Germany: LK Oldenburg (Niedersachsen)¶

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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 13:22:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Oldenburg", weeks=5);
2023-03-07T13:23:05.634717 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) 71.9 LK Oldenburg, 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 Oldenburg new deaths (rolling 7d mean) Germany-LK Oldenburg 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-LK Oldenburg cases daily growth factor Germany-LK Oldenburg cases daily growth factor (rolling mean) Germany-LK Oldenburg 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 Oldenburg deaths daily growth factor Germany-LK Oldenburg deaths daily growth factor (rolling mean) Germany-LK Oldenburg 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 Oldenburg doubling time cases (rolling mean) 0.00 13.21 26.42 39.63 52.84 daily change Germany-LK Oldenburg new cases (rolling 7d mean) Germany-LK Oldenburg new cases 0.000 0.342 0.684
In [4]:
overview(country="Germany", subregion="LK Oldenburg");
2023-03-07T13:23:24.927693 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) 71.9 LK Oldenburg, 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Oldenburg cases daily growth factor Germany-LK Oldenburg cases daily growth factor (rolling mean) Germany-LK Oldenburg 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 Oldenburg deaths daily growth factor Germany-LK Oldenburg deaths daily growth factor (rolling mean) Germany-LK Oldenburg 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 8000 cases doubling time [days] Germany-LK Oldenburg doubling time cases (rolling mean) Germany-LK Oldenburg doubling time deaths (rolling mean) 0 264 528 793 daily change Germany-LK Oldenburg new cases (rolling 7d mean) Germany-LK Oldenburg new cases 0.00 2.64 5.28 7.93 10.57 daily change Germany-LK Oldenburg new deaths (rolling 7d mean) Germany-LK Oldenburg new deaths 0.0 35.9 71.8 107.7 143.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Oldenburg", dates="2020-03-15:");
2023-03-07T13:25:45.558773 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 Oldenburg LK Oldenburg 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 Oldenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Oldenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Oldenburg")
print(f'Population of country="Germany", subregion="LK Oldenburg": {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 Oldenburg": 132091 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 68243 21 190 0
2023-03-03 68222 13 190 0
2023-03-02 68209 6 190 0
2023-03-01 68203 23 190 0
2023-02-28 68180 32 190 0
2023-02-27 68148 46 190 0
2023-02-24 68102 19 190 0
2023-02-23 68083 34 190 0
2023-02-22 68049 34 190 0
2023-02-21 68015 43 190 0
2023-02-20 67972 55 190 0
2023-02-17 67917 23 190 0
2023-02-16 67894 34 190 0
2023-02-15 67860 26 190 0
2023-02-14 67834 51 190 0
2023-02-13 67783 39 190 0
2023-02-12 67744 4 190 0
2023-02-11 67740 1 190 0
2023-02-10 67739 18 190 0
2023-02-09 67721 24 190 0
2023-02-08 67697 26 190 0
2023-02-07 67671 27 190 0
2023-02-06 67644 29 190 0
2023-02-05 67615 1 190 0
2023-02-03 67614 18 190 0
2023-02-02 67596 26 190 0
2023-02-01 67570 40 190 0
2023-01-31 67530 42 190 0
2023-01-30 67488 54 190 0
2023-01-27 67434 23 190 0
2023-01-26 67411 28 190 0
2023-01-25 67383 30 190 0
2023-01-24 67353 55 190 0
2023-01-23 67298 39 190 0
2023-01-22 67259 2 190 0
2023-01-20 67257 32 190 0
2023-01-19 67225 20 190 0
2023-01-18 67205 28 190 0
2023-01-17 67177 32 190 0
2023-01-16 67145 41 190 0
2023-01-13 67104 23 190 0
2023-01-12 67081 35 190 0
2023-01-11 67046 40 190 0
2023-01-10 67006 41 190 0
2023-01-09 66965 68 190 0
2023-01-06 66897 31 190 0
2023-01-05 66866 57 190 0
2023-01-04 66809 59 190 0
2023-01-03 66750 89 190 0
2023-01-02 66661 104 190 0
2023-01-01 66557 1 190 0
2022-12-30 66556 71 190 0
2022-12-29 66485 69 190 0
2022-12-28 66416 120 190 0
2022-12-27 66296 179 190 0
2022-12-26 66117 3 190 0
2022-12-23 66114 76 190 0
2022-12-22 66038 109 190 0
2022-12-21 65929 128 190 0
2022-12-20 65801 110 190 0
2022-12-19 65691 196 190 1
2022-12-17 65495 5 189 0
2022-12-16 65490 110 189 1
2022-12-15 65380 131 188 0
2022-12-14 65249 140 188 1
2022-12-13 65109 174 187 0
2022-12-12 64935 230 187 0
2022-12-09 64705 86 187 0
2022-12-08 64619 111 187 0
2022-12-07 64508 108 187 0
2022-12-06 64400 168 187 0
2022-12-05 64232 173 187 1
2022-12-02 64059 87 186 0
2022-12-01 63972 86 186 1
2022-11-30 63886 82 185 1
2022-11-29 63804 116 184 0
2022-11-28 63688 154 184 0
2022-11-25 63534 71 184 0
2022-11-24 63463 75 184 0
2022-11-23 63388 78 184 1
2022-11-22 63310 102 183 0
2022-11-21 63208 112 183 0
2022-11-18 63096 71 183 0
2022-11-17 63025 77 183 0
2022-11-16 62948 82 183 0
2022-11-15 62866 137 183 0
2022-11-14 62729 143 183 0
2022-11-11 62586 70 183 0
2022-11-10 62516 172 183 0
2022-11-09 62344 98 183 0
2022-11-08 62246 119 183 1
2022-11-07 62127 148 182 0
2022-11-06 61979 1 182 0
2022-11-04 61978 82 182 0
2022-11-03 61896 117 182 0
2022-11-02 61779 164 182 0
2022-11-01 61615 261 182 0
2022-10-30 61354 1 182 0
2022-10-29 61353 1 182 0
2022-10-28 61352 93 182 0
2022-10-27 61259 113 182 0
2022-10-26 61146 136 182 0
2022-10-25 61010 179 182 0
2022-10-24 60831 287 182 0
2022-10-21 60544 149 182 1
2022-10-20 60395 205 181 0
2022-10-19 60190 197 181 0
2022-10-18 59993 310 181 0
2022-10-17 59683 386 181 1
2022-10-15 59297 2 180 0
2022-10-14 59295 170 180 0
2022-10-13 59125 204 180 1
2022-10-12 58921 225 179 0
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2022-10-09 58031 1 179 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: 0:04:03.646770