Germany: LK Nürnberger Land (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 11:12:33 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Nürnberger Land", weeks=5);
2023-03-07T11:12:48.404871 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) 70.6 LK Nürnberger Land, 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 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 Nürnberger Land cases daily growth factor Germany-LK Nürnberger Land cases daily growth factor (rolling mean) Germany-LK Nürnberger Land 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 Nürnberger Land deaths daily growth factor Germany-LK Nürnberger Land deaths daily growth factor (rolling mean) Germany-LK Nürnberger Land estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Nürnberger Land doubling time cases (rolling mean) 0.00 17.14 34.28 51.43 68.57 daily change Germany-LK Nürnberger Land new cases (rolling 7d mean) Germany-LK Nürnberger Land new cases 0.000 0.343 0.686 daily change Germany-LK Nürnberger Land new deaths (rolling 7d mean) Germany-LK Nürnberger Land new deaths 0.000 0.360 0.721
In [4]:
overview(country="Germany", subregion="LK Nürnberger Land");
2023-03-07T11:13:09.115279 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 3000 3000 7-day incidence rate (per 100K people) 70.6 LK Nürnberger Land, 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 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 Nürnberger Land cases daily growth factor Germany-LK Nürnberger Land cases daily growth factor (rolling mean) Germany-LK Nürnberger Land 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 Nürnberger Land deaths daily growth factor Germany-LK Nürnberger Land deaths daily growth factor (rolling mean) Germany-LK Nürnberger Land 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 8000 cases doubling time [days] Germany-LK Nürnberger Land doubling time cases (rolling mean) Germany-LK Nürnberger Land doubling time deaths (rolling mean) 0 343 686 1029 daily change Germany-LK Nürnberger Land new cases (rolling 7d mean) Germany-LK Nürnberger Land new cases 0.00 3.43 6.86 daily change Germany-LK Nürnberger Land new deaths (rolling 7d mean) Germany-LK Nürnberger Land new deaths 0.0 112.6 225.2 337.8 450.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Nürnberger Land", dates="2020-03-15:");
2023-03-07T11:15:57.162851 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 Nürnberger Land LK Nürnberger Land 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 Nürnberger Land Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Nürnberger Land")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Nürnberger Land")
print(f'Population of country="Germany", subregion="LK Nürnberger Land": {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 Nürnberger Land": 171424 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 84493 3 433 0
2023-03-03 84490 -1 433 0
2023-03-02 84491 23 433 0
2023-03-01 84468 49 433 0
2023-02-28 84419 47 433 0
2023-02-27 84372 82 433 0
2023-02-24 84290 35 433 0
2023-02-23 84255 26 433 0
2023-02-22 84229 67 433 0
2023-02-21 84162 82 433 0
2023-02-20 84080 27 433 0
2023-02-17 84053 42 433 0
2023-02-16 84011 38 433 1
2023-02-15 83973 66 432 0
2023-02-14 83907 55 432 0
2023-02-13 83852 55 432 0
2023-02-10 83797 60 432 1
2023-02-09 83737 27 431 0
2023-02-08 83710 51 431 0
2023-02-07 83659 46 431 0
2023-02-06 83613 62 431 0
2023-02-03 83551 32 431 0
2023-02-02 83519 43 431 0
2023-02-01 83476 53 431 0
2023-01-31 83423 44 431 0
2023-01-30 83379 52 431 1
2023-01-27 83327 28 430 0
2023-01-26 83299 25 430 0
2023-01-25 83274 25 430 0
2023-01-24 83249 36 430 1
2023-01-23 83213 41 429 0
2023-01-20 83172 28 429 0
2023-01-19 83144 29 429 0
2023-01-18 83115 33 429 0
2023-01-17 83082 29 429 0
2023-01-16 83053 46 429 1
2023-01-13 83007 25 428 0
2023-01-12 82982 34 428 0
2023-01-11 82948 42 428 0
2023-01-10 82906 47 428 1
2023-01-09 82859 72 427 0
2023-01-05 82787 19 427 0
2023-01-04 82768 38 427 0
2023-01-03 82730 33 427 0
2023-01-02 82697 60 427 0
2022-12-30 82637 43 427 0
2022-12-29 82594 34 427 1
2022-12-28 82560 50 426 0
2022-12-27 82510 110 426 0
2022-12-23 82400 71 426 0
2022-12-22 82329 30 426 0
2022-12-21 82299 72 426 0
2022-12-20 82227 63 426 0
2022-12-19 82164 75 426 2
2022-12-16 82089 38 424 0
2022-12-15 82051 68 424 0
2022-12-14 81983 30 424 0
2022-12-13 81953 60 424 0
2022-12-12 81893 79 424 1
2022-12-09 81814 21 423 0
2022-12-08 81793 38 423 0
2022-12-07 81755 56 423 0
2022-12-06 81699 55 423 1
2022-12-05 81644 68 422 0
2022-12-02 81576 40 422 1
2022-12-01 81536 28 421 0
2022-11-30 81508 43 421 0
2022-11-29 81465 57 421 0
2022-11-28 81408 66 421 0
2022-11-25 81342 37 421 0
2022-11-24 81305 59 421 0
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2022-11-18 81066 33 421 1
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2022-11-06 80409 1 418 0
2022-11-04 80408 80 418 1
2022-11-03 80328 72 417 0
2022-11-02 80256 142 417 0
2022-10-31 80114 151 417 0
2022-10-28 79963 136 417 0
2022-10-27 79827 62 417 0
2022-10-26 79765 171 417 0
2022-10-25 79594 175 417 1
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2022-10-21 79207 217 416 0
2022-10-20 78990 194 416 0
2022-10-19 78796 233 416 1
2022-10-18 78563 395 415 0
2022-10-17 78168 406 415 2
2022-10-15 77762 1 413 0
2022-10-14 77761 137 413 0
2022-10-13 77624 314 413 1
2022-10-12 77310 216 412 0
2022-10-11 77094 427 412 2
2022-10-10 76667 558 410 2
2022-10-08 76109 1 408 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: 0:03:36.430144