Germany: LK Vulkaneifel (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 14:56:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Vulkaneifel", weeks=5);
2023-03-07T14:56:38.313864 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 200 200 300 300 400 400 500 500 7-day incidence rate (per 100K people) 177.4 LK Vulkaneifel, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 150 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 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Vulkaneifel cases daily growth factor Germany-LK Vulkaneifel cases daily growth factor (rolling mean) Germany-LK Vulkaneifel 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 R & growth factor (based on deaths) Germany-LK Vulkaneifel deaths daily growth factor Germany-LK Vulkaneifel deaths daily growth factor (rolling mean) Germany-LK Vulkaneifel estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] Germany-LK Vulkaneifel doubling time cases (rolling mean) 0.0 30.4 60.9 91.3 daily change Germany-LK Vulkaneifel new cases (rolling 7d mean) Germany-LK Vulkaneifel new cases 0.000 0.304 0.609 0.913 daily change Germany-LK Vulkaneifel new deaths (rolling 7d mean) Germany-LK Vulkaneifel new deaths 0.000 0.248 0.496 0.745 0.993
In [4]:
overview(country="Germany", subregion="LK Vulkaneifel");
2023-03-07T14:56:57.066689 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) 177.4 LK Vulkaneifel, 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 2 4 6 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Vulkaneifel cases daily growth factor Germany-LK Vulkaneifel cases daily growth factor (rolling mean) Germany-LK Vulkaneifel 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 R & growth factor (based on deaths) Germany-LK Vulkaneifel deaths daily growth factor Germany-LK Vulkaneifel deaths daily growth factor (rolling mean) Germany-LK Vulkaneifel estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Vulkaneifel doubling time cases (rolling mean) Germany-LK Vulkaneifel doubling time deaths (rolling mean) 0.0 121.8 243.5 365.3 daily change Germany-LK Vulkaneifel new cases (rolling 7d mean) Germany-LK Vulkaneifel new cases 0.000 1.218 2.435 3.653 4.871 daily change Germany-LK Vulkaneifel new deaths (rolling 7d mean) Germany-LK Vulkaneifel new deaths 0.00 13.29 26.58 39.87 53.16 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Vulkaneifel", dates="2020-03-15:");
2023-03-07T14:59:32.158153 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 Vulkaneifel LK Vulkaneifel 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 LK Vulkaneifel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Vulkaneifel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Vulkaneifel")
print(f'Population of country="Germany", subregion="LK Vulkaneifel": {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 Vulkaneifel": 60882 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 30621 15 99 0
2023-03-03 30606 11 99 0
2023-03-02 30595 19 99 0
2023-03-01 30576 35 99 0
2023-02-28 30541 28 99 0
2023-02-27 30513 101 99 0
2023-02-24 30412 50 99 0
2023-02-23 30362 53 99 0
2023-02-22 30309 87 99 0
2023-02-17 30222 25 99 0
2023-02-16 30197 33 99 0
2023-02-15 30164 23 99 0
2023-02-14 30141 37 99 0
2023-02-13 30104 25 99 0
2023-02-10 30079 29 99 0
2023-02-09 30050 25 99 0
2023-02-08 30025 19 99 0
2023-02-07 30006 28 99 0
2023-02-06 29978 27 99 1
2023-02-03 29951 21 98 0
2023-02-02 29930 13 98 0
2023-02-01 29917 15 98 0
2023-01-31 29902 16 98 0
2023-01-30 29886 18 98 0
2023-01-27 29868 4 98 0
2023-01-26 29864 7 98 0
2023-01-25 29857 11 98 0
2023-01-24 29846 14 98 0
2023-01-23 29832 14 98 0
2023-01-20 29818 9 98 0
2023-01-19 29809 10 98 0
2023-01-18 29799 13 98 0
2023-01-17 29786 13 98 0
2023-01-16 29773 14 98 0
2023-01-13 29759 10 98 0
2023-01-12 29749 13 98 0
2023-01-11 29736 24 98 0
2023-01-10 29712 25 98 0
2023-01-09 29687 22 98 0
2023-01-06 29665 20 98 0
2023-01-05 29645 19 98 0
2023-01-04 29626 13 98 0
2023-01-03 29613 22 98 0
2023-01-02 29591 18 98 0
2022-12-30 29573 16 98 1
2022-12-29 29557 27 97 0
2022-12-28 29530 24 97 0
2022-12-27 29506 49 97 0
2022-12-23 29457 23 97 0
2022-12-22 29434 36 97 0
2022-12-21 29398 43 97 0
2022-12-20 29355 28 97 0
2022-12-19 29327 55 97 0
2022-12-16 29272 21 97 0
2022-12-15 29251 21 97 0
2022-12-14 29230 28 97 0
2022-12-13 29202 35 97 0
2022-12-12 29167 27 97 0
2022-12-09 29140 25 97 0
2022-12-08 29115 20 97 0
2022-12-07 29095 21 97 0
2022-12-06 29074 22 97 0
2022-12-05 29052 32 97 1
2022-12-02 29020 13 96 0
2022-12-01 29007 7 96 0
2022-11-30 29000 25 96 0
2022-11-29 28975 22 96 0
2022-11-28 28953 32 96 0
2022-11-25 28921 27 96 0
2022-11-24 28894 14 96 0
2022-11-23 28880 34 96 0
2022-11-22 28846 36 96 0
2022-11-21 28810 58 96 0
2022-11-18 28752 21 96 0
2022-11-17 28731 30 96 0
2022-11-16 28701 43 96 0
2022-11-15 28658 19 96 0
2022-11-14 28639 65 96 0
2022-11-11 28574 27 96 0
2022-11-10 28547 27 96 0
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2022-11-08 28478 44 96 0
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2022-11-04 28390 30 96 0
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2022-10-31 28245 56 96 0
2022-10-28 28189 43 96 0
2022-10-27 28146 44 96 0
2022-10-26 28102 63 96 0
2022-10-25 28039 76 96 0
2022-10-24 27963 88 96 0
2022-10-21 27875 60 96 0
2022-10-20 27815 68 96 0
2022-10-19 27747 127 96 0
2022-10-18 27620 122 96 0
2022-10-17 27498 150 96 0
2022-10-14 27348 82 96 0
2022-10-13 27266 96 96 0
2022-10-12 27170 189 96 0
2022-10-11 26981 102 96 0
2022-10-10 26879 189 96 0
2022-10-07 26690 137 96 0
2022-10-06 26553 174 96 0
2022-10-05 26379 111 96 0
2022-10-04 26268 198 96 0
2022-09-30 26070 107 96 0
2022-09-29 25963 56 96 0
2022-09-28 25907 100 96 0
2022-09-27 25807 100 96 0
2022-09-26 25707 100 96 0
2022-09-23 25607 63 96 0
2022-09-22 25544 70 96 0
2022-09-21 25474 25 96 0
2022-09-20 25449 80 96 0
2022-09-19 25369 53 96 0
2022-09-16 25316 35 96 0
2022-09-15 25281 36 96 0
2022-09-14 25245 65 96 0
2022-09-13 25180 32 96 0
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2022-09-09 25107 24 96 1
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2022-08-26 24744 23 95 0
2022-08-25 24721 30 95 0
2022-08-24 24691 29 95 0
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2022-08-22 24595 35 95 0
2022-08-19 24560 24 95 0
2022-08-18 24536 38 95 0
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2022-08-04 24102 36 95 0
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2022-08-01 23957 99 95 0
2022-07-29 23858 56 95 1
2022-07-28 23802 28 94 0
2022-07-27 23774 80 94 0
2022-07-26 23694 164 94 0
2022-07-25 23530 95 94 0
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2022-05-23 19075 73 89 2
2022-05-20 19002 58 87 0
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2022-05-04 18122 104 86 1
2022-05-03 18018 75 85 0
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2022-04-30 17817 31 85 0
2022-04-29 17786 118 85 0
2022-04-28 17668 102 85 0
2022-04-27 17566 146 85 0
2022-04-26 17420 105 85 0
2022-04-25 17315 170 85 0
2022-04-23 17145 116 85 0
2022-04-22 17029 113 85 0
2022-04-21 16916 145 85 0
2022-04-20 16771 301 85 2
2022-04-19 16470 311 83 0
2022-04-16 16159 307 83 0
2022-04-14 15852 225 83 0
2022-04-13 15627 118 83 0
2022-04-12 15509 180 83 0
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2022-04-01 13929 140 82 1
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2022-03-30 13525 257 81 3
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2021-05-29 2019 6 61 0
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2021-05-26 2004 2 61 0
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2021-05-22 1995 2 61 0
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2021-01-04 1049 3 37 1
2021-01-03 1046 2 36 0
2021-01-02 1044 6 36 0
2021-01-01 1038 4 36 0
2020-12-31 1034 8 36 0
2020-12-30 1026 8 36 0
2020-12-29 1018 6 36 1
2020-12-28 1012 10 35 0
2020-12-27 1002 8 35 0
2020-12-26 994 4 35 0
2020-12-25 990 6 35 0
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2020-12-22 945 16 35 0
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2020-12-17 867 15 33 1
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2020-12-15 817 11 31 1
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2020-12-05 675 11 23 0
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2020-10-25 269 6 7 0
2020-10-24 263 4 7 0
2020-10-23 259 7 7 0
2020-10-22 252 17 7 0
2020-10-21 235 12 7 0
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2020-10-18 215 9 6 0
2020-10-17 206 16 6 0
2020-10-16 190 1 6 0
2020-10-15 189 11 6 0
2020-10-14 178 6 6 0
2020-10-13 172 9 6 0
2020-10-12 163 6 6 0
2020-10-10 157 4 6 0
2020-10-09 153 4 6 0
2020-10-08 149 2 6 0
2020-10-05 147 1 6 0
2020-10-02 146 2 6 0
2020-09-30 144 1 6 0
2020-09-17 143 1 6 0
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2020-09-08 141 1 6 0
2020-09-05 140 1 6 0
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2020-09-01 138 3 6 0
2020-08-27 135 1 6 0
2020-08-26 134 1 6 0
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2020-08-05 129 1 6 0
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2020-07-29 127 1 6 0
2020-07-24 126 1 6 0
2020-07-21 125 1 6 0
2020-07-09 124 1 6 0
2020-06-29 123 1 6 0
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2020-04-23 109 1 4 0
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2020-04-04 82 17 1 0
2020-04-03 65 1 1 0
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2020-03-31 52 1 0 0
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2020-03-20 31 6 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: 0:03:18.773634