Germany: SK Leverkusen (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 14:32:51 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Leverkusen", weeks=5);
2023-03-07T14:33:05.505797 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 200 200 250 250 300 300 350 350 400 400 7-day incidence rate (per 100K people) 219.1 SK Leverkusen, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 25 50 75 100 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-SK Leverkusen new deaths (rolling 7d mean) Germany-SK Leverkusen 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-SK Leverkusen cases daily growth factor Germany-SK Leverkusen cases daily growth factor (rolling mean) Germany-SK Leverkusen 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-SK Leverkusen deaths daily growth factor Germany-SK Leverkusen deaths daily growth factor (rolling mean) Germany-SK Leverkusen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 cases doubling time [days] Germany-SK Leverkusen doubling time cases (rolling mean) 0.0 41.0 81.9 122.9 163.9 daily change Germany-SK Leverkusen new cases (rolling 7d mean) Germany-SK Leverkusen new cases 0.000 0.273 0.545 0.818
In [4]:
overview(country="Germany", subregion="SK Leverkusen");
2023-03-07T14:33:20.784119 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 219.1 SK Leverkusen, 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Leverkusen cases daily growth factor Germany-SK Leverkusen cases daily growth factor (rolling mean) Germany-SK Leverkusen 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-SK Leverkusen deaths daily growth factor Germany-SK Leverkusen deaths daily growth factor (rolling mean) Germany-SK Leverkusen 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-SK Leverkusen doubling time cases (rolling mean) Germany-SK Leverkusen doubling time deaths (rolling mean) 0.0 163.9 327.7 491.6 655.4 daily change Germany-SK Leverkusen new cases (rolling 7d mean) Germany-SK Leverkusen new cases 0.000 1.639 3.277 4.916 6.554 daily change Germany-SK Leverkusen new deaths (rolling 7d mean) Germany-SK Leverkusen new deaths 0.0 43.7 87.4 131.1 174.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Leverkusen", dates="2020-03-15:");
2023-03-07T14:35:22.641617 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: SK Leverkusen SK Leverkusen 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 SK Leverkusen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Leverkusen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Leverkusen")
print(f'Population of country="Germany", subregion="SK Leverkusen": {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="SK Leverkusen": 163851 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 77649 22 204 0
2023-03-03 77627 42 204 0
2023-03-02 77585 30 204 0
2023-03-01 77555 94 204 0
2023-02-28 77461 171 204 0
2023-02-27 77290 145 204 0
2023-02-24 77145 151 204 0
2023-02-23 76994 94 204 0
2023-02-22 76900 84 204 0
2023-02-21 76816 137 204 0
2023-02-17 76679 56 204 0
2023-02-16 76623 65 204 0
2023-02-15 76558 90 204 0
2023-02-14 76468 117 204 0
2023-02-13 76351 114 204 0
2023-02-11 76237 1 204 0
2023-02-10 76236 67 204 0
2023-02-09 76169 56 204 0
2023-02-08 76113 57 204 0
2023-02-07 76056 115 204 0
2023-02-06 75941 86 204 0
2023-02-03 75855 46 204 0
2023-02-02 75809 60 204 0
2023-02-01 75749 72 204 0
2023-01-31 75677 74 204 0
2023-01-30 75603 102 204 0
2023-01-27 75501 60 204 0
2023-01-26 75441 47 204 0
2023-01-25 75394 66 204 0
2023-01-24 75328 73 204 0
2023-01-23 75255 84 204 0
2023-01-20 75171 34 204 0
2023-01-19 75137 61 204 0
2023-01-18 75076 52 204 0
2023-01-17 75024 69 204 0
2023-01-16 74955 89 204 0
2023-01-13 74866 35 204 0
2023-01-12 74831 50 204 0
2023-01-11 74781 37 204 0
2023-01-10 74744 96 204 0
2023-01-09 74648 98 204 0
2023-01-06 74550 43 204 1
2023-01-05 74507 62 203 0
2023-01-04 74445 103 203 0
2023-01-03 74342 94 203 0
2023-01-02 74248 137 203 0
2022-12-30 74111 82 203 0
2022-12-29 74029 110 203 0
2022-12-28 73919 132 203 0
2022-12-27 73787 219 203 0
2022-12-23 73568 128 203 0
2022-12-22 73440 143 203 0
2022-12-21 73297 129 203 1
2022-12-20 73168 128 202 0
2022-12-19 73040 189 202 0
2022-12-16 72851 67 202 0
2022-12-15 72784 176 202 0
2022-12-14 72608 107 202 1
2022-12-13 72501 113 201 0
2022-12-12 72388 71 201 0
2022-12-09 72317 77 201 0
2022-12-08 72240 73 201 0
2022-12-07 72167 44 201 0
2022-12-06 72123 75 201 0
2022-12-05 72048 84 201 0
2022-12-02 71964 65 201 0
2022-12-01 71899 64 201 0
2022-11-30 71835 76 201 0
2022-11-29 71759 66 201 0
2022-11-28 71693 84 201 0
2022-11-25 71609 496 201 0
2022-11-24 71113 124 201 0
2022-11-23 70989 13 201 0
2022-11-22 70976 23 201 0
2022-11-21 70953 14 201 0
2022-11-18 70939 19 201 0
2022-11-17 70920 18 201 0
2022-11-16 70902 155 201 0
2022-11-15 70747 131 201 0
2022-11-14 70616 125 201 0
2022-11-11 70491 77 201 0
2022-11-10 70414 112 201 0
2022-11-09 70302 102 201 0
2022-11-08 70200 104 201 0
2022-11-07 70096 160 201 0
2022-11-04 69936 92 201 0
2022-11-03 69844 159 201 2
2022-11-02 69685 418 199 0
2022-10-28 69267 103 199 0
2022-10-27 69164 149 199 0
2022-10-26 69015 258 199 0
2022-10-25 68757 117 199 0
2022-10-24 68640 235 199 0
2022-10-21 68405 131 199 1
2022-10-20 68274 304 198 0
2022-10-19 67970 181 198 0
2022-10-18 67789 287 198 0
2022-10-17 67502 288 198 1
2022-10-14 67214 199 197 0
2022-10-13 67015 200 197 1
2022-10-12 66815 311 196 2
2022-10-11 66504 216 194 1
2022-10-10 66288 295 193 0
2022-10-07 65993 176 193 0
2022-10-06 65817 273 193 0
2022-10-05 65544 245 193 0
2022-10-04 65299 223 193 1
2022-09-30 65076 134 192 0
2022-09-29 64942 136 192 0
2022-09-28 64806 138 192 0
2022-09-27 64668 164 192 0
2022-09-26 64504 116 192 0
2022-09-23 64388 100 192 0
2022-09-22 64288 87 192 1
2022-09-21 64201 79 191 0
2022-09-20 64122 144 191 0
2022-09-19 63978 97 191 0
2022-09-16 63881 56 191 0
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2022-09-14 63753 74 191 1
2022-09-13 63679 88 190 0
2022-09-12 63591 98 190 0
2022-09-09 63493 71 190 0
2022-09-08 63422 76 190 0
2022-09-07 63346 117 190 0
2022-09-06 63229 158 190 0
2022-09-05 63071 90 190 0
2022-09-02 62981 99 190 0
2022-09-01 62882 96 190 0
2022-08-31 62786 129 190 1
2022-08-30 62657 177 189 0
2022-08-29 62480 149 189 0
2022-08-26 62331 117 189 0
2022-08-25 62214 93 189 0
2022-08-24 62121 163 189 0
2022-08-23 61958 170 189 0
2022-08-22 61788 157 189 0
2022-08-19 61631 128 189 0
2022-08-18 61503 133 189 0
2022-08-17 61370 184 189 0
2022-08-16 61186 217 189 1
2022-08-15 60969 128 188 0
2022-08-12 60841 115 188 0
2022-08-11 60726 159 188 0
2022-08-10 60567 164 188 0
2022-08-09 60403 208 188 0
2022-08-08 60195 149 188 0
2022-08-05 60046 130 188 0
2022-08-04 59916 115 188 0
2022-08-03 59801 179 188 0
2022-08-02 59622 192 188 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: 0:02:55.740653