Germany: LK Olpe (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 14:17:35 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Olpe", weeks=5);
2023-03-07T14:17:51.213433 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 7-day incidence rate (per 100K people) 118.7 LK Olpe, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 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 Olpe new deaths (rolling 7d mean) Germany-LK Olpe new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Olpe cases daily growth factor Germany-LK Olpe cases daily growth factor (rolling mean) Germany-LK Olpe 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 Olpe deaths daily growth factor Germany-LK Olpe deaths daily growth factor (rolling mean) Germany-LK Olpe 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 Olpe doubling time cases (rolling mean) 0.0 26.6 53.2 79.9 daily change Germany-LK Olpe new cases (rolling 7d mean) Germany-LK Olpe new cases 0.000 0.203 0.406 0.609 0.811
In [4]:
overview(country="Germany", subregion="LK Olpe");
2023-03-07T14:18:10.767848 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) 118.7 LK Olpe, 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 0 200 400 600 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Olpe cases daily growth factor Germany-LK Olpe cases daily growth factor (rolling mean) Germany-LK Olpe 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 Olpe deaths daily growth factor Germany-LK Olpe deaths daily growth factor (rolling mean) Germany-LK Olpe 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 Olpe doubling time cases (rolling mean) Germany-LK Olpe doubling time deaths (rolling mean) 0 266 532 799 daily change Germany-LK Olpe new cases (rolling 7d mean) Germany-LK Olpe new cases 0.000 1.331 2.662 3.994 5.325 daily change Germany-LK Olpe new deaths (rolling 7d mean) Germany-LK Olpe new deaths 0.0 50.6 101.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Olpe", dates="2020-03-15:");
2023-03-07T14:20:19.050444 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 Olpe LK Olpe 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 Olpe Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Olpe")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Olpe")
print(f'Population of country="Germany", subregion="LK Olpe": {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 Olpe": 133120 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 48074 20 231 0
2023-03-03 48054 10 231 0
2023-03-02 48044 15 231 0
2023-03-01 48029 22 231 0
2023-02-28 48007 91 231 0
2023-02-24 47916 37 231 0
2023-02-23 47879 47 231 0
2023-02-22 47832 50 231 0
2023-02-21 47782 32 231 0
2023-02-20 47750 36 231 0
2023-02-17 47714 21 231 0
2023-02-16 47693 10 231 0
2023-02-15 47683 29 231 0
2023-02-14 47654 27 231 0
2023-02-13 47627 16 231 0
2023-02-10 47611 9 231 0
2023-02-09 47602 13 231 0
2023-02-08 47589 7 231 0
2023-02-07 47582 9 231 0
2023-02-06 47573 17 231 0
2023-02-03 47556 17 231 0
2023-02-02 47539 9 231 0
2023-02-01 47530 26 231 0
2023-01-31 47504 16 231 0
2023-01-30 47488 13 231 0
2023-01-27 47475 12 231 0
2023-01-26 47463 14 231 0
2023-01-25 47449 21 231 0
2023-01-24 47428 19 231 0
2023-01-23 47409 18 231 0
2023-01-20 47391 9 231 0
2023-01-19 47382 14 231 0
2023-01-18 47368 22 231 0
2023-01-17 47346 11 231 0
2023-01-16 47335 19 231 0
2023-01-13 47316 12 231 0
2023-01-12 47304 21 231 0
2023-01-11 47283 24 231 1
2023-01-10 47259 24 230 2
2023-01-09 47235 24 228 0
2023-01-06 47211 6 228 0
2023-01-05 47205 19 228 0
2023-01-04 47186 33 228 0
2023-01-03 47153 43 228 0
2023-01-02 47110 50 228 1
2022-12-30 47060 14 227 0
2022-12-29 47046 40 227 1
2022-12-28 47006 38 226 0
2022-12-27 46968 84 226 0
2022-12-23 46884 26 226 0
2022-12-22 46858 37 226 0
2022-12-21 46821 53 226 0
2022-12-20 46768 32 226 0
2022-12-19 46736 58 226 1
2022-12-16 46678 24 225 0
2022-12-15 46654 41 225 0
2022-12-14 46613 33 225 0
2022-12-13 46580 93 225 0
2022-12-09 46487 26 225 0
2022-12-08 46461 38 225 0
2022-12-07 46423 26 225 0
2022-12-06 46397 33 225 0
2022-12-05 46364 47 225 0
2022-12-02 46317 14 225 0
2022-12-01 46303 32 225 0
2022-11-30 46271 39 225 0
2022-11-29 46232 98 225 0
2022-11-28 46134 75 225 0
2022-11-25 46059 21 225 0
2022-11-24 46038 39 225 0
2022-11-23 45999 53 225 0
2022-11-22 45946 42 225 0
2022-11-21 45904 40 225 1
2022-11-18 45864 14 224 0
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2022-11-15 45752 45 224 0
2022-11-14 45707 72 224 1
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2022-11-10 45613 50 222 0
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2022-11-08 45484 62 222 0
2022-11-07 45422 81 222 1
2022-11-04 45341 33 221 0
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2022-10-25 44865 88 219 1
2022-10-24 44777 145 218 1
2022-10-21 44632 63 217 0
2022-10-20 44569 72 217 0
2022-10-19 44497 140 217 0
2022-10-18 44357 122 217 1
2022-10-17 44235 177 216 1
2022-10-14 44058 60 215 0
2022-10-13 43998 105 215 1
2022-10-12 43893 90 214 0
2022-10-11 43803 114 214 0
2022-10-10 43689 122 214 0
2022-10-07 43567 69 214 1
2022-10-06 43498 117 213 0
2022-10-05 43381 77 213 0
2022-10-04 43304 105 213 0
2022-09-30 43199 40 213 0
2022-09-29 43159 47 213 0
2022-09-28 43112 81 213 0
2022-09-27 43031 64 213 0
2022-09-26 42967 69 213 0
2022-09-23 42898 11 213 0
2022-09-22 42887 49 213 0
2022-09-21 42838 53 213 0
2022-09-20 42785 32 213 0
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2022-08-19 41920 23 212 0
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2022-08-17 41828 57 212 0
2022-08-16 41771 66 212 1
2022-08-15 41705 67 211 0
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2022-07-29 41101 37 210 1
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2020-03-27 89 23 1 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: 0:02:58.294375