Germany: SK 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 13:39:04 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Oldenburg", weeks=5);
2023-03-07T13:39:15.835570 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 7-day incidence rate (per 100K people) 59.9 SK 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 20 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-SK Oldenburg new deaths (rolling 7d mean) Germany-SK Oldenburg new deaths 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 R & growth factor (based on cases) Germany-SK Oldenburg cases daily growth factor Germany-SK Oldenburg cases daily growth factor (rolling mean) Germany-SK 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-SK Oldenburg deaths daily growth factor Germany-SK Oldenburg deaths daily growth factor (rolling mean) Germany-SK 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-SK Oldenburg doubling time cases (rolling mean) 0.0 34.1 68.2 daily change Germany-SK Oldenburg new cases (rolling 7d mean) Germany-SK Oldenburg new cases 0.000 0.353 0.707
In [4]:
overview(country="Germany", subregion="SK Oldenburg");
2023-03-07T13:39:31.881758 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) 59.9 SK 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 0 100 200 300 400 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Oldenburg cases daily growth factor Germany-SK Oldenburg cases daily growth factor (rolling mean) Germany-SK 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-SK Oldenburg deaths daily growth factor Germany-SK Oldenburg deaths daily growth factor (rolling mean) Germany-SK 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 cases doubling time [days] Germany-SK Oldenburg doubling time cases (rolling mean) Germany-SK Oldenburg doubling time deaths (rolling mean) 0.0 170.4 340.8 511.2 681.6 daily change Germany-SK Oldenburg new cases (rolling 7d mean) Germany-SK Oldenburg new cases 0.000 1.704 3.408 5.112 6.816 daily change Germany-SK Oldenburg new deaths (rolling 7d mean) Germany-SK Oldenburg new deaths 0.00 5.55 11.10 16.65 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Oldenburg", dates="2020-03-15:");
2023-03-07T13:41:24.998058 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 Oldenburg SK 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 SK Oldenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Oldenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Oldenburg")
print(f'Population of country="Germany", subregion="SK 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="SK Oldenburg": 170389 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 79228 4 72 0
2023-03-05 79224 3 72 0
2023-03-04 79221 4 72 0
2023-03-03 79217 17 72 0
2023-03-02 79200 18 72 0
2023-03-01 79182 33 72 0
2023-02-28 79149 23 72 0
2023-02-27 79126 39 72 0
2023-02-26 79087 9 72 0
2023-02-25 79078 14 72 0
2023-02-24 79064 52 72 0
2023-02-23 79012 26 72 0
2023-02-22 78986 42 72 0
2023-02-21 78944 87 72 0
2023-02-20 78857 19 72 0
2023-02-19 78838 8 72 0
2023-02-18 78830 10 72 0
2023-02-17 78820 36 72 0
2023-02-16 78784 24 72 0
2023-02-15 78760 53 72 0
2023-02-14 78707 32 72 0
2023-02-13 78675 30 72 0
2023-02-12 78645 6 72 0
2023-02-11 78639 14 72 0
2023-02-10 78625 27 72 0
2023-02-09 78598 26 72 0
2023-02-08 78572 22 72 0
2023-02-07 78550 38 72 0
2023-02-06 78512 22 72 0
2023-02-05 78490 5 72 0
2023-02-04 78485 16 72 0
2023-02-03 78469 31 72 0
2023-02-02 78438 45 72 0
2023-02-01 78393 46 72 0
2023-01-31 78347 78 72 0
2023-01-30 78269 60 72 0
2023-01-29 78209 10 72 0
2023-01-28 78199 18 72 0
2023-01-27 78181 44 72 0
2023-01-26 78137 68 72 0
2023-01-25 78069 42 72 0
2023-01-24 78027 46 72 0
2023-01-23 77981 43 72 0
2023-01-22 77938 8 72 0
2023-01-21 77930 24 72 0
2023-01-20 77906 27 72 0
2023-01-19 77879 38 72 0
2023-01-18 77841 17 72 0
2023-01-17 77824 57 72 0
2023-01-16 77767 37 72 0
2023-01-15 77730 5 72 0
2023-01-14 77725 27 72 0
2023-01-13 77698 9 72 0
2023-01-12 77689 37 72 0
2023-01-11 77652 48 72 0
2023-01-10 77604 136 72 0
2023-01-09 77468 38 72 0
2023-01-08 77430 10 72 0
2023-01-07 77420 26 72 0
2023-01-06 77394 36 72 0
2023-01-05 77358 55 72 0
2023-01-04 77303 75 72 0
2023-01-03 77228 91 72 0
2023-01-02 77137 67 72 0
2023-01-01 77070 6 72 0
2022-12-31 77064 39 72 0
2022-12-30 77025 111 72 0
2022-12-29 76914 87 72 0
2022-12-28 76827 121 72 0
2022-12-27 76706 47 72 0
2022-12-26 76659 23 72 0
2022-12-25 76636 16 72 0
2022-12-24 76620 61 72 0
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2022-12-07 74582 153 72 0
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2022-12-05 74324 127 72 0
2022-12-04 74197 13 72 1
2022-12-03 74184 69 71 0
2022-12-02 74115 83 71 0
2022-12-01 74032 136 71 0
2022-11-30 73896 164 71 0
2022-11-29 73732 143 71 2
2022-11-28 73589 95 69 0
2022-11-27 73494 12 69 0
2022-11-26 73482 53 69 0
2022-11-25 73429 74 69 0
2022-11-24 73355 107 69 1
2022-11-23 73248 68 68 0
2022-11-22 73180 134 68 0
2022-11-21 73046 96 68 0
2022-11-20 72950 20 68 0
2022-11-19 72930 43 68 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: 0:02:31.297393