Germany: SK Osnabrück (Niedersachsen)¶

  • 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 13:45:04 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Osnabrück", weeks=5);
2023-03-07T13:45:14.733529 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 200 200 7-day incidence rate (per 100K people) 100.0 SK Osnabrück, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 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-SK Osnabrück cases daily growth factor Germany-SK Osnabrück cases daily growth factor (rolling mean) Germany-SK Osnabrück estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Osnabrück deaths daily growth factor Germany-SK Osnabrück deaths daily growth factor (rolling mean) Germany-SK Osnabrück 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-SK Osnabrück doubling time cases (rolling mean) 0.00 33.01 66.01 daily change Germany-SK Osnabrück new cases (rolling 7d mean) Germany-SK Osnabrück new cases 0.00 0.33 0.66 0.99 daily change Germany-SK Osnabrück new deaths (rolling 7d mean) Germany-SK Osnabrück new deaths 0.000 0.245 0.490 0.735 0.981
In [4]:
overview(country="Germany", subregion="SK Osnabrück");
2023-03-07T13:45:29.785760 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) 100.0 SK Osnabrück, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Osnabrück cases daily growth factor Germany-SK Osnabrück cases daily growth factor (rolling mean) Germany-SK Osnabrück estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Osnabrück deaths daily growth factor Germany-SK Osnabrück deaths daily growth factor (rolling mean) Germany-SK Osnabrück 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 cases doubling time [days] Germany-SK Osnabrück doubling time cases (rolling mean) Germany-SK Osnabrück doubling time deaths (rolling mean) 0 330 660 990 daily change Germany-SK Osnabrück new cases (rolling 7d mean) Germany-SK Osnabrück new cases 0.000 1.650 3.301 4.951 6.601 daily change Germany-SK Osnabrück new deaths (rolling 7d mean) Germany-SK Osnabrück new deaths 0.0 48.9 97.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Osnabrück", dates="2020-03-15:");
2023-03-07T13:48:04.583386 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 Osnabrück SK Osnabrück 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 Osnabrück Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Osnabrück")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Osnabrück")
print(f'Population of country="Germany", subregion="SK Osnabrück": {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 Osnabrück": 165034 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 81372 10 203 0
2023-03-05 81362 4 203 0
2023-03-04 81358 2 203 0
2023-03-03 81356 15 203 0
2023-03-02 81341 16 203 0
2023-03-01 81325 28 203 0
2023-02-28 81297 90 203 0
2023-02-27 81207 74 203 0
2023-02-26 81133 2 203 0
2023-02-25 81131 11 203 0
2023-02-24 81120 41 203 0
2023-02-23 81079 45 203 0
2023-02-22 81034 54 203 1
2023-02-21 80980 55 202 0
2023-02-20 80925 45 202 0
2023-02-19 80880 2 202 0
2023-02-18 80878 5 202 0
2023-02-17 80873 39 202 0
2023-02-16 80834 20 202 0
2023-02-15 80814 82 202 0
2023-02-14 80732 54 202 0
2023-02-13 80678 40 202 0
2023-02-12 80638 3 202 0
2023-02-11 80635 11 202 0
2023-02-10 80624 19 202 0
2023-02-09 80605 29 202 0
2023-02-08 80576 39 202 0
2023-02-07 80537 82 202 0
2023-02-06 80455 32 202 0
2023-02-05 80423 15 202 1
2023-02-04 80408 5 201 0
2023-02-03 80403 45 201 0
2023-02-02 80358 50 201 0
2023-02-01 80308 37 201 0
2023-01-31 80271 85 201 0
2023-01-30 80186 86 201 0
2023-01-29 80100 24 201 0
2023-01-28 80076 14 201 0
2023-01-27 80062 38 201 0
2023-01-26 80024 49 201 0
2023-01-25 79975 37 201 0
2023-01-24 79938 47 201 0
2023-01-23 79891 70 201 0
2023-01-22 79821 11 201 0
2023-01-21 79810 12 201 0
2023-01-20 79798 42 201 0
2023-01-19 79756 21 201 1
2023-01-18 79735 30 200 0
2023-01-17 79705 53 200 0
2023-01-16 79652 45 200 0
2023-01-15 79607 5 200 0
2023-01-14 79602 9 200 0
2023-01-13 79593 36 200 0
2023-01-12 79557 37 200 0
2023-01-11 79520 52 200 0
2023-01-10 79468 127 200 1
2023-01-09 79341 55 199 0
2023-01-08 79286 6 199 0
2023-01-07 79280 15 199 0
2023-01-06 79265 62 199 0
2023-01-05 79203 67 199 1
2023-01-04 79136 82 198 0
2023-01-03 79054 132 198 0
2023-01-02 78922 110 198 0
2023-01-01 78812 15 198 0
2022-12-31 78797 20 198 0
2022-12-30 78777 94 198 0
2022-12-29 78683 89 198 1
2022-12-28 78594 143 197 1
2022-12-27 78451 130 196 1
2022-12-26 78321 17 195 0
2022-12-25 78304 18 195 0
2022-12-24 78286 26 195 0
2022-12-23 78260 107 195 0
2022-12-22 78153 182 195 0
2022-12-21 77971 128 195 1
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2022-12-10 76652 29 193 0
2022-12-09 76623 200 193 1
2022-12-08 76423 120 192 0
2022-12-07 76303 131 192 0
2022-12-06 76172 164 192 0
2022-12-05 76008 87 192 1
2022-12-04 75921 52 191 0
2022-12-03 75869 27 191 1
2022-12-02 75842 91 190 0
2022-12-01 75751 148 190 0
2022-11-30 75603 173 190 1
2022-11-29 75430 248 189 1
2022-11-28 75182 146 188 1
2022-11-27 75036 6 187 0
2022-11-26 75030 28 187 0
2022-11-25 75002 119 187 0
2022-11-24 74883 100 187 0
2022-11-23 74783 66 187 0
2022-11-22 74717 90 187 0
2022-11-21 74627 90 187 0
2022-11-20 74537 30 187 0
2022-11-19 74507 7 187 0
2022-11-18 74500 72 187 0
2022-11-17 74428 74 187 0
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2022-11-05 73305 20 187 0
2022-11-04 73285 283 187 0
2022-11-03 73002 203 187 0
2022-11-02 72799 92 187 0
2022-11-01 72707 122 187 0
2022-10-31 72585 23 187 0
2022-10-30 72562 47 187 0
2022-10-29 72515 32 187 0
2022-10-28 72483 117 187 1
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2022-10-25 71983 245 186 2
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2022-10-14 69980 258 181 0
2022-10-13 69722 229 181 0
2022-10-12 69493 220 181 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: 0:04:17.149976