Germany: SK Darmstadt (Hessen)¶

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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 12:44:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Darmstadt", weeks=5);
2023-03-07T12:44:42.780096 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 73.9 SK Darmstadt, 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 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Darmstadt cases daily growth factor Germany-SK Darmstadt cases daily growth factor (rolling mean) Germany-SK Darmstadt 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 Darmstadt deaths daily growth factor Germany-SK Darmstadt deaths daily growth factor (rolling mean) Germany-SK Darmstadt 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 Darmstadt doubling time cases (rolling mean) 0.0 31.9 63.9 95.8 daily change Germany-SK Darmstadt new cases (rolling 7d mean) Germany-SK Darmstadt new cases 0.000 0.319 0.639 0.958 daily change Germany-SK Darmstadt new deaths (rolling 7d mean) Germany-SK Darmstadt new deaths 0.000 0.201 0.403 0.604 0.805
In [4]:
overview(country="Germany", subregion="SK Darmstadt");
2023-03-07T12:45:00.218535 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) 73.9 SK Darmstadt, 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 2 4 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Darmstadt cases daily growth factor Germany-SK Darmstadt cases daily growth factor (rolling mean) Germany-SK Darmstadt 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 Darmstadt deaths daily growth factor Germany-SK Darmstadt deaths daily growth factor (rolling mean) Germany-SK Darmstadt 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-SK Darmstadt doubling time cases (rolling mean) Germany-SK Darmstadt doubling time deaths (rolling mean) 0.0 159.6 319.3 478.9 638.5 daily change Germany-SK Darmstadt new cases (rolling 7d mean) Germany-SK Darmstadt new cases 0.000 3.193 6.385 daily change Germany-SK Darmstadt new deaths (rolling 7d mean) Germany-SK Darmstadt new deaths 0.0 48.7 97.4 146.1 194.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Darmstadt", dates="2020-03-15:");
2023-03-07T12:47:07.616079 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 Darmstadt SK Darmstadt 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 Darmstadt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Darmstadt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Darmstadt")
print(f'Population of country="Germany", subregion="SK Darmstadt": {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 Darmstadt": 159631 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 71284 24 274 0
2023-03-03 71260 11 274 0
2023-03-02 71249 6 274 0
2023-03-01 71243 37 274 0
2023-02-28 71206 40 274 0
2023-02-27 71166 119 274 0
2023-02-24 71047 31 274 0
2023-02-23 71016 36 274 1
2023-02-22 70980 49 273 0
2023-02-21 70931 31 273 0
2023-02-20 70900 100 273 0
2023-02-17 70800 23 273 0
2023-02-16 70777 38 273 0
2023-02-15 70739 62 273 0
2023-02-14 70677 50 273 0
2023-02-13 70627 78 273 0
2023-02-10 70549 31 273 0
2023-02-09 70518 18 273 0
2023-02-08 70500 35 273 0
2023-02-07 70465 32 273 0
2023-02-06 70433 54 273 0
2023-02-03 70379 31 273 0
2023-02-02 70348 33 273 0
2023-02-01 70315 38 273 0
2023-01-31 70277 25 273 1
2023-01-30 70252 64 272 0
2023-01-27 70188 24 272 1
2023-01-26 70164 29 271 0
2023-01-25 70135 28 271 0
2023-01-24 70107 26 271 0
2023-01-23 70081 35 271 0
2023-01-20 70046 23 271 0
2023-01-19 70023 23 271 0
2023-01-18 70000 24 271 0
2023-01-17 69976 23 271 0
2023-01-16 69953 45 271 0
2023-01-13 69908 27 271 0
2023-01-12 69881 22 271 0
2023-01-11 69859 45 271 1
2023-01-10 69814 49 270 0
2023-01-09 69765 83 270 0
2023-01-06 69682 40 270 1
2023-01-05 69642 37 269 0
2023-01-04 69605 62 269 0
2023-01-03 69543 63 269 0
2023-01-02 69480 115 269 2
2022-12-30 69365 75 267 0
2022-12-29 69290 58 267 0
2022-12-28 69232 173 267 0
2022-12-27 69059 179 267 0
2022-12-23 68880 99 267 1
2022-12-22 68781 43 266 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: 0:02:52.471858