Germany: LK Minden-Lübbecke (Nordrhein-Westfalen)¶

  • Homepage of project: https://oscovida.github.io
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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 14:02:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Minden-Lübbecke", weeks=5);
2023-03-07T14:02:21.063198 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 75.5 LK Minden-Lübbecke, 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 0.0 0.1 0.2 0.3 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Minden-Lübbecke cases daily growth factor Germany-LK Minden-Lübbecke cases daily growth factor (rolling mean) Germany-LK Minden-Lübbecke estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Minden-Lübbecke deaths daily growth factor Germany-LK Minden-Lübbecke deaths daily growth factor (rolling mean) Germany-LK Minden-Lübbecke estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Minden-Lübbecke doubling time cases (rolling mean) 0.0 62.2 124.5 186.7 daily change Germany-LK Minden-Lübbecke new cases (rolling 7d mean) Germany-LK Minden-Lübbecke new cases 0.000 0.311 0.622 0.934 daily change Germany-LK Minden-Lübbecke new deaths (rolling 7d mean) Germany-LK Minden-Lübbecke new deaths 0.000 0.335 0.669
In [4]:
overview(country="Germany", subregion="LK Minden-Lübbecke");
2023-03-07T14:02:41.477124 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) 75.5 LK Minden-Lübbecke, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Minden-Lübbecke cases daily growth factor Germany-LK Minden-Lübbecke cases daily growth factor (rolling mean) Germany-LK Minden-Lübbecke estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Minden-Lübbecke deaths daily growth factor Germany-LK Minden-Lübbecke deaths daily growth factor (rolling mean) Germany-LK Minden-Lübbecke 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 15000 20000 cases doubling time [days] Germany-LK Minden-Lübbecke doubling time cases (rolling mean) Germany-LK Minden-Lübbecke doubling time deaths (rolling mean) 0 622 1245 1867 daily change Germany-LK Minden-Lübbecke new cases (rolling 7d mean) Germany-LK Minden-Lübbecke new cases 0.00 3.11 6.22 9.34 12.45 daily change Germany-LK Minden-Lübbecke new deaths (rolling 7d mean) Germany-LK Minden-Lübbecke new deaths 0.0 132.6 265.1 397.7 530.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Minden-Lübbecke", dates="2020-03-15:");
2023-03-07T14:04:54.346816 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 Minden-Lübbecke LK Minden-Lübbecke 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 Minden-Lübbecke Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Minden-Lübbecke")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Minden-Lübbecke")
print(f'Population of country="Germany", subregion="LK Minden-Lübbecke": {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 Minden-Lübbecke": 311214 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 175826 31 502 0
2023-03-03 175795 12 502 0
2023-03-02 175783 31 502 0
2023-03-01 175752 86 502 0
2023-02-28 175666 75 502 0
2023-02-27 175591 170 502 0
2023-02-24 175421 107 502 0
2023-02-23 175314 122 502 0
2023-02-22 175192 115 502 0
2023-02-21 175077 135 502 0
2023-02-20 174942 192 502 0
2023-02-17 174750 83 502 0
2023-02-16 174667 72 502 0
2023-02-15 174595 118 502 0
2023-02-14 174477 180 502 0
2023-02-13 174297 162 502 0
2023-02-10 174135 74 502 0
2023-02-09 174061 52 502 0
2023-02-08 174009 79 502 0
2023-02-07 173930 107 502 0
2023-02-06 173823 100 502 1
2023-02-03 173723 58 501 0
2023-02-02 173665 62 501 0
2023-02-01 173603 82 501 0
2023-01-31 173521 73 501 0
2023-01-30 173448 162 501 0
2023-01-27 173286 65 501 0
2023-01-26 173221 93 501 0
2023-01-25 173128 78 501 0
2023-01-24 173050 78 501 0
2023-01-23 172972 120 501 1
2023-01-20 172852 57 500 0
2023-01-19 172795 48 500 0
2023-01-18 172747 36 500 0
2023-01-17 172711 69 500 0
2023-01-16 172642 87 500 0
2023-01-13 172555 57 500 0
2023-01-12 172498 67 500 0
2023-01-11 172431 65 500 0
2023-01-10 172366 106 500 0
2023-01-09 172260 194 500 0
2023-01-06 172066 85 500 0
2023-01-05 171981 119 500 0
2023-01-04 171862 150 500 1
2023-01-03 171712 223 499 0
2023-01-02 171489 355 499 1
2022-12-30 171134 216 498 0
2022-12-29 170918 213 498 0
2022-12-28 170705 281 498 0
2022-12-27 170424 645 498 0
2022-12-23 169779 254 498 0
2022-12-22 169525 336 498 0
2022-12-21 169189 430 498 0
2022-12-20 168759 324 498 0
2022-12-19 168435 579 498 1
2022-12-16 167856 282 497 0
2022-12-15 167574 276 497 0
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2022-12-13 166963 407 497 0
2022-12-12 166556 622 497 0
2022-12-09 165934 285 497 0
2022-12-08 165649 285 497 0
2022-12-07 165364 245 497 0
2022-12-06 165119 292 497 1
2022-12-05 164827 445 496 1
2022-12-03 164382 1 495 0
2022-12-02 164381 205 495 0
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2022-11-30 163971 266 495 0
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2022-11-23 162552 181 495 0
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2022-11-21 162156 351 495 1
2022-11-18 161805 181 494 0
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2022-10-27 157638 318 493 2
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2022-10-18 154394 538 491 2
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2022-10-13 152373 418 488 2
2022-10-12 151955 627 486 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: 0:03:08.218965