Germany: LK Bayreuth (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 10:33:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bayreuth", weeks=5);
2023-03-07T10:33:13.548487 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) 68.5 LK Bayreuth, 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 0.0 0.5 1.0 1.5 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-LK Bayreuth cases daily growth factor Germany-LK Bayreuth cases daily growth factor (rolling mean) Germany-LK Bayreuth 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-LK Bayreuth deaths daily growth factor Germany-LK Bayreuth deaths daily growth factor (rolling mean) Germany-LK Bayreuth 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 Bayreuth doubling time cases (rolling mean) 0.00 10.36 20.73 31.09 41.46 daily change Germany-LK Bayreuth new cases (rolling 7d mean) Germany-LK Bayreuth new cases 0.000 0.518 1.036 1.555 daily change Germany-LK Bayreuth new deaths (rolling 7d mean) Germany-LK Bayreuth new deaths 0.000 0.335 0.670
In [4]:
overview(country="Germany", subregion="LK Bayreuth");
2023-03-07T10:33:31.239220 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) 68.5 LK Bayreuth, 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 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 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-LK Bayreuth cases daily growth factor Germany-LK Bayreuth cases daily growth factor (rolling mean) Germany-LK Bayreuth 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-LK Bayreuth deaths daily growth factor Germany-LK Bayreuth deaths daily growth factor (rolling mean) Germany-LK Bayreuth 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 Bayreuth doubling time cases (rolling mean) Germany-LK Bayreuth doubling time deaths (rolling mean) 0.0 207.3 414.6 daily change Germany-LK Bayreuth new cases (rolling 7d mean) Germany-LK Bayreuth new cases 0.000 2.073 4.146 6.219 daily change Germany-LK Bayreuth new deaths (rolling 7d mean) Germany-LK Bayreuth new deaths 0.0 65.8 131.6 197.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bayreuth", dates="2020-03-15:");
2023-03-07T10:36:13.010178 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 Bayreuth LK Bayreuth 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Bayreuth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bayreuth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bayreuth")
print(f'Population of country="Germany", subregion="LK Bayreuth": {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 Bayreuth": 103648 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 55045 5 268 0
2023-03-05 55040 6 268 0
2023-03-04 55034 3 268 0
2023-03-03 55031 8 268 0
2023-03-02 55023 6 268 0
2023-03-01 55017 22 268 1
2023-02-28 54995 21 267 0
2023-02-27 54974 47 267 0
2023-02-26 54927 5 267 0
2023-02-25 54922 13 267 0
2023-02-24 54909 17 267 0
2023-02-23 54892 34 267 0
2023-02-22 54858 33 267 0
2023-02-21 54825 34 267 0
2023-02-20 54791 23 267 0
2023-02-19 54768 9 267 0
2023-02-18 54759 11 267 0
2023-02-17 54748 29 267 0
2023-02-16 54719 36 267 0
2023-02-15 54683 32 267 0
2023-02-14 54651 48 267 0
2023-02-13 54603 28 267 0
2023-02-12 54575 6 267 0
2023-02-11 54569 16 267 0
2023-02-10 54553 20 267 0
2023-02-09 54533 27 267 0
2023-02-08 54506 18 267 1
2023-02-07 54488 36 266 0
2023-02-06 54452 30 266 0
2023-02-05 54422 4 266 0
2023-02-04 54418 12 266 0
2023-02-03 54406 31 266 0
2023-02-02 54375 23 266 1
2023-02-01 54352 20 265 2
2023-01-31 54332 24 263 0
2023-01-30 54308 22 263 0
2023-01-29 54286 12 263 0
2023-01-28 54274 3 263 0
2023-01-27 54271 21 263 0
2023-01-26 54250 17 263 0
2023-01-25 54233 12 263 0
2023-01-24 54221 11 263 0
2023-01-23 54210 26 263 0
2023-01-22 54184 7 263 0
2023-01-21 54177 12 263 0
2023-01-20 54165 13 263 0
2023-01-19 54152 16 263 0
2023-01-18 54136 10 263 0
2023-01-17 54126 19 263 0
2023-01-16 54107 17 263 0
2023-01-14 54090 5 263 0
2023-01-13 54085 9 263 0
2023-01-12 54076 13 263 0
2023-01-11 54063 13 263 0
2023-01-10 54050 10 263 0
2023-01-09 54040 21 263 0
2023-01-08 54019 4 263 0
2023-01-07 54015 6 263 0
2023-01-06 54009 9 263 0
2023-01-05 54000 17 263 0
2023-01-04 53983 17 263 0
2023-01-03 53966 27 263 0
2023-01-02 53939 27 263 0
2023-01-01 53912 7 263 0
2022-12-31 53905 11 263 0
2022-12-30 53894 17 263 0
2022-12-29 53877 28 263 0
2022-12-28 53849 29 263 0
2022-12-27 53820 39 263 0
2022-12-26 53781 9 263 2
2022-12-25 53772 10 261 0
2022-12-24 53762 16 261 0
2022-12-23 53746 20 261 0
2022-12-22 53726 21 261 0
2022-12-21 53705 23 261 0
2022-12-20 53682 32 261 0
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2022-12-15 53572 22 261 1
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2022-12-03 53268 13 260 0
2022-12-02 53255 18 260 2
2022-12-01 53237 25 258 2
2022-11-30 53212 34 256 0
2022-11-29 53178 29 256 0
2022-11-28 53149 21 256 0
2022-11-27 53128 12 256 0
2022-11-26 53116 9 256 0
2022-11-25 53107 23 256 0
2022-11-24 53084 22 256 0
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2022-11-22 53033 26 256 0
2022-11-21 53007 31 256 0
2022-11-20 52976 11 256 0
2022-11-19 52965 9 256 0
2022-11-18 52956 16 256 0
2022-11-17 52940 31 256 0
2022-11-16 52909 32 256 1
2022-11-15 52877 28 255 0
2022-11-14 52849 44 255 0
2022-11-13 52805 10 255 1
2022-11-12 52795 21 254 0
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2022-11-02 52437 55 252 0
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2022-10-31 52335 32 252 1
2022-10-30 52303 17 251 0
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2022-10-28 52253 51 251 0
2022-10-27 52202 72 251 0
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2022-10-25 52062 79 251 0
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2022-10-23 51894 25 251 0
2022-10-22 51869 62 251 0
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2022-10-20 51720 92 251 0
2022-10-19 51628 118 251 1
2022-10-18 51510 179 250 0
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2022-10-16 51219 27 250 1
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2021-01-02 2055 1 78 0
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2020-12-31 2013 96 78 8
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2020-12-26 1760 3 51 0
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2020-12-24 1726 76 51 2
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2020-12-22 1599 58 46 2
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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: 0:03:40.818463