Germany: SK Worms (Rheinland-Pfalz)¶

  • Homepage of project: https://oscovida.github.io
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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 15:11:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Worms", weeks=5);
2023-03-07T15:11:43.258284 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 64.4 SK Worms, 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 0.00 0.25 0.50 0.75 1.00 daily change normalised per 100K 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 Worms cases daily growth factor Germany-SK Worms cases daily growth factor (rolling mean) Germany-SK Worms 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 Worms deaths daily growth factor Germany-SK Worms deaths daily growth factor (rolling mean) Germany-SK Worms estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Worms doubling time cases (rolling mean) 0.00 16.77 33.54 daily change Germany-SK Worms new cases (rolling 7d mean) Germany-SK Worms new cases 0.000 0.210 0.419 0.629 0.839 daily change Germany-SK Worms new deaths (rolling 7d mean) Germany-SK Worms new deaths 0.000 0.338 0.676
In [4]:
overview(country="Germany", subregion="SK Worms");
2023-03-07T15:12:00.199788 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 2000 2000 7-day incidence rate (per 100K people) 64.4 SK Worms, 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 0.0 2.5 5.0 7.5 10.0 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 Worms cases daily growth factor Germany-SK Worms cases daily growth factor (rolling mean) Germany-SK Worms 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 Worms deaths daily growth factor Germany-SK Worms deaths daily growth factor (rolling mean) Germany-SK Worms 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 Worms doubling time cases (rolling mean) Germany-SK Worms doubling time deaths (rolling mean) 0.0 167.7 335.4 503.1 daily change Germany-SK Worms new cases (rolling 7d mean) Germany-SK Worms new cases 0.00 2.10 4.19 6.29 8.39 daily change Germany-SK Worms new deaths (rolling 7d mean) Germany-SK Worms new deaths 0.0 35.2 70.4 105.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Worms", dates="2020-03-15:");
2023-03-07T15:14:22.198317 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 Worms SK Worms 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 Worms Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Worms")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Worms")
print(f'Population of country="Germany", subregion="SK Worms": {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 Worms": 83850 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 38519 2 182 0
2023-03-02 38517 3 182 0
2023-03-01 38514 21 182 0
2023-02-28 38493 28 182 0
2023-02-27 38465 31 182 0
2023-02-24 38434 11 182 0
2023-02-23 38423 28 182 0
2023-02-22 38395 20 182 0
2023-02-21 38375 13 182 0
2023-02-20 38362 31 182 0
2023-02-17 38331 10 182 0
2023-02-16 38321 31 182 1
2023-02-15 38290 15 181 0
2023-02-14 38275 42 181 0
2023-02-13 38233 23 181 0
2023-02-10 38210 12 181 0
2023-02-09 38198 17 181 0
2023-02-08 38181 23 181 0
2023-02-07 38158 21 181 0
2023-02-06 38137 29 181 0
2023-02-03 38108 16 181 0
2023-02-02 38092 3 181 1
2023-02-01 38089 15 180 0
2023-01-31 38074 16 180 0
2023-01-30 38058 13 180 0
2023-01-27 38045 9 180 0
2023-01-26 38036 6 180 0
2023-01-25 38030 13 180 0
2023-01-24 38017 9 180 0
2023-01-23 38008 13 180 0
2023-01-20 37995 5 180 0
2023-01-19 37990 12 180 0
2023-01-18 37978 12 180 0
2023-01-17 37966 15 180 0
2023-01-16 37951 16 180 0
2023-01-13 37935 4 180 0
2023-01-12 37931 14 180 0
2023-01-11 37917 4 180 0
2023-01-10 37913 21 180 1
2023-01-09 37892 27 179 0
2023-01-06 37865 12 179 0
2023-01-05 37853 8 179 0
2023-01-04 37845 20 179 0
2023-01-03 37825 23 179 1
2023-01-02 37802 38 178 0
2022-12-30 37764 23 178 1
2022-12-29 37741 35 177 0
2022-12-28 37706 45 177 0
2022-12-27 37661 55 177 0
2022-12-23 37606 22 177 0
2022-12-22 37584 30 177 0
2022-12-21 37554 35 177 0
2022-12-20 37519 44 177 0
2022-12-19 37475 32 177 0
2022-12-16 37443 19 177 0
2022-12-15 37424 36 177 0
2022-12-14 37388 37 177 0
2022-12-13 37351 40 177 0
2022-12-12 37311 32 177 0
2022-12-09 37279 15 177 0
2022-12-08 37264 40 177 0
2022-12-07 37224 20 177 0
2022-12-06 37204 26 177 1
2022-12-05 37178 26 176 0
2022-12-02 37152 20 176 0
2022-12-01 37132 14 176 0
2022-11-30 37118 11 176 0
2022-11-29 37107 27 176 0
2022-11-28 37080 25 176 0
2022-11-25 37055 8 176 0
2022-11-24 37047 21 176 0
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2022-11-22 37000 25 176 0
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2022-10-28 36506 31 175 0
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2022-10-26 36422 73 175 1
2022-10-25 36349 83 174 0
2022-10-24 36266 89 174 0
2022-10-21 36177 48 174 0
2022-10-20 36129 218 174 0
2022-10-19 35911 206 174 0
2022-10-18 35705 205 174 0
2022-10-17 35500 295 174 0
2022-10-14 35205 179 174 0
2022-10-13 35026 59 174 1
2022-10-12 34967 94 173 0
2022-10-11 34873 69 173 0
2022-10-10 34804 109 173 0
2022-10-07 34695 73 173 0
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2022-10-05 34493 80 173 1
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2022-09-30 34334 53 172 0
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2022-09-27 33990 163 172 2
2022-09-26 33827 29 170 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: 0:03:39.385651