Germany: LK Bergstraße (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 12:28:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bergstraße", weeks=5);
2023-03-07T12:28:24.516676 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 90 90 100 100 110 110 120 120 7-day incidence rate (per 100K people) 85.2 LK Bergstraße, 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 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 R & growth factor (based on cases) Germany-LK Bergstraße cases daily growth factor Germany-LK Bergstraße cases daily growth factor (rolling mean) Germany-LK Bergstraße 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 Bergstraße deaths daily growth factor Germany-LK Bergstraße deaths daily growth factor (rolling mean) Germany-LK Bergstraße estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-LK Bergstraße doubling time cases (rolling mean) 0.00 27.12 54.23 daily change Germany-LK Bergstraße new cases (rolling 7d mean) Germany-LK Bergstraße new cases 0.000 0.542 1.085 1.627 daily change Germany-LK Bergstraße new deaths (rolling 7d mean) Germany-LK Bergstraße new deaths 0.000 0.342 0.684
In [4]:
overview(country="Germany", subregion="LK Bergstraße");
2023-03-07T12:28:44.589245 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) 85.2 LK Bergstraße, 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 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 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 Bergstraße cases daily growth factor Germany-LK Bergstraße cases daily growth factor (rolling mean) Germany-LK Bergstraße 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 Bergstraße deaths daily growth factor Germany-LK Bergstraße deaths daily growth factor (rolling mean) Germany-LK Bergstraße 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 Bergstraße doubling time cases (rolling mean) Germany-LK Bergstraße doubling time deaths (rolling mean) 0 542 1085 daily change Germany-LK Bergstraße new cases (rolling 7d mean) Germany-LK Bergstraße new cases 0.00 5.42 10.85 16.27 daily change Germany-LK Bergstraße new deaths (rolling 7d mean) Germany-LK Bergstraße new deaths 0.0 86.6 173.1 259.7 346.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bergstraße", dates="2020-03-15:");
2023-03-07T12:31:03.582958 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 Bergstraße LK Bergstraße 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 Bergstraße Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bergstraße")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bergstraße")
print(f'Population of country="Germany", subregion="LK Bergstraße": {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 Bergstraße": 271166 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 131244 32 524 0
2023-03-03 131212 44 524 0
2023-03-02 131168 35 524 0
2023-03-01 131133 76 524 0
2023-02-28 131057 44 524 0
2023-02-27 131013 76 524 0
2023-02-24 130937 65 524 0
2023-02-23 130872 73 524 0
2023-02-22 130799 58 524 0
2023-02-21 130741 74 524 0
2023-02-20 130667 52 524 2
2023-02-17 130615 50 522 0
2023-02-16 130565 57 522 0
2023-02-15 130508 57 522 0
2023-02-14 130451 54 522 0
2023-02-13 130397 65 522 0
2023-02-10 130332 52 522 0
2023-02-09 130280 37 522 0
2023-02-08 130243 61 522 0
2023-02-07 130182 72 522 0
2023-02-06 130110 71 522 0
2023-02-03 130039 37 522 1
2023-02-02 130002 43 521 0
2023-02-01 129959 47 521 0
2023-01-31 129912 74 521 0
2023-01-30 129838 58 521 0
2023-01-27 129780 44 521 0
2023-01-26 129736 37 521 0
2023-01-25 129699 36 521 0
2023-01-24 129663 38 521 0
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2023-01-20 129577 40 521 0
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2023-01-09 129161 74 521 1
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2022-12-27 128253 183 519 2
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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: 0:03:06.120111