Germany: SK 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 11:44:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Fürth", weeks=5);
2023-03-07T11:44:21.575049 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 85.2 SK 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 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 Fürth cases daily growth factor Germany-SK Fürth cases daily growth factor (rolling mean) Germany-SK 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 8 8 R & growth factor (based on deaths) Germany-SK Fürth deaths daily growth factor Germany-SK Fürth deaths daily growth factor (rolling mean) Germany-SK Fürth 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 Fürth doubling time cases (rolling mean) 0.00 12.91 25.82 38.74 51.65 daily change Germany-SK Fürth new cases (rolling 7d mean) Germany-SK Fürth new cases 0.000 0.258 0.516 0.775 daily change Germany-SK Fürth new deaths (rolling 7d mean) Germany-SK Fürth new deaths 0.000 0.233 0.465 0.698 0.930
In [4]:
overview(country="Germany", subregion="SK Fürth");
2023-03-07T11:44:39.924785 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) 85.2 SK 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 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 Fürth cases daily growth factor Germany-SK Fürth cases daily growth factor (rolling mean) Germany-SK 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 8 8 R & growth factor (based on deaths) Germany-SK Fürth deaths daily growth factor Germany-SK Fürth deaths daily growth factor (rolling mean) Germany-SK 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-SK Fürth doubling time cases (rolling mean) Germany-SK Fürth doubling time deaths (rolling mean) 0.0 258.2 516.5 daily change Germany-SK Fürth new cases (rolling 7d mean) Germany-SK Fürth new cases 0.00 3.23 6.46 9.68 12.91 daily change Germany-SK Fürth new deaths (rolling 7d mean) Germany-SK Fürth new deaths 0.0 82.6 165.1 247.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Fürth", dates="2020-03-15:");
2023-03-07T11:46:50.573352 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 Fürth SK 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 SK Fürth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Fürth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Fürth")
print(f'Population of country="Germany", subregion="SK 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="SK Fürth": 129122 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 65554 18 342 0
2023-03-03 65536 11 342 0
2023-03-02 65525 16 342 0
2023-03-01 65509 27 342 0
2023-02-28 65482 38 342 0
2023-02-27 65444 47 342 0
2023-02-24 65397 24 342 0
2023-02-23 65373 31 342 0
2023-02-22 65342 36 342 0
2023-02-21 65306 52 342 1
2023-02-20 65254 47 341 1
2023-02-17 65207 35 340 0
2023-02-16 65172 40 340 0
2023-02-15 65132 36 340 0
2023-02-14 65096 48 340 0
2023-02-13 65048 56 340 0
2023-02-10 64992 22 340 0
2023-02-09 64970 37 340 0
2023-02-08 64933 44 340 0
2023-02-07 64889 55 340 0
2023-02-06 64834 63 340 0
2023-02-03 64771 25 340 0
2023-02-02 64746 38 340 0
2023-02-01 64708 27 340 0
2023-01-31 64681 50 340 0
2023-01-30 64631 41 340 1
2023-01-27 64590 20 339 0
2023-01-26 64570 27 339 0
2023-01-25 64543 24 339 0
2023-01-24 64519 26 339 0
2023-01-23 64493 20 339 0
2023-01-20 64473 11 339 0
2023-01-19 64462 13 339 0
2023-01-18 64449 17 339 0
2023-01-17 64432 25 339 0
2023-01-16 64407 29 339 0
2023-01-13 64378 13 339 0
2023-01-12 64365 18 339 0
2023-01-11 64347 16 339 0
2023-01-10 64331 39 339 0
2023-01-09 64292 62 339 0
2023-01-05 64230 28 339 0
2023-01-04 64202 35 339 0
2023-01-03 64167 22 339 0
2023-01-02 64145 42 339 0
2022-12-30 64103 24 339 1
2022-12-29 64079 37 338 0
2022-12-28 64042 41 338 1
2022-12-27 64001 86 337 1
2022-12-23 63915 49 336 0
2022-12-22 63866 39 336 0
2022-12-21 63827 53 336 0
2022-12-20 63774 51 336 2
2022-12-19 63723 47 334 0
2022-12-16 63676 37 334 0
2022-12-15 63639 45 334 0
2022-12-14 63594 25 334 0
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2022-12-12 63528 40 334 0
2022-12-09 63488 18 334 0
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2022-12-07 63439 36 334 0
2022-12-06 63403 35 334 0
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2022-12-02 63331 21 334 0
2022-12-01 63310 35 334 1
2022-11-30 63275 41 333 0
2022-11-29 63234 54 333 0
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2022-10-25 61916 133 329 0
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2022-10-20 61493 95 329 0
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2022-10-18 61230 196 329 1
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2022-10-14 60793 145 328 0
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2022-10-11 60214 246 328 1
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2022-10-08 59596 1 326 0
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2022-10-06 59402 203 326 0
2022-10-05 59199 272 326 1
2022-10-04 58927 304 325 0
2022-10-02 58623 1 325 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: 0:03:13.472487