Germany: LK Siegen-Wittgenstein (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 14:09:41 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Siegen-Wittgenstein", weeks=5);
2023-03-07T14:10:00.008685 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 102.4 LK Siegen-Wittgenstein, 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 0.1 0.2 0.3 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 R & growth factor (based on cases) Germany-LK Siegen-Wittgenstein cases daily growth factor Germany-LK Siegen-Wittgenstein cases daily growth factor (rolling mean) Germany-LK Siegen-Wittgenstein estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Siegen-Wittgenstein deaths daily growth factor Germany-LK Siegen-Wittgenstein deaths daily growth factor (rolling mean) Germany-LK Siegen-Wittgenstein 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 Siegen-Wittgenstein doubling time cases (rolling mean) 0.0 27.4 54.9 82.3 daily change Germany-LK Siegen-Wittgenstein new cases (rolling 7d mean) Germany-LK Siegen-Wittgenstein new cases 0.000 0.274 0.549 0.823 daily change Germany-LK Siegen-Wittgenstein new deaths (rolling 7d mean) Germany-LK Siegen-Wittgenstein new deaths 0.000 0.211 0.423 0.634 0.846
In [4]:
overview(country="Germany", subregion="LK Siegen-Wittgenstein");
2023-03-07T14:10:16.830387 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) 102.4 LK Siegen-Wittgenstein, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Siegen-Wittgenstein cases daily growth factor Germany-LK Siegen-Wittgenstein cases daily growth factor (rolling mean) Germany-LK Siegen-Wittgenstein estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Siegen-Wittgenstein deaths daily growth factor Germany-LK Siegen-Wittgenstein deaths daily growth factor (rolling mean) Germany-LK Siegen-Wittgenstein 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 Siegen-Wittgenstein doubling time cases (rolling mean) Germany-LK Siegen-Wittgenstein doubling time deaths (rolling mean) 0 274 549 823 1097 daily change Germany-LK Siegen-Wittgenstein new cases (rolling 7d mean) Germany-LK Siegen-Wittgenstein new cases 0.00 2.74 5.49 8.23 10.97 daily change Germany-LK Siegen-Wittgenstein new deaths (rolling 7d mean) Germany-LK Siegen-Wittgenstein new deaths 0.0 101.3 202.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Siegen-Wittgenstein", dates="2020-03-15:");
2023-03-07T14:12:33.295196 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 Siegen-Wittgenstein LK Siegen-Wittgenstein 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 Siegen-Wittgenstein Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Siegen-Wittgenstein")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Siegen-Wittgenstein")
print(f'Population of country="Germany", subregion="LK Siegen-Wittgenstein": {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 Siegen-Wittgenstein": 274342 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 132061 25 317 0
2023-03-03 132036 18 317 0
2023-03-02 132018 50 317 0
2023-03-01 131968 94 317 0
2023-02-28 131874 94 317 0
2023-02-27 131780 80 317 1
2023-02-24 131700 67 316 0
2023-02-23 131633 87 316 1
2023-02-22 131546 80 315 0
2023-02-21 131466 82 315 0
2023-02-20 131384 72 315 0
2023-02-17 131312 56 315 0
2023-02-16 131256 64 315 0
2023-02-15 131192 81 315 0
2023-02-14 131111 76 315 0
2023-02-13 131035 55 315 0
2023-02-10 130980 29 315 0
2023-02-09 130951 50 315 0
2023-02-08 130901 64 315 0
2023-02-07 130837 61 315 0
2023-02-06 130776 47 315 0
2023-02-03 130729 33 315 0
2023-02-02 130696 31 315 0
2023-02-01 130665 55 315 0
2023-01-31 130610 70 315 1
2023-01-30 130540 66 314 1
2023-01-27 130474 52 313 0
2023-01-26 130422 47 313 0
2023-01-25 130375 68 313 0
2023-01-24 130307 44 313 0
2023-01-23 130263 55 313 0
2023-01-20 130208 32 313 0
2023-01-19 130176 23 313 1
2023-01-18 130153 53 312 2
2023-01-17 130100 55 310 0
2023-01-16 130045 65 310 1
2023-01-13 129980 38 309 0
2023-01-12 129942 65 309 0
2023-01-11 129877 95 309 0
2023-01-10 129782 93 309 0
2023-01-09 129689 211 309 0
2023-01-06 129478 69 309 0
2023-01-05 129409 67 309 0
2023-01-04 129342 192 309 1
2023-01-03 129150 168 308 0
2023-01-02 128982 195 308 0
2022-12-30 128787 116 308 0
2022-12-29 128671 163 308 0
2022-12-28 128508 259 308 0
2022-12-27 128249 250 308 0
2022-12-23 127999 202 308 1
2022-12-22 127797 178 307 0
2022-12-21 127619 207 307 0
2022-12-20 127412 133 307 0
2022-12-19 127279 303 307 1
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2022-12-09 126077 117 306 0
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2022-12-06 125691 145 306 0
2022-12-05 125546 191 306 0
2022-12-02 125355 87 306 0
2022-12-01 125268 103 306 0
2022-11-30 125165 113 306 1
2022-11-29 125052 142 305 0
2022-11-28 124910 154 305 1
2022-11-25 124756 136 304 0
2022-11-24 124620 115 304 0
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2022-11-14 123522 192 303 2
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2022-10-31 121411 489 297 0
2022-10-28 120922 297 297 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: 0:03:05.022008