Germany: SK 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 11:43:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Bayreuth", weeks=5);
2023-03-07T11:43:57.651761 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 7-day incidence rate (per 100K people) 56.8 SK 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 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Bayreuth cases daily growth factor Germany-SK Bayreuth cases daily growth factor (rolling mean) Germany-SK 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-SK Bayreuth deaths daily growth factor Germany-SK Bayreuth deaths daily growth factor (rolling mean) Germany-SK Bayreuth 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 Bayreuth doubling time cases (rolling mean) 0.00 14.78 29.56 daily change Germany-SK Bayreuth new cases (rolling 7d mean) Germany-SK Bayreuth new cases 0.000 0.370 0.739 daily change Germany-SK Bayreuth new deaths (rolling 7d mean) Germany-SK Bayreuth new deaths 0.000 0.291 0.582 0.873
In [4]:
overview(country="Germany", subregion="SK Bayreuth");
2023-03-07T11:44:14.209795 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) 56.8 SK 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 0 200 400 600 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 15 20 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Bayreuth cases daily growth factor Germany-SK Bayreuth cases daily growth factor (rolling mean) Germany-SK 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-SK Bayreuth deaths daily growth factor Germany-SK Bayreuth deaths daily growth factor (rolling mean) Germany-SK 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-SK Bayreuth doubling time cases (rolling mean) Germany-SK Bayreuth doubling time deaths (rolling mean) 0.0 147.8 295.6 443.5 daily change Germany-SK Bayreuth new cases (rolling 7d mean) Germany-SK Bayreuth new cases 0.00 3.70 7.39 11.09 14.78 daily change Germany-SK Bayreuth new deaths (rolling 7d mean) Germany-SK Bayreuth new deaths 0.0 40.1 80.2 120.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Bayreuth", dates="2020-03-15:");
2023-03-07T11:46:22.760112 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 Bayreuth SK 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 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people SK Bayreuth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Bayreuth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Bayreuth")
print(f'Population of country="Germany", subregion="SK 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="SK Bayreuth": 73909 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 39109 2 200 0
2023-03-05 39107 4 200 0
2023-03-04 39103 2 200 0
2023-03-03 39101 5 200 0
2023-03-02 39096 12 200 0
2023-03-01 39084 4 200 0
2023-02-28 39080 13 200 0
2023-02-27 39067 30 200 0
2023-02-26 39037 9 200 0
2023-02-25 39028 11 200 0
2023-02-24 39017 24 200 0
2023-02-23 38993 16 200 0
2023-02-22 38977 21 200 0
2023-02-21 38956 40 200 0
2023-02-20 38916 25 200 0
2023-02-19 38891 4 200 0
2023-02-18 38887 24 200 0
2023-02-17 38863 27 200 0
2023-02-16 38836 23 200 1
2023-02-15 38813 15 199 0
2023-02-14 38798 24 199 0
2023-02-13 38774 22 199 0
2023-02-12 38752 10 199 0
2023-02-11 38742 3 199 0
2023-02-10 38739 15 199 0
2023-02-09 38724 16 199 0
2023-02-08 38708 16 199 0
2023-02-07 38692 30 199 0
2023-02-06 38662 14 199 0
2023-02-05 38648 4 199 0
2023-02-04 38644 2 199 0
2023-02-03 38642 17 199 0
2023-02-02 38625 17 199 0
2023-02-01 38608 20 199 0
2023-01-31 38588 12 199 0
2023-01-30 38576 31 199 0
2023-01-29 38545 7 199 0
2023-01-28 38538 7 199 0
2023-01-27 38531 20 199 0
2023-01-26 38511 14 199 0
2023-01-25 38497 19 199 0
2023-01-24 38478 9 199 0
2023-01-23 38469 17 199 0
2023-01-22 38452 4 199 0
2023-01-21 38448 3 199 0
2023-01-20 38445 5 199 1
2023-01-19 38440 6 198 0
2023-01-18 38434 8 198 0
2023-01-17 38426 13 198 0
2023-01-16 38413 12 198 0
2023-01-15 38401 1 198 0
2023-01-14 38400 6 198 0
2023-01-13 38394 8 198 0
2023-01-12 38386 11 198 0
2023-01-11 38375 8 198 0
2023-01-10 38367 13 198 0
2023-01-09 38354 15 198 0
2023-01-08 38339 3 198 0
2023-01-07 38336 4 198 0
2023-01-06 38332 9 198 0
2023-01-05 38323 14 198 0
2023-01-04 38309 12 198 0
2023-01-03 38297 20 198 0
2023-01-02 38277 20 198 0
2023-01-01 38257 6 198 0
2022-12-31 38251 5 198 0
2022-12-30 38246 12 198 0
2022-12-29 38234 26 198 0
2022-12-28 38208 25 198 0
2022-12-27 38183 37 198 0
2022-12-26 38146 7 198 0
2022-12-25 38139 5 198 0
2022-12-24 38134 29 198 1
2022-12-23 38105 24 197 0
2022-12-22 38081 26 197 0
2022-12-21 38055 32 197 0
2022-12-20 38023 41 197 0
2022-12-19 37982 24 197 0
2022-12-18 37958 6 197 0
2022-12-17 37952 11 197 0
2022-12-16 37941 19 197 0
2022-12-15 37922 29 197 0
2022-12-14 37893 35 197 0
2022-12-13 37858 48 197 0
2022-12-12 37810 26 197 0
2022-12-11 37784 13 197 0
2022-12-10 37771 16 197 0
2022-12-09 37755 21 197 1
2022-12-08 37734 43 196 1
2022-12-07 37691 26 195 0
2022-12-06 37665 42 195 0
2022-12-05 37623 26 195 1
2022-12-04 37597 13 194 0
2022-12-03 37584 11 194 0
2022-12-02 37573 20 194 0
2022-12-01 37553 27 194 0
2022-11-30 37526 30 194 0
2022-11-29 37496 35 194 0
2022-11-28 37461 28 194 0
2022-11-27 37433 4 194 0
2022-11-26 37429 12 194 0
2022-11-25 37417 22 194 0
2022-11-24 37395 18 194 0
2022-11-23 37377 27 194 0
2022-11-22 37350 25 194 0
2022-11-21 37325 27 194 0
2022-11-20 37298 18 194 1
2022-11-19 37280 4 193 0
2022-11-18 37276 28 193 0
2022-11-17 37248 29 193 0
2022-11-16 37219 12 193 0
2022-11-15 37207 35 193 0
2022-11-14 37172 37 193 0
2022-11-13 37135 16 193 0
2022-11-12 37119 20 193 0
2022-11-11 37099 31 193 0
2022-11-10 37068 30 193 0
2022-11-09 37038 31 193 1
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2022-11-06 36923 6 192 0
2022-11-05 36917 27 192 0
2022-11-04 36890 35 192 0
2022-11-03 36855 38 192 0
2022-11-02 36817 32 192 0
2022-11-01 36785 48 192 0
2022-10-31 36737 29 192 0
2022-10-30 36708 13 192 0
2022-10-29 36695 35 192 0
2022-10-28 36660 36 192 0
2022-10-27 36624 47 192 0
2022-10-26 36577 59 192 0
2022-10-25 36518 50 192 0
2022-10-24 36468 87 192 0
2022-10-23 36381 23 192 0
2022-10-22 36358 28 192 0
2022-10-21 36330 65 192 1
2022-10-20 36265 71 191 1
2022-10-19 36194 81 190 0
2022-10-18 36113 121 190 0
2022-10-17 35992 115 190 1
2022-10-16 35877 43 189 2
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2022-10-11 35414 168 186 0
2022-10-10 35246 139 186 0
2022-10-09 35107 39 186 0
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2021-01-04 1379 7 40 1
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2020-12-27 1204 5 34 0
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2020-03-26 34 15 3 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: 0:03:02.442335