Germany: LK Kelheim (Bayern)¶

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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 10:55:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Kelheim", weeks=5);
2023-03-07T10:55:54.691338 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 7-day incidence rate (per 100K people) 89.6 LK Kelheim, 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 Kelheim new deaths (rolling 7d mean) Germany-LK Kelheim 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 R & growth factor (based on cases) Germany-LK Kelheim cases daily growth factor Germany-LK Kelheim cases daily growth factor (rolling mean) Germany-LK Kelheim 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 Kelheim deaths daily growth factor Germany-LK Kelheim deaths daily growth factor (rolling mean) Germany-LK Kelheim 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 Kelheim doubling time cases (rolling mean) 0.00 12.39 24.78 37.17 49.56 daily change Germany-LK Kelheim new cases (rolling 7d mean) Germany-LK Kelheim new cases 0.00 0.21 0.42 0.63 0.84
In [4]:
overview(country="Germany", subregion="LK Kelheim");
2023-03-07T10:56:14.349982 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) 89.6 LK Kelheim, 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 2 4 6 8 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 Kelheim cases daily growth factor Germany-LK Kelheim cases daily growth factor (rolling mean) Germany-LK Kelheim 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 Kelheim deaths daily growth factor Germany-LK Kelheim deaths daily growth factor (rolling mean) Germany-LK Kelheim 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 6000 cases doubling time [days] Germany-LK Kelheim doubling time cases (rolling mean) Germany-LK Kelheim doubling time deaths (rolling mean) 0.0 247.8 495.6 743.4 daily change Germany-LK Kelheim new cases (rolling 7d mean) Germany-LK Kelheim new cases 0.00 2.48 4.96 7.43 9.91 daily change Germany-LK Kelheim new deaths (rolling 7d mean) Germany-LK Kelheim new deaths 0.0 40.7 81.4 122.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Kelheim", dates="2020-03-15:");
2023-03-07T10:58:26.621728 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 Kelheim LK Kelheim 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 Kelheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Kelheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Kelheim")
print(f'Population of country="Germany", subregion="LK Kelheim": {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 Kelheim": 123899 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 66032 21 174 0
2023-03-03 66011 10 174 0
2023-03-02 66001 16 174 0
2023-03-01 65985 26 174 0
2023-02-28 65959 38 174 0
2023-02-27 65921 50 174 0
2023-02-24 65871 21 174 0
2023-02-23 65850 39 174 0
2023-02-22 65811 28 174 0
2023-02-21 65783 25 174 0
2023-02-20 65758 53 174 0
2023-02-17 65705 30 174 0
2023-02-16 65675 24 174 0
2023-02-15 65651 26 174 0
2023-02-14 65625 57 174 0
2023-02-13 65568 33 174 0
2023-02-10 65535 15 174 0
2023-02-09 65520 25 174 0
2023-02-08 65495 35 174 0
2023-02-07 65460 54 174 0
2023-02-06 65406 38 174 0
2023-02-03 65368 19 174 0
2023-02-02 65349 26 174 0
2023-02-01 65323 41 174 0
2023-01-31 65282 36 174 0
2023-01-30 65246 34 174 0
2023-01-27 65212 8 174 0
2023-01-26 65204 14 174 0
2023-01-25 65190 14 174 0
2023-01-24 65176 25 174 0
2023-01-23 65151 26 174 0
2023-01-20 65125 14 174 0
2023-01-19 65111 14 174 0
2023-01-18 65097 14 174 0
2023-01-17 65083 25 174 0
2023-01-16 65058 24 174 0
2023-01-13 65034 17 174 0
2023-01-12 65017 11 174 0
2023-01-11 65006 17 174 0
2023-01-10 64989 32 174 0
2023-01-09 64957 32 174 1
2023-01-05 64925 13 173 0
2023-01-04 64912 24 173 1
2023-01-03 64888 19 172 0
2023-01-02 64869 43 172 0
2022-12-30 64826 21 172 0
2022-12-29 64805 13 172 0
2022-12-28 64792 30 172 0
2022-12-27 64762 40 172 0
2022-12-23 64722 13 172 0
2022-12-22 64709 24 172 0
2022-12-21 64685 41 172 0
2022-12-20 64644 41 172 0
2022-12-19 64603 47 172 0
2022-12-16 64556 11 172 0
2022-12-15 64545 28 172 0
2022-12-14 64517 23 172 0
2022-12-13 64494 33 172 0
2022-12-12 64461 44 172 0
2022-12-09 64417 18 172 0
2022-12-08 64399 22 172 0
2022-12-07 64377 26 172 0
2022-12-06 64351 47 172 0
2022-12-05 64304 50 172 0
2022-12-02 64254 21 172 0
2022-12-01 64233 26 172 0
2022-11-30 64207 31 172 0
2022-11-29 64176 40 172 0
2022-11-28 64136 45 172 0
2022-11-25 64091 18 172 1
2022-11-24 64073 44 171 0
2022-11-23 64029 39 171 0
2022-11-22 63990 44 171 0
2022-11-21 63946 32 171 0
2022-11-18 63914 16 171 0
2022-11-17 63898 37 171 0
2022-11-16 63861 42 171 0
2022-11-15 63819 53 171 0
2022-11-14 63766 19 171 0
2022-11-12 63747 26 171 0
2022-11-11 63721 20 171 0
2022-11-10 63701 43 171 0
2022-11-09 63658 38 171 0
2022-11-08 63620 60 171 0
2022-11-07 63560 47 171 0
2022-11-05 63513 21 171 0
2022-11-04 63492 36 171 0
2022-11-03 63456 55 171 0
2022-11-02 63401 84 171 0
2022-10-31 63317 25 171 0
2022-10-30 63292 22 171 0
2022-10-29 63270 43 171 0
2022-10-28 63227 27 171 0
2022-10-27 63200 74 171 0
2022-10-26 63126 77 171 0
2022-10-25 63049 129 171 0
2022-10-24 62920 121 171 0
2022-10-23 62799 25 171 0
2022-10-22 62774 27 171 0
2022-10-21 62747 56 171 0
2022-10-20 62691 101 171 0
2022-10-19 62590 133 171 0
2022-10-18 62457 172 171 0
2022-10-17 62285 93 171 1
2022-10-16 62192 101 170 0
2022-10-15 62091 81 170 0
2022-10-14 62010 98 170 0
2022-10-13 61912 160 170 0
2022-10-12 61752 201 170 0
2022-10-11 61551 231 170 1
2022-10-10 61320 85 169 0
2022-10-09 61235 86 169 0
2022-10-08 61149 126 169 0
2022-10-07 61023 170 169 0
2022-10-06 60853 241 169 0
2022-10-05 60612 256 169 0
2022-10-04 60356 115 169 0
2022-10-03 60241 63 169 0
2022-10-02 60178 75 169 0
2022-10-01 60103 131 169 0
2022-09-30 59972 96 169 0
2022-09-29 59876 176 169 0
2022-09-28 59700 238 169 0
2022-09-27 59462 217 169 0
2022-09-26 59245 115 169 0
2022-09-25 59130 38 169 0
2022-09-24 59092 112 169 0
2022-09-23 58980 74 169 0
2022-09-22 58906 114 169 0
2022-09-21 58792 176 169 0
2022-09-20 58616 173 169 0
2022-09-19 58443 63 169 0
2022-09-18 58380 88 169 0
2022-09-17 58292 71 169 0
2022-09-16 58221 109 169 0
2022-09-15 58112 175 169 2
2022-09-14 57937 211 167 1
2022-09-13 57726 327 166 0
2022-09-12 57399 150 166 0
2022-09-11 57249 116 166 0
2022-09-10 57133 212 166 0
2022-09-09 56921 126 166 0
2022-09-08 56795 234 166 0
2022-09-07 56561 129 166 0
2022-09-06 56432 122 166 0
2022-09-05 56310 42 166 0
2022-09-04 56268 28 166 0
2022-09-03 56240 24 166 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: 0:03:00.020312