Germany: LK Hochsauerlandkreis (Nordrhein-Westfalen)¶

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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 14:09:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hochsauerlandkreis", weeks=5);
2023-03-07T14:10:01.413711 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 107.1 LK Hochsauerlandkreis, 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 40 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 Hochsauerlandkreis new deaths (rolling 7d mean) Germany-LK Hochsauerlandkreis 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 R & growth factor (based on cases) Germany-LK Hochsauerlandkreis cases daily growth factor Germany-LK Hochsauerlandkreis cases daily growth factor (rolling mean) Germany-LK Hochsauerlandkreis 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 Hochsauerlandkreis deaths daily growth factor Germany-LK Hochsauerlandkreis deaths daily growth factor (rolling mean) Germany-LK Hochsauerlandkreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Hochsauerlandkreis doubling time cases (rolling mean) 0.0 25.9 51.7 77.6 103.4 daily change Germany-LK Hochsauerlandkreis new cases (rolling 7d mean) Germany-LK Hochsauerlandkreis new cases 0.000 0.246 0.491 0.737 0.982
In [4]:
overview(country="Germany", subregion="LK Hochsauerlandkreis");
2023-03-07T14:10:18.032245 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) 107.1 LK Hochsauerlandkreis, 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 0.0 0.5 1.0 1.5 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 Hochsauerlandkreis cases daily growth factor Germany-LK Hochsauerlandkreis cases daily growth factor (rolling mean) Germany-LK Hochsauerlandkreis 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 Hochsauerlandkreis deaths daily growth factor Germany-LK Hochsauerlandkreis deaths daily growth factor (rolling mean) Germany-LK Hochsauerlandkreis 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 cases doubling time [days] Germany-LK Hochsauerlandkreis doubling time cases (rolling mean) Germany-LK Hochsauerlandkreis doubling time deaths (rolling mean) 0 517 1034 daily change Germany-LK Hochsauerlandkreis new cases (rolling 7d mean) Germany-LK Hochsauerlandkreis new cases 0.000 1.293 2.586 3.879 daily change Germany-LK Hochsauerlandkreis new deaths (rolling 7d mean) Germany-LK Hochsauerlandkreis new deaths 0.0 80.8 161.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hochsauerlandkreis", dates="2020-03-15:");
2023-03-07T14:12:39.566196 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 Hochsauerlandkreis LK Hochsauerlandkreis 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 Hochsauerlandkreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hochsauerlandkreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hochsauerlandkreis")
print(f'Population of country="Germany", subregion="LK Hochsauerlandkreis": {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 Hochsauerlandkreis": 258615 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 114696 31 295 0
2023-03-03 114665 41 295 0
2023-03-02 114624 14 295 0
2023-03-01 114610 65 295 0
2023-02-28 114545 126 295 0
2023-02-27 114419 120 295 0
2023-02-26 114299 3 295 0
2023-02-24 114296 94 295 0
2023-02-23 114202 68 295 0
2023-02-22 114134 78 295 0
2023-02-21 114056 118 295 0
2023-02-20 113938 91 295 0
2023-02-17 113847 39 295 0
2023-02-16 113808 66 295 0
2023-02-15 113742 44 295 0
2023-02-14 113698 64 295 0
2023-02-13 113634 61 295 0
2023-02-10 113573 25 295 0
2023-02-09 113548 41 295 0
2023-02-08 113507 37 295 0
2023-02-07 113470 80 295 0
2023-02-06 113390 45 295 0
2023-02-03 113345 39 295 0
2023-02-02 113306 25 295 0
2023-02-01 113281 39 295 0
2023-01-31 113242 103 295 0
2023-01-30 113139 50 295 0
2023-01-27 113089 39 295 0
2023-01-26 113050 69 295 0
2023-01-25 112981 38 295 0
2023-01-24 112943 81 295 0
2023-01-23 112862 35 295 0
2023-01-20 112827 52 295 0
2023-01-19 112775 36 295 0
2023-01-18 112739 37 295 0
2023-01-17 112702 64 295 0
2023-01-16 112638 57 295 0
2023-01-14 112581 6 295 0
2023-01-13 112575 61 295 0
2023-01-12 112514 45 295 0
2023-01-11 112469 56 295 0
2023-01-10 112413 66 295 0
2023-01-09 112347 75 295 0
2023-01-07 112272 4 295 0
2023-01-06 112268 47 295 0
2023-01-05 112221 52 295 0
2023-01-04 112169 92 295 0
2023-01-03 112077 176 295 1
2023-01-02 111901 184 294 1
2022-12-31 111717 4 293 0
2022-12-30 111713 100 293 0
2022-12-29 111613 64 293 0
2022-12-28 111549 88 293 0
2022-12-27 111461 103 293 0
2022-12-23 111358 105 293 0
2022-12-22 111253 155 293 0
2022-12-21 111098 134 293 0
2022-12-20 110964 226 293 2
2022-12-19 110738 149 291 0
2022-12-16 110589 82 291 0
2022-12-15 110507 101 291 0
2022-12-14 110406 94 291 0
2022-12-13 110312 198 291 0
2022-12-12 110114 156 291 1
2022-12-09 109958 68 290 0
2022-12-08 109890 98 290 0
2022-12-07 109792 91 290 0
2022-12-06 109701 127 290 1
2022-12-05 109574 98 289 0
2022-12-02 109476 85 289 0
2022-12-01 109391 69 289 0
2022-11-30 109322 93 289 0
2022-11-29 109229 158 289 0
2022-11-28 109071 113 289 0
2022-11-25 108958 54 289 0
2022-11-24 108904 107 289 0
2022-11-23 108797 114 289 0
2022-11-22 108683 107 289 0
2022-11-21 108576 113 289 0
2022-11-18 108463 52 289 0
2022-11-17 108411 69 289 0
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2022-11-09 107819 162 289 0
2022-11-08 107657 191 289 0
2022-11-07 107466 174 289 1
2022-11-04 107292 137 288 0
2022-11-03 107155 356 288 0
2022-11-02 106799 254 288 0
2022-10-28 106545 212 288 0
2022-10-27 106333 281 288 0
2022-10-26 106052 301 288 0
2022-10-25 105751 243 288 0
2022-10-24 105508 371 288 1
2022-10-21 105137 309 287 0
2022-10-20 104828 244 287 0
2022-10-19 104584 289 287 1
2022-10-18 104295 328 286 0
2022-10-17 103967 303 286 0
2022-10-14 103664 215 286 0
2022-10-13 103449 240 286 0
2022-10-12 103209 333 286 0
2022-10-11 102876 388 286 0
2022-10-10 102488 275 286 1
2022-10-07 102213 295 285 2
2022-10-06 101918 421 283 0
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2022-09-06 98462 144 283 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: 0:03:49.954643