Germany: LK Neustadt a.d.Waldnaab (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 11:12:46 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neustadt a.d.Waldnaab", weeks=5);
2023-03-07T11:13:00.898324 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 300 300 7-day incidence rate (per 100K people) 112.8 LK Neustadt a.d.Waldnaab, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Neustadt a.d.Waldnaab new deaths (rolling 7d mean) Germany-LK Neustadt a.d.Waldnaab new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Neustadt a.d.Waldnaab cases daily growth factor Germany-LK Neustadt a.d.Waldnaab cases daily growth factor (rolling mean) Germany-LK Neustadt a.d.Waldnaab estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Neustadt a.d.Waldnaab deaths daily growth factor Germany-LK Neustadt a.d.Waldnaab deaths daily growth factor (rolling mean) Germany-LK Neustadt a.d.Waldnaab 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-LK Neustadt a.d.Waldnaab doubling time cases (rolling mean) 0.00 18.97 37.94 56.90 daily change Germany-LK Neustadt a.d.Waldnaab new cases (rolling 7d mean) Germany-LK Neustadt a.d.Waldnaab new cases 0.000 0.250 0.499 0.749 0.998
In [4]:
overview(country="Germany", subregion="LK Neustadt a.d.Waldnaab");
2023-03-07T11:13:17.187658 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) 112.8 LK Neustadt a.d.Waldnaab, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Neustadt a.d.Waldnaab cases daily growth factor Germany-LK Neustadt a.d.Waldnaab cases daily growth factor (rolling mean) Germany-LK Neustadt a.d.Waldnaab estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Neustadt a.d.Waldnaab deaths daily growth factor Germany-LK Neustadt a.d.Waldnaab deaths daily growth factor (rolling mean) Germany-LK Neustadt a.d.Waldnaab 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 Neustadt a.d.Waldnaab doubling time cases (rolling mean) Germany-LK Neustadt a.d.Waldnaab doubling time deaths (rolling mean) 0.0 189.7 379.4 569.0 daily change Germany-LK Neustadt a.d.Waldnaab new cases (rolling 7d mean) Germany-LK Neustadt a.d.Waldnaab new cases 0.000 1.897 3.794 5.690 daily change Germany-LK Neustadt a.d.Waldnaab new deaths (rolling 7d mean) Germany-LK Neustadt a.d.Waldnaab new deaths 0 56 112 168 224 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neustadt a.d.Waldnaab", dates="2020-03-15:");
2023-03-07T11:16:07.758299 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 Neustadt a.d.Waldnaab LK Neustadt a.d.Waldnaab 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 Neustadt a.d.Waldnaab Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neustadt a.d.Waldnaab")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neustadt a.d.Waldnaab")
print(f'Population of country="Germany", subregion="LK Neustadt a.d.Waldnaab": {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 Neustadt a.d.Waldnaab": 94838 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 53963 6 274 0
2023-03-05 53957 1 274 0
2023-03-04 53956 3 274 0
2023-03-03 53953 16 274 0
2023-03-02 53937 10 274 0
2023-03-01 53927 21 274 0
2023-02-28 53906 50 274 0
2023-02-27 53856 38 274 0
2023-02-26 53818 6 274 0
2023-02-25 53812 16 274 0
2023-02-24 53796 45 274 0
2023-02-23 53751 57 274 0
2023-02-22 53694 44 274 0
2023-02-21 53650 70 274 0
2023-02-20 53580 33 274 0
2023-02-19 53547 12 274 0
2023-02-18 53535 14 274 0
2023-02-17 53521 30 274 0
2023-02-16 53491 47 274 0
2023-02-15 53444 43 274 0
2023-02-14 53401 54 274 0
2023-02-13 53347 49 274 0
2023-02-12 53298 10 274 0
2023-02-11 53288 5 274 0
2023-02-10 53283 32 274 0
2023-02-09 53251 51 274 0
2023-02-08 53200 43 274 0
2023-02-07 53157 60 274 0
2023-02-06 53097 47 274 0
2023-02-05 53050 8 274 0
2023-02-04 53042 13 274 0
2023-02-03 53029 38 274 0
2023-02-02 52991 36 274 0
2023-02-01 52955 37 274 0
2023-01-31 52918 70 274 0
2023-01-30 52848 48 274 0
2023-01-29 52800 9 274 0
2023-01-28 52791 5 274 0
2023-01-27 52786 33 274 0
2023-01-26 52753 18 274 0
2023-01-25 52735 35 274 0
2023-01-24 52700 24 274 0
2023-01-23 52676 23 274 0
2023-01-22 52653 4 274 0
2023-01-21 52649 5 274 0
2023-01-20 52644 15 274 0
2023-01-19 52629 15 274 0
2023-01-18 52614 19 274 0
2023-01-17 52595 24 274 0
2023-01-16 52571 30 274 0
2023-01-15 52541 3 274 0
2023-01-14 52538 6 274 0
2023-01-13 52532 11 274 0
2023-01-12 52521 18 274 0
2023-01-11 52503 19 274 0
2023-01-10 52484 41 274 0
2023-01-09 52443 41 274 0
2023-01-08 52402 4 274 0
2023-01-07 52398 3 274 0
2023-01-06 52395 6 274 0
2023-01-05 52389 53 274 0
2023-01-04 52336 10 274 0
2023-01-03 52326 31 274 0
2023-01-02 52295 17 274 0
2023-01-01 52278 4 274 0
2022-12-31 52274 5 274 0
2022-12-30 52269 14 274 1
2022-12-29 52255 23 273 0
2022-12-28 52232 28 273 0
2022-12-27 52204 23 273 0
2022-12-26 52181 10 273 0
2022-12-25 52171 4 273 0
2022-12-24 52167 13 273 0
2022-12-23 52154 8 273 0
2022-12-22 52146 21 273 0
2022-12-21 52125 16 273 0
2022-12-20 52109 22 273 0
2022-12-19 52087 23 273 0
2022-12-18 52064 5 273 0
2022-12-17 52059 5 273 0
2022-12-16 52054 12 273 0
2022-12-15 52042 17 273 0
2022-12-14 52025 31 273 0
2022-12-13 51994 13 273 0
2022-12-12 51981 15 273 0
2022-12-11 51966 4 273 0
2022-12-10 51962 5 273 0
2022-12-09 51957 18 273 0
2022-12-08 51939 9 273 0
2022-12-07 51930 26 273 0
2022-12-06 51904 34 273 0
2022-12-05 51870 21 273 0
2022-12-04 51849 3 273 0
2022-12-03 51846 3 273 0
2022-12-02 51843 11 273 0
2022-12-01 51832 15 273 0
2022-11-30 51817 16 273 0
2022-11-29 51801 22 273 0
2022-11-28 51779 32 273 0
2022-11-27 51747 6 273 0
2022-11-26 51741 5 273 0
2022-11-25 51736 17 273 0
2022-11-24 51719 19 273 0
2022-11-23 51700 32 273 0
2022-11-22 51668 24 273 0
2022-11-21 51644 29 273 0
2022-11-20 51615 1 273 0
2022-11-19 51614 5 273 0
2022-11-18 51609 10 273 0
2022-11-17 51599 20 273 0
2022-11-16 51579 21 273 0
2022-11-15 51558 30 273 0
2022-11-14 51528 25 273 0
2022-11-13 51503 5 273 0
2022-11-12 51498 16 273 0
2022-11-11 51482 14 273 0
2022-11-10 51468 24 273 0
2022-11-09 51444 35 273 0
2022-11-08 51409 50 273 1
2022-11-07 51359 56 272 0
2022-11-06 51303 14 272 0
2022-11-05 51289 19 272 0
2022-11-04 51270 39 272 0
2022-11-03 51231 60 272 0
2022-11-02 51171 55 272 0
2022-11-01 51116 40 272 0
2022-10-31 51076 55 272 0
2022-10-30 51021 15 272 0
2022-10-29 51006 16 272 0
2022-10-28 50990 67 272 0
2022-10-27 50923 84 272 0
2022-10-26 50839 77 272 0
2022-10-25 50762 117 272 0
2022-10-24 50645 154 272 0
2022-10-23 50491 28 272 0
2022-10-22 50463 79 272 0
2022-10-21 50384 50 272 0
2022-10-20 50334 121 272 0
2022-10-19 50213 176 272 0
2022-10-18 50037 150 272 0
2022-10-17 49887 149 272 0
2022-10-16 49738 43 272 1
2022-10-15 49695 71 271 0
2022-10-14 49624 212 271 0
2022-10-13 49412 130 271 1
2022-10-12 49282 152 270 1
2022-10-11 49130 231 269 0
2022-10-10 48899 256 269 1
2022-10-09 48643 34 268 0
2022-10-08 48609 55 268 0
2022-10-07 48554 205 268 0
2022-10-06 48349 188 268 0
2022-10-05 48161 223 268 1
2022-10-04 47938 189 267 0
2022-10-03 47749 48 267 1
2022-10-02 47701 51 266 0
2022-10-01 47650 52 266 0
2022-09-30 47598 183 266 0
2022-09-29 47415 104 266 1
2022-09-28 47311 200 265 0
2022-09-27 47111 134 265 0
2022-09-26 46977 173 265 0
2022-09-25 46804 31 265 0
2022-09-24 46773 29 265 0
2022-09-23 46744 141 265 0
2022-09-22 46603 55 265 0
2022-09-21 46548 101 265 0
2022-09-20 46447 92 265 0
2022-09-19 46355 114 265 0
2022-09-18 46241 17 265 0
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2022-08-27 45143 31 264 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: 0:03:39.446519