Germany: LK Fürth (Bayern)¶

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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 10:47:26 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Fürth", weeks=5);
2023-03-07T10:47:39.780541 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 7-day incidence rate (per 100K people) 85.4 LK Fürth, 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 30 40 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 0.8 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 Fürth cases daily growth factor Germany-LK Fürth cases daily growth factor (rolling mean) Germany-LK Fürth estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Fürth deaths daily growth factor Germany-LK Fürth deaths daily growth factor (rolling mean) Germany-LK Fürth 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 Fürth doubling time cases (rolling mean) 0.00 11.94 23.89 35.83 47.77 daily change Germany-LK Fürth new cases (rolling 7d mean) Germany-LK Fürth new cases 0.000 0.239 0.478 0.717 0.955 daily change Germany-LK Fürth new deaths (rolling 7d mean) Germany-LK Fürth new deaths 0.000 0.299 0.598 0.896
In [4]:
overview(country="Germany", subregion="LK Fürth");
2023-03-07T10:47:57.021250 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 7-day incidence rate (per 100K people) 85.4 LK Fürth, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Fürth cases daily growth factor Germany-LK Fürth cases daily growth factor (rolling mean) Germany-LK Fürth estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Fürth deaths daily growth factor Germany-LK Fürth deaths daily growth factor (rolling mean) Germany-LK Fürth 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-LK Fürth doubling time cases (rolling mean) Germany-LK Fürth doubling time deaths (rolling mean) 0.0 238.9 477.7 716.6 daily change Germany-LK Fürth new cases (rolling 7d mean) Germany-LK Fürth new cases 0.00 2.39 4.78 7.17 9.55 daily change Germany-LK Fürth new deaths (rolling 7d mean) Germany-LK Fürth new deaths 0.0 64.9 129.8 194.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Fürth", dates="2020-03-15:");
2023-03-07T10:50:35.230846 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 Fürth LK Fürth 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 Fürth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Fürth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Fürth")
print(f'Population of country="Germany", subregion="LK Fürth": {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 Fürth": 119432 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 60516 19 317 0
2023-03-03 60497 14 317 0
2023-03-02 60483 13 317 0
2023-03-01 60470 20 317 0
2023-02-28 60450 36 317 0
2023-02-27 60414 52 317 0
2023-02-24 60362 24 317 0
2023-02-23 60338 23 317 0
2023-02-22 60315 23 317 0
2023-02-21 60292 37 317 0
2023-02-20 60255 53 317 0
2023-02-17 60202 36 317 0
2023-02-16 60166 40 317 1
2023-02-15 60126 28 316 0
2023-02-14 60098 44 316 0
2023-02-13 60054 45 316 1
2023-02-10 60009 26 315 0
2023-02-09 59983 35 315 0
2023-02-08 59948 30 315 0
2023-02-07 59918 37 315 0
2023-02-06 59881 41 315 0
2023-02-03 59840 15 315 0
2023-02-02 59825 25 315 0
2023-02-01 59800 38 315 0
2023-01-31 59762 25 315 0
2023-01-30 59737 28 315 0
2023-01-27 59709 13 315 0
2023-01-26 59696 13 315 0
2023-01-25 59683 12 315 0
2023-01-24 59671 21 315 0
2023-01-23 59650 19 315 0
2023-01-20 59631 13 315 0
2023-01-19 59618 11 315 0
2023-01-18 59607 22 315 0
2023-01-17 59585 18 315 0
2023-01-16 59567 14 315 0
2023-01-13 59553 7 315 0
2023-01-12 59546 13 315 0
2023-01-11 59533 16 315 0
2023-01-10 59517 24 315 0
2023-01-09 59493 43 315 0
2023-01-05 59450 13 315 0
2023-01-04 59437 17 315 0
2023-01-03 59420 21 315 0
2023-01-02 59399 33 315 0
2022-12-30 59366 15 315 0
2022-12-29 59351 23 315 1
2022-12-28 59328 36 314 0
2022-12-27 59292 78 314 0
2022-12-23 59214 32 314 0
2022-12-22 59182 30 314 1
2022-12-21 59152 30 313 1
2022-12-20 59122 56 312 1
2022-12-19 59066 38 311 0
2022-12-16 59028 24 311 1
2022-12-15 59004 29 310 0
2022-12-14 58975 27 310 1
2022-12-13 58948 40 309 0
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2022-12-08 58818 23 309 1
2022-12-07 58795 48 308 0
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2022-12-05 58713 48 308 0
2022-12-02 58665 23 308 0
2022-12-01 58642 32 308 0
2022-11-30 58610 34 308 0
2022-11-29 58576 41 308 0
2022-11-28 58535 45 308 0
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2022-11-24 58464 18 308 1
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2022-10-17 56463 253 303 2
2022-10-15 56210 3 301 0
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2022-10-04 54342 276 301 1
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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: 0:03:45.717526