Germany: LK Unterallgäu (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 11:29:26 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Unterallgäu", weeks=5);
2023-03-07T11:29:40.044288 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 40.6 LK Unterallgäu, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Unterallgäu cases daily growth factor Germany-LK Unterallgäu cases daily growth factor (rolling mean) Germany-LK Unterallgäu estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Unterallgäu deaths daily growth factor Germany-LK Unterallgäu deaths daily growth factor (rolling mean) Germany-LK Unterallgäu estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Unterallgäu doubling time cases (rolling mean) 0.00 14.78 29.56 daily change Germany-LK Unterallgäu new cases (rolling 7d mean) Germany-LK Unterallgäu new cases 0.000 0.296 0.591 0.887 daily change Germany-LK Unterallgäu new deaths (rolling 7d mean) Germany-LK Unterallgäu new deaths 0.000 0.214 0.427 0.641 0.854
In [4]:
overview(country="Germany", subregion="LK Unterallgäu");
2023-03-07T11:29:57.181236 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) 40.6 LK Unterallgäu, 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 1 2 3 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Unterallgäu cases daily growth factor Germany-LK Unterallgäu cases daily growth factor (rolling mean) Germany-LK Unterallgäu estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Unterallgäu deaths daily growth factor Germany-LK Unterallgäu deaths daily growth factor (rolling mean) Germany-LK Unterallgäu 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 Unterallgäu doubling time cases (rolling mean) Germany-LK Unterallgäu doubling time deaths (rolling mean) 0.0 295.6 591.1 daily change Germany-LK Unterallgäu new cases (rolling 7d mean) Germany-LK Unterallgäu new cases 0.000 1.478 2.956 4.433 5.911 daily change Germany-LK Unterallgäu new deaths (rolling 7d mean) Germany-LK Unterallgäu new deaths 0.0 52.2 104.3 156.5 208.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Unterallgäu", dates="2020-03-15:");
2023-03-07T11:32:21.578333 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 Unterallgäu LK Unterallgäu 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 Unterallgäu Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Unterallgäu")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Unterallgäu")
print(f'Population of country="Germany", subregion="LK Unterallgäu": {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 Unterallgäu": 147776 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 74016 7 280 0
2023-03-05 74009 2 280 0
2023-03-04 74007 3 280 0
2023-03-03 74004 11 280 0
2023-03-02 73993 6 280 0
2023-03-01 73987 14 280 0
2023-02-28 73973 17 280 0
2023-02-27 73956 29 280 0
2023-02-26 73927 9 280 0
2023-02-25 73918 13 280 0
2023-02-24 73905 25 280 0
2023-02-23 73880 25 280 0
2023-02-22 73855 28 280 0
2023-02-21 73827 43 280 0
2023-02-20 73784 27 280 0
2023-02-19 73757 5 280 0
2023-02-18 73752 3 280 0
2023-02-17 73749 22 280 0
2023-02-16 73727 14 280 0
2023-02-15 73713 21 280 0
2023-02-14 73692 26 280 0
2023-02-13 73666 29 280 0
2023-02-12 73637 4 280 0
2023-02-11 73633 4 280 0
2023-02-10 73629 15 280 0
2023-02-09 73614 23 280 0
2023-02-08 73591 26 280 1
2023-02-07 73565 37 279 0
2023-02-06 73528 31 279 0
2023-02-05 73497 7 279 0
2023-02-04 73490 4 279 0
2023-02-03 73486 18 279 0
2023-02-02 73468 16 279 0
2023-02-01 73452 26 279 0
2023-01-31 73426 20 279 0
2023-01-30 73406 17 279 0
2023-01-29 73389 8 279 0
2023-01-28 73381 7 279 0
2023-01-27 73374 16 279 0
2023-01-26 73358 19 279 0
2023-01-25 73339 13 279 0
2023-01-24 73326 22 279 0
2023-01-23 73304 18 279 0
2023-01-22 73286 1 279 0
2023-01-21 73285 3 279 0
2023-01-20 73282 13 279 0
2023-01-19 73269 11 279 0
2023-01-18 73258 15 279 0
2023-01-17 73243 22 279 0
2023-01-16 73221 10 279 0
2023-01-14 73211 2 279 0
2023-01-13 73209 9 279 1
2023-01-12 73200 6 278 0
2023-01-11 73194 9 278 0
2023-01-10 73185 16 278 0
2023-01-09 73169 14 278 0
2023-01-08 73155 6 278 0
2023-01-07 73149 6 278 0
2023-01-06 73143 3 278 0
2023-01-05 73140 14 278 0
2023-01-04 73126 13 278 0
2023-01-03 73113 22 278 0
2023-01-02 73091 17 278 0
2023-01-01 73074 2 278 0
2022-12-31 73072 2 278 0
2022-12-30 73070 24 278 1
2022-12-29 73046 18 277 0
2022-12-28 73028 25 277 0
2022-12-27 73003 14 277 2
2022-12-26 72989 3 275 0
2022-12-25 72986 7 275 0
2022-12-24 72979 4 275 0
2022-12-23 72975 17 275 0
2022-12-22 72958 17 275 0
2022-12-21 72941 34 275 0
2022-12-20 72907 29 275 1
2022-12-19 72878 23 274 0
2022-12-18 72855 6 274 1
2022-12-17 72849 6 273 0
2022-12-16 72843 14 273 1
2022-12-15 72829 20 272 0
2022-12-14 72809 37 272 0
2022-12-13 72772 20 272 0
2022-12-12 72752 26 272 0
2022-12-11 72726 3 272 0
2022-12-10 72723 6 272 0
2022-12-09 72717 13 272 1
2022-12-08 72704 19 271 0
2022-12-07 72685 21 271 0
2022-12-06 72664 32 271 0
2022-12-05 72632 16 271 1
2022-12-04 72616 4 270 0
2022-12-03 72612 7 270 0
2022-12-02 72605 11 270 0
2022-12-01 72594 21 270 0
2022-11-30 72573 15 270 0
2022-11-29 72558 34 270 0
2022-11-28 72524 13 270 0
2022-11-26 72511 7 270 0
2022-11-25 72504 11 270 0
2022-11-24 72493 16 270 0
2022-11-23 72477 16 270 0
2022-11-22 72461 26 270 0
2022-11-21 72435 29 270 0
2022-11-20 72406 8 270 0
2022-11-19 72398 8 270 0
2022-11-18 72390 10 270 0
2022-11-17 72380 16 270 0
2022-11-16 72364 20 270 0
2022-11-15 72344 36 270 1
2022-11-14 72308 30 269 0
2022-11-13 72278 3 269 0
2022-11-12 72275 9 269 0
2022-11-11 72266 31 269 0
2022-11-10 72235 31 269 0
2022-11-09 72204 39 269 0
2022-11-08 72165 50 269 0
2022-11-07 72115 41 269 0
2022-11-06 72074 8 269 0
2022-11-05 72066 19 269 0
2022-11-04 72047 24 269 0
2022-11-03 72023 56 269 0
2022-11-02 71967 62 269 0
2022-11-01 71905 13 269 0
2022-10-31 71892 66 269 0
2022-10-30 71826 1 269 0
2022-10-29 71825 24 269 0
2022-10-28 71801 59 269 0
2022-10-27 71742 67 269 1
2022-10-26 71675 84 268 0
2022-10-25 71591 148 268 0
2022-10-24 71443 166 268 0
2022-10-23 71277 7 268 0
2022-10-22 71270 17 268 0
2022-10-21 71253 56 268 0
2022-10-20 71197 170 268 0
2022-10-19 71027 98 268 0
2022-10-18 70929 182 268 1
2022-10-17 70747 333 267 2
2022-10-14 70414 104 265 0
2022-10-13 70310 199 265 1
2022-10-12 70111 210 264 0
2022-10-11 69901 313 264 0
2022-10-10 69588 191 264 0
2022-10-09 69397 75 264 0
2022-10-08 69322 97 264 0
2022-10-07 69225 225 264 0
2022-10-06 69000 326 264 1
2022-10-05 68674 449 263 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: 0:03:06.800138