Germany: SK Aschaffenburg (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 11:36:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Aschaffenburg", weeks=5);
2023-03-07T11:36:55.474073 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) 134.5 SK Aschaffenburg, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Aschaffenburg cases daily growth factor Germany-SK Aschaffenburg cases daily growth factor (rolling mean) Germany-SK Aschaffenburg 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-SK Aschaffenburg deaths daily growth factor Germany-SK Aschaffenburg deaths daily growth factor (rolling mean) Germany-SK Aschaffenburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-SK Aschaffenburg doubling time cases (rolling mean) 0.00 14.28 28.55 daily change Germany-SK Aschaffenburg new cases (rolling 7d mean) Germany-SK Aschaffenburg new cases 0.000 0.357 0.714 daily change Germany-SK Aschaffenburg new deaths (rolling 7d mean) Germany-SK Aschaffenburg new deaths 0.000 0.358 0.715
In [4]:
overview(country="Germany", subregion="SK Aschaffenburg");
2023-03-07T11:37:10.429847 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) 134.5 SK Aschaffenburg, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Aschaffenburg cases daily growth factor Germany-SK Aschaffenburg cases daily growth factor (rolling mean) Germany-SK Aschaffenburg 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-SK Aschaffenburg deaths daily growth factor Germany-SK Aschaffenburg deaths daily growth factor (rolling mean) Germany-SK Aschaffenburg 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 Aschaffenburg doubling time cases (rolling mean) Germany-SK Aschaffenburg doubling time deaths (rolling mean) 0.0 71.4 142.8 214.1 285.5 daily change Germany-SK Aschaffenburg new cases (rolling 7d mean) Germany-SK Aschaffenburg new cases 0.000 1.428 2.855 4.283 5.710 daily change Germany-SK Aschaffenburg new deaths (rolling 7d mean) Germany-SK Aschaffenburg new deaths 0.0 47.5 94.9 142.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Aschaffenburg", dates="2020-03-15:");
2023-03-07T11:39:17.480418 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 Aschaffenburg SK Aschaffenburg 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 Aschaffenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Aschaffenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Aschaffenburg")
print(f'Population of country="Germany", subregion="SK Aschaffenburg": {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 Aschaffenburg": 71381 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 35098 13 218 0
2023-03-03 35085 10 218 0
2023-03-02 35075 15 218 0
2023-03-01 35060 25 218 0
2023-02-28 35035 33 218 0
2023-02-27 35002 21 218 0
2023-02-24 34981 20 218 0
2023-02-23 34961 9 218 0
2023-02-22 34952 11 218 0
2023-02-21 34941 13 218 0
2023-02-20 34928 22 218 1
2023-02-17 34906 18 217 0
2023-02-16 34888 21 217 0
2023-02-15 34867 22 217 0
2023-02-14 34845 25 217 0
2023-02-13 34820 22 217 0
2023-02-10 34798 12 217 0
2023-02-09 34786 14 217 1
2023-02-08 34772 21 216 0
2023-02-07 34751 19 216 1
2023-02-06 34732 40 215 0
2023-02-03 34692 16 215 0
2023-02-02 34676 19 215 0
2023-02-01 34657 28 215 0
2023-01-31 34629 13 215 1
2023-01-30 34616 21 214 0
2023-01-27 34595 13 214 0
2023-01-26 34582 11 214 0
2023-01-25 34571 15 214 0
2023-01-24 34556 10 214 0
2023-01-23 34546 7 214 0
2023-01-20 34539 7 214 0
2023-01-19 34532 7 214 1
2023-01-18 34525 11 213 0
2023-01-17 34514 8 213 0
2023-01-16 34506 11 213 0
2023-01-13 34495 9 213 0
2023-01-12 34486 12 213 0
2023-01-11 34474 15 213 0
2023-01-10 34459 9 213 0
2023-01-09 34450 38 213 0
2023-01-05 34412 16 213 0
2023-01-04 34396 32 213 0
2023-01-03 34364 28 213 0
2023-01-02 34336 26 213 1
2022-12-30 34310 19 212 0
2022-12-29 34291 44 212 1
2022-12-28 34247 26 211 3
2022-12-27 34221 57 208 0
2022-12-23 34164 33 208 1
2022-12-22 34131 29 207 0
2022-12-21 34102 41 207 1
2022-12-20 34061 26 206 0
2022-12-19 34035 36 206 1
2022-12-18 33999 1 205 0
2022-12-16 33998 18 205 0
2022-12-15 33980 26 205 0
2022-12-14 33954 41 205 1
2022-12-13 33913 15 204 0
2022-12-12 33898 52 204 0
2022-12-09 33846 18 204 0
2022-12-08 33828 23 204 1
2022-12-07 33805 38 203 0
2022-12-06 33767 22 203 0
2022-12-05 33745 39 203 1
2022-12-02 33706 11 202 0
2022-12-01 33695 19 202 0
2022-11-30 33676 32 202 0
2022-11-29 33644 22 202 0
2022-11-28 33622 27 202 0
2022-11-25 33595 9 202 0
2022-11-24 33586 34 202 1
2022-11-23 33552 18 201 0
2022-11-22 33534 12 201 0
2022-11-21 33522 20 201 0
2022-11-18 33502 7 201 0
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2022-11-16 33470 15 201 0
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2022-11-14 33433 34 201 1
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2022-11-10 33364 37 200 0
2022-11-09 33327 57 200 0
2022-11-08 33270 30 200 0
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2022-11-05 33185 1 200 0
2022-11-04 33184 32 200 0
2022-11-03 33152 27 200 0
2022-11-02 33125 72 200 0
2022-10-31 33053 61 200 0
2022-10-29 32992 1 200 0
2022-10-28 32991 52 200 0
2022-10-27 32939 46 200 1
2022-10-26 32893 90 199 0
2022-10-25 32803 78 199 0
2022-10-24 32725 112 199 0
2022-10-21 32613 45 199 0
2022-10-20 32568 95 199 1
2022-10-19 32473 112 198 0
2022-10-18 32361 54 198 1
2022-10-17 32307 140 197 0
2022-10-15 32167 1 197 0
2022-10-14 32166 71 197 0
2022-10-13 32095 87 197 0
2022-10-12 32008 184 197 0
2022-10-11 31824 110 197 0
2022-10-10 31714 223 197 0
2022-10-07 31491 96 197 0
2022-10-06 31395 222 197 0
2022-10-05 31173 63 197 2
2022-10-04 31110 218 195 1
2022-10-02 30892 1 194 0
2022-10-01 30891 4 194 0
2022-09-30 30887 73 194 0
2022-09-29 30814 85 194 0
2022-09-28 30729 142 194 1
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2022-09-22 30386 57 193 1
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2022-09-19 30197 71 192 0
2022-09-16 30126 13 192 0
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2022-09-13 30004 47 192 0
2022-09-12 29957 64 192 0
2022-09-10 29893 1 192 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: 0:02:45.717759