Germany: SK Speyer (Rheinland-Pfalz)¶

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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 15:11:38 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Speyer", weeks=5);
2023-03-07T15:11:47.465892 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 7-day incidence rate (per 100K people) 77.1 SK Speyer, 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 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 Speyer new deaths (rolling 7d mean) Germany-SK Speyer new deaths 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 Speyer cases daily growth factor Germany-SK Speyer cases daily growth factor (rolling mean) Germany-SK Speyer 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 Speyer deaths daily growth factor Germany-SK Speyer deaths daily growth factor (rolling mean) Germany-SK Speyer 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 Speyer doubling time cases (rolling mean) 0.00 5.06 10.11 15.17 daily change Germany-SK Speyer new cases (rolling 7d mean) Germany-SK Speyer new cases 0.000 0.228 0.455 0.683 0.910
In [4]:
overview(country="Germany", subregion="SK Speyer");
2023-03-07T15:12:05.697821 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 7-day incidence rate (per 100K people) 77.1 SK Speyer, 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 10 20 30 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 Speyer cases daily growth factor Germany-SK Speyer cases daily growth factor (rolling mean) Germany-SK Speyer 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 Speyer deaths daily growth factor Germany-SK Speyer deaths daily growth factor (rolling mean) Germany-SK Speyer 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 cases doubling time [days] Germany-SK Speyer doubling time cases (rolling mean) Germany-SK Speyer doubling time deaths (rolling mean) 0.0 50.6 101.1 151.7 202.3 daily change Germany-SK Speyer new cases (rolling 7d mean) Germany-SK Speyer new cases 0.00 5.06 10.11 15.17 daily change Germany-SK Speyer new deaths (rolling 7d mean) Germany-SK Speyer new deaths 0.00 20.03 40.06 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Speyer", dates="2020-03-15:");
2023-03-07T15:14:19.322826 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 Speyer SK Speyer 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Speyer Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Speyer")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Speyer")
print(f'Population of country="Germany", subregion="SK Speyer": {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 Speyer": 50565 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 23343 4 133 0
2023-03-05 23339 1 133 0
2023-03-04 23338 1 133 0
2023-03-03 23337 5 133 0
2023-03-02 23332 10 133 0
2023-03-01 23322 10 133 0
2023-02-28 23312 8 133 0
2023-02-27 23304 7 133 0
2023-02-26 23297 2 133 0
2023-02-25 23295 6 133 0
2023-02-24 23289 13 133 0
2023-02-23 23276 7 133 0
2023-02-22 23269 12 133 0
2023-02-21 23257 5 133 0
2023-02-20 23252 4 133 0
2023-02-19 23248 2 133 0
2023-02-18 23246 7 133 0
2023-02-17 23239 12 133 0
2023-02-16 23227 10 133 0
2023-02-15 23217 7 133 0
2023-02-14 23210 10 133 0
2023-02-13 23200 17 133 0
2023-02-12 23183 3 133 0
2023-02-11 23180 2 133 0
2023-02-10 23178 9 133 0
2023-02-09 23169 8 133 0
2023-02-08 23161 6 133 0
2023-02-07 23155 10 133 0
2023-02-06 23145 7 133 0
2023-02-05 23138 1 133 0
2023-02-04 23137 3 133 0
2023-02-03 23134 10 133 0
2023-02-02 23124 6 133 0
2023-02-01 23118 6 133 0
2023-01-31 23112 6 133 0
2023-01-30 23106 4 133 0
2023-01-29 23102 4 133 0
2023-01-28 23098 2 133 0
2023-01-27 23096 10 133 0
2023-01-26 23086 1 133 0
2023-01-25 23085 7 133 0
2023-01-24 23078 13 133 0
2023-01-23 23065 4 133 0
2023-01-22 23061 3 133 1
2023-01-21 23058 3 132 0
2023-01-20 23055 7 132 0
2023-01-19 23048 7 132 0
2023-01-18 23041 8 132 0
2023-01-17 23033 7 132 0
2023-01-16 23026 14 132 0
2023-01-15 23012 3 132 0
2023-01-14 23009 8 132 0
2023-01-13 23001 4 132 0
2023-01-12 22997 10 132 0
2023-01-11 22987 6 132 0
2023-01-10 22981 13 132 0
2023-01-09 22968 25 132 0
2023-01-08 22943 1 132 1
2023-01-07 22942 2 131 0
2023-01-06 22940 14 131 0
2023-01-05 22926 15 131 0
2023-01-04 22911 12 131 0
2023-01-03 22899 16 131 1
2023-01-02 22883 7 130 0
2023-01-01 22876 9 130 0
2022-12-31 22867 19 130 0
2022-12-30 22848 17 130 0
2022-12-29 22831 10 130 0
2022-12-28 22821 31 130 0
2022-12-27 22790 10 130 0
2022-12-26 22780 9 130 0
2022-12-25 22771 4 130 0
2022-12-24 22767 16 130 0
2022-12-23 22751 37 130 0
2022-12-22 22714 28 130 0
2022-12-21 22686 18 130 0
2022-12-20 22668 28 130 0
2022-12-19 22640 6 130 0
2022-12-18 22634 9 130 0
2022-12-17 22625 25 130 0
2022-12-16 22600 10 130 0
2022-12-15 22590 21 130 0
2022-12-14 22569 29 130 0
2022-12-13 22540 26 130 0
2022-12-12 22514 13 130 0
2022-12-11 22501 5 130 0
2022-12-10 22496 11 130 0
2022-12-09 22485 11 130 0
2022-12-08 22474 15 130 0
2022-12-07 22459 23 130 0
2022-12-06 22436 24 130 0
2022-12-05 22412 13 130 0
2022-12-04 22399 3 130 0
2022-12-03 22396 10 130 0
2022-12-02 22386 11 130 0
2022-12-01 22375 14 130 0
2022-11-30 22361 20 130 0
2022-11-29 22341 13 130 0
2022-11-28 22328 30 130 0
2022-11-27 22298 3 130 0
2022-11-26 22295 4 130 0
2022-11-25 22291 11 130 0
2022-11-24 22280 27 130 0
2022-11-23 22253 17 130 0
2022-11-22 22236 16 130 0
2022-11-21 22220 16 130 0
2022-11-20 22204 3 130 0
2022-11-19 22201 12 130 0
2022-11-18 22189 15 130 0
2022-11-17 22174 16 130 0
2022-11-16 22158 14 130 0
2022-11-15 22144 20 130 0
2022-11-14 22124 25 130 0
2022-11-13 22099 6 130 0
2022-11-12 22093 4 130 0
2022-11-11 22089 20 130 0
2022-11-10 22069 25 130 0
2022-11-09 22044 23 130 0
2022-11-08 22021 26 130 0
2022-11-07 21995 26 130 0
2022-11-06 21969 4 130 0
2022-11-05 21965 11 130 0
2022-11-04 21954 14 130 0
2022-11-03 21940 27 130 0
2022-11-02 21913 35 130 0
2022-11-01 21878 22 130 0
2022-10-28 21856 2 130 0
2022-10-27 21854 27 130 0
2022-10-26 21827 52 130 0
2022-10-25 21775 53 130 0
2022-10-24 21722 41 130 0
2022-10-23 21681 16 130 0
2022-10-22 21665 15 130 0
2022-10-21 21650 47 130 0
2022-10-20 21603 41 130 0
2022-10-19 21562 59 130 0
2022-10-18 21503 88 130 0
2022-10-17 21415 80 130 0
2022-10-16 21335 29 130 0
2022-10-15 21306 40 130 0
2022-10-14 21266 43 130 0
2022-10-13 21223 44 130 0
2022-10-12 21179 82 130 0
2022-10-11 21097 91 130 0
2022-10-10 21006 41 130 0
2022-10-09 20965 22 130 0
2022-10-08 20943 45 130 0
2022-10-07 20898 61 130 0
2022-10-06 20837 110 130 1
2022-10-05 20727 98 129 0
2022-10-04 20629 44 129 0
2022-10-03 20585 3 129 0
2022-10-02 20582 2 129 0
2022-10-01 20580 46 129 0
2022-09-30 20534 33 129 0
2022-09-29 20501 27 129 0
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2022-07-27 19254 56 126 1
2022-07-26 19198 88 125 0
2022-07-25 19110 36 125 0
2022-07-24 19074 16 125 0
2022-07-23 19058 50 125 0
2022-07-22 19008 77 125 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: 0:03:26.412167