0
2022-12-05 63263 14 465 0
2022-12-02 63249 18 465 0
2022-12-01 63231 26 465 0
2022-11-30 63205 19 465 0
2022-11-29 63186 36 465 0
2022-11-28 63150 23 465 0
2022-11-25 63127 22 465 0
2022-11-24 63105 21 465 0
2022-11-23 63084 22 465 0
2022-11-22 63062 27 465 0
2022-11-21 63035 33 465 0
2022-11-19 63002 2 465 0
2022-11-18 63000 18 465 0
2022-11-17 62982 25 465 0
2022-11-16 62957 28 465 0
2022-11-15 62929 22 465 0
2022-11-14 62907 38 465 0
2022-11-13 62869 1 465 0
2022-11-11 62868 17 465 0
2022-11-10 62851 23 465 0
2022-11-09 62828 35 465 0
2022-11-08 62793 79 465 0
2022-11-07 62714 55 465 0
2022-11-04 62659 48 465 0
2022-11-03 62611 59 465 0
2022-11-02 62552 109 465 0
2022-11-01 62443 154 465 0
2022-10-28 62289 39 465 0
2022-10-27 62250 120 465 0
2022-10-26 62130 112 465 0
2022-10-25 62018 122 465 0
2022-10-24 61896 190 465 1
2022-10-23 61706 1 464 0
2022-10-21 61705 66 464 0
2022-10-20 61639 171 464 0
2022-10-19 61468 134 464 0
2022-10-18 61334 380 464 1
2022-10-17 60954 81 463 0
2022-10-16 60873 4 463 0
2022-10-15 60869 3 463 0
2022-10-14 60866 101 463 0
2022-10-13 60765 192 463 0
2022-10-12 60573 239 463 1
2022-10-11 60334 290 462 1
2022-10-10 60044 281 461 1
2022-10-07 59763 211 460 0
2022-10-06 59552 314 460 0
2022-10-05 59238 307 460 1
2022-10-04 58931 236 459 1
2022-10-02 58695 1 458 0
2022-10-01 58694 4 458 0
2022-09-30 58690 117 458 0
2022-09-29 58573 129 458 1
2022-09-28 58444 137 457 0
2022-09-27 58307 167 457 0
2022-09-26 58140 134 457 0
2022-09-23 58006 132 457 0
2022-09-22 57874 108 457 0
2022-09-21 57766 93 457 0
2022-09-19 57673 87 457 1
2022-09-16 57586 41 456 0
2022-09-15 57545 78 456 0
2022-09-14 57467 98 456 0
2022-09-13 57369 99 456 0
2022-09-12 57270 84 456 0
2022-09-11 57186 2 456 0
2022-09-09 57184 39 456 0
2022-09-08 57145 57 456 0
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2022-09-06 57013 77 456 0
2022-09-05 56936 65 456 0
2022-09-02 56871 34 456 0
2022-09-01 56837 33 456 0
2022-08-31 56804 54 456 0
2022-08-30 56750 74 456 0
2022-08-29 56676 49 456 0
2022-08-28 56627 1 456 0
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2022-08-23 56448 85 456 0
2022-08-22 56363 58 456 0
2022-08-19 56305 28 456 0
2022-08-18 56277 59 456 0
2022-08-17 56218 63 456 0
2022-08-16 56155 75 456 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: 0:01:04.648255