Germany: SK Straubing (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 11:58:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Straubing", weeks=5);
2023-03-07T11:58:56.048549 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 58.5 SK Straubing, 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 60 80 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 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 Straubing cases daily growth factor Germany-SK Straubing cases daily growth factor (rolling mean) Germany-SK Straubing estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-SK Straubing deaths daily growth factor Germany-SK Straubing deaths daily growth factor (rolling mean) Germany-SK Straubing estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-SK Straubing doubling time cases (rolling mean) 0.00 9.57 19.14 28.71 38.28 daily change Germany-SK Straubing new cases (rolling 7d mean) Germany-SK Straubing new cases 0.000 0.239 0.479 0.718 0.957 daily change Germany-SK Straubing new deaths (rolling 7d mean) Germany-SK Straubing new deaths 0.000 0.324 0.649 0.973
In [4]:
overview(country="Germany", subregion="SK Straubing");
2023-03-07T11:59:15.309386 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 3000 3000 7-day incidence rate (per 100K people) 58.5 SK Straubing, 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 5 10 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 Straubing cases daily growth factor Germany-SK Straubing cases daily growth factor (rolling mean) Germany-SK Straubing estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-SK Straubing deaths daily growth factor Germany-SK Straubing deaths daily growth factor (rolling mean) Germany-SK Straubing 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 Straubing doubling time cases (rolling mean) Germany-SK Straubing doubling time deaths (rolling mean) 0.0 95.7 191.4 287.1 daily change Germany-SK Straubing new cases (rolling 7d mean) Germany-SK Straubing new cases 0.000 2.393 4.785 daily change Germany-SK Straubing new deaths (rolling 7d mean) Germany-SK Straubing new deaths 0.0 27.2 54.5 81.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Straubing", dates="2020-03-15:");
2023-03-07T12:01:53.130532 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 Straubing SK Straubing 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 Straubing Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Straubing")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Straubing")
print(f'Population of country="Germany", subregion="SK Straubing": {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 Straubing": 47854 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 22613 6 138 0
2023-03-03 22607 1 138 0
2023-03-02 22606 10 138 0
2023-03-01 22596 5 138 0
2023-02-28 22591 6 138 0
2023-02-27 22585 18 138 0
2023-02-24 22567 7 138 0
2023-02-23 22560 11 138 0
2023-02-22 22549 40 138 0
2023-02-20 22509 23 138 0
2023-02-17 22486 11 138 0
2023-02-16 22475 13 138 0
2023-02-15 22462 13 138 1
2023-02-14 22449 16 137 0
2023-02-13 22433 21 137 0
2023-02-10 22412 10 137 0
2023-02-09 22402 25 137 0
2023-02-08 22377 19 137 0
2023-02-07 22358 23 137 0
2023-02-06 22335 34 137 1
2023-02-03 22301 16 136 0
2023-02-02 22285 22 136 0
2023-02-01 22263 22 136 0
2023-01-31 22241 21 136 0
2023-01-30 22220 29 136 1
2023-01-27 22191 9 135 0
2023-01-26 22182 14 135 0
2023-01-25 22168 19 135 0
2023-01-24 22149 16 135 0
2023-01-23 22133 21 135 0
2023-01-20 22112 5 135 0
2023-01-19 22107 5 135 0
2023-01-18 22102 4 135 0
2023-01-17 22098 10 135 0
2023-01-16 22088 8 135 1
2023-01-13 22080 7 134 0
2023-01-12 22073 11 134 0
2023-01-11 22062 5 134 0
2023-01-10 22057 3 134 0
2023-01-09 22054 20 134 0
2023-01-05 22034 8 134 0
2023-01-04 22026 7 134 0
2023-01-03 22019 7 134 0
2023-01-02 22012 25 134 1
2022-12-29 21987 14 133 0
2022-12-28 21973 16 133 0
2022-12-27 21957 24 133 0
2022-12-22 21933 9 133 0
2022-12-21 21924 8 133 0
2022-12-20 21916 16 133 0
2022-12-19 21900 22 133 0
2022-12-16 21878 15 133 0
2022-12-15 21863 10 133 0
2022-12-14 21853 20 133 0
2022-12-13 21833 30 133 0
2022-12-12 21803 23 133 0
2022-12-09 21780 13 133 0
2022-12-08 21767 13 133 0
2022-12-07 21754 8 133 0
2022-12-06 21746 18 133 0
2022-12-05 21728 11 133 0
2022-12-02 21717 8 133 0
2022-12-01 21709 10 133 0
2022-11-30 21699 8 133 0
2022-11-29 21691 11 133 0
2022-11-28 21680 11 133 0
2022-11-25 21669 6 133 0
2022-11-24 21663 8 133 0
2022-11-23 21655 5 133 0
2022-11-22 21650 14 133 0
2022-11-21 21636 19 133 0
2022-11-18 21617 1 133 0
2022-11-17 21616 8 133 0
2022-11-16 21608 8 133 0
2022-11-15 21600 14 133 0
2022-11-14 21586 19 133 0
2022-11-11 21567 11 133 0
2022-11-10 21556 15 133 0
2022-11-09 21541 15 133 0
2022-11-08 21526 21 133 0
2022-11-07 21505 27 133 0
2022-11-04 21478 16 133 0
2022-11-03 21462 5 133 0
2022-11-02 21457 24 133 0
2022-10-31 21433 19 133 0
2022-10-28 21414 22 133 0
2022-10-27 21392 19 133 0
2022-10-26 21373 27 133 0
2022-10-25 21346 39 133 0
2022-10-24 21307 33 133 0
2022-10-21 21274 13 133 0
2022-10-20 21261 28 133 0
2022-10-19 21233 34 133 0
2022-10-18 21199 62 133 0
2022-10-17 21137 68 133 0
2022-10-14 21069 30 133 0
2022-10-13 21039 61 133 0
2022-10-12 20978 44 133 0
2022-10-11 20934 70 133 0
2022-10-10 20864 65 133 0
2022-10-07 20799 43 133 0
2022-10-06 20756 50 133 0
2022-10-05 20706 72 133 0
2022-10-04 20634 76 133 0
2022-09-30 20558 32 133 0
2022-09-29 20526 34 133 0
2022-09-28 20492 39 133 0
2022-09-27 20453 48 133 0
2022-09-26 20405 40 133 0
2022-09-23 20365 16 133 0
2022-09-22 20349 21 133 0
2022-09-21 20328 29 133 0
2022-09-20 20299 36 133 0
2022-09-19 20263 23 133 1
2022-09-16 20240 7 132 0
2022-09-15 20233 12 132 0
2022-09-14 20221 16 132 0
2022-09-13 20205 20 132 0
2022-09-12 20185 14 132 0
2022-09-09 20171 15 132 0
2022-09-08 20156 23 132 0
2022-09-07 20133 28 132 0
2022-09-06 20105 37 132 0
2022-09-05 20068 27 132 0
2022-09-02 20041 24 132 0
2022-09-01 20017 27 132 0
2022-08-31 19990 51 132 0
2022-08-30 19939 70 132 0
2022-08-29 19869 146 132 0
2022-08-26 19723 42 132 0
2022-08-25 19681 93 132 0
2022-08-24 19588 72 132 0
2022-08-23 19516 81 132 0
2022-08-22 19435 63 132 0
2022-08-19 19372 29 132 0
2022-08-18 19343 39 132 0
2022-08-17 19304 32 132 0
2022-08-16 19272 37 132 0
2022-08-12 19235 16 132 0
2022-08-11 19219 15 132 0
2022-08-10 19204 19 132 0
2022-08-09 19185 21 132 0
2022-08-08 19164 31 132 0
2022-08-05 19133 20 132 0
2022-08-04 19113 20 132 0
2022-08-03 19093 40 132 0
2022-08-02 19053 33 132 0
2022-08-01 19020 45 132 1
2022-07-29 18975 36 131 0
2022-07-28 18939 43 131 0
2022-07-27 18896 69 131 0
2022-07-26 18827 68 131 0
2022-07-25 18759 90 131 0
2022-07-22 18669 43 131 0
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2020-12-29 1259 15 65 0
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2020-11-20 797 12 51 0
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2020-04-25 356 1 45 0
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2020-04-22 337 30 42 3
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2020-04-15 271 36 32 6
2020-04-14 235 1 26 0
2020-04-13 234 6 26 2
2020-04-11 228 6 24 3
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2020-03-31 101 16 10 0
2020-03-30 85 7 10 1
2020-03-29 78 9 9 1
2020-03-28 69 1 8 1
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2020-03-26 58 9 4 1
2020-03-25 49 6 3 2
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2020-03-21 38 11 1 1
2020-03-20 27 7 0 0
2020-03-19 20 7 0 0
2020-03-18 13 5 0 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: 0:03:18.579817