Germany: LK Gießen (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 12:36:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Gießen", weeks=5);
2023-03-07T12:36:23.486843 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 125 125 150 150 175 175 200 200 225 225 7-day incidence rate (per 100K people) 127.9 LK Gießen, 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 0.1 0.2 0.3 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-LK Gießen cases daily growth factor Germany-LK Gießen cases daily growth factor (rolling mean) Germany-LK Gießen 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-LK Gießen deaths daily growth factor Germany-LK Gießen deaths daily growth factor (rolling mean) Germany-LK Gießen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Gießen doubling time cases (rolling mean) 0.0 54.6 109.1 163.7 daily change Germany-LK Gießen new cases (rolling 7d mean) Germany-LK Gießen new cases 0.000 0.273 0.546 0.819 daily change Germany-LK Gießen new deaths (rolling 7d mean) Germany-LK Gießen new deaths 0.000 0.311 0.622 0.933
In [4]:
overview(country="Germany", subregion="LK Gießen");
2023-03-07T12:36:41.089597 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) 127.9 LK Gießen, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Gießen cases daily growth factor Germany-LK Gießen cases daily growth factor (rolling mean) Germany-LK Gießen 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-LK Gießen deaths daily growth factor Germany-LK Gießen deaths daily growth factor (rolling mean) Germany-LK Gießen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Gießen doubling time cases (rolling mean) Germany-LK Gießen doubling time deaths (rolling mean) 0 273 546 819 1091 daily change Germany-LK Gießen new cases (rolling 7d mean) Germany-LK Gießen new cases 0.00 5.46 10.91 16.37 21.83 daily change Germany-LK Gießen new deaths (rolling 7d mean) Germany-LK Gießen new deaths 0.0 97.2 194.3 291.5 388.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Gießen", dates="2020-03-15:");
2023-03-07T12:39:32.141938 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 Gießen LK Gießen 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 Gießen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Gießen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Gießen")
print(f'Population of country="Germany", subregion="LK Gießen": {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 Gießen": 272874 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 137239 26 458 0
2023-03-03 137213 32 458 0
2023-03-02 137181 69 458 0
2023-03-01 137112 28 458 0
2023-02-28 137084 194 458 0
2023-02-27 136890 131 458 0
2023-02-26 136759 2 458 0
2023-02-24 136757 88 458 0
2023-02-23 136669 154 458 0
2023-02-22 136515 88 458 0
2023-02-21 136427 56 458 1
2023-02-20 136371 151 457 0
2023-02-18 136220 23 457 0
2023-02-17 136197 74 457 0
2023-02-16 136123 94 457 0
2023-02-15 136029 81 457 0
2023-02-14 135948 116 457 0
2023-02-13 135832 157 457 0
2023-02-10 135675 63 457 0
2023-02-09 135612 73 457 0
2023-02-08 135539 73 457 0
2023-02-07 135466 67 457 0
2023-02-06 135399 129 457 0
2023-02-04 135270 1 457 0
2023-02-03 135269 58 457 0
2023-02-02 135211 65 457 0
2023-02-01 135146 57 457 0
2023-01-31 135089 81 457 0
2023-01-30 135008 129 457 0
2023-01-27 134879 43 457 0
2023-01-26 134836 44 457 0
2023-01-25 134792 62 457 0
2023-01-24 134730 65 457 0
2023-01-23 134665 134 457 0
2023-01-22 134531 2 457 0
2023-01-20 134529 49 457 0
2023-01-19 134480 88 457 1
2023-01-18 134392 47 456 0
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2023-01-16 134276 105 456 1
2023-01-15 134171 1 455 0
2023-01-14 134170 1 455 0
2023-01-13 134169 41 455 1
2023-01-12 134128 51 454 0
2023-01-11 134077 73 454 2
2023-01-10 134004 81 452 0
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2023-01-06 133767 76 451 0
2023-01-05 133691 68 451 0
2023-01-04 133623 91 451 0
2023-01-03 133532 122 451 0
2023-01-02 133410 174 451 0
2022-12-31 133236 93 451 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: 0:03:59.022892