Germany: LK Aichach-Friedberg (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 10:25:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Aichach-Friedberg", weeks=5);
2023-03-07T10:26:06.054388 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 53.1 LK Aichach-Friedberg, 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 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-LK Aichach-Friedberg cases daily growth factor Germany-LK Aichach-Friedberg cases daily growth factor (rolling mean) Germany-LK Aichach-Friedberg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 R & growth factor (based on deaths) Germany-LK Aichach-Friedberg deaths daily growth factor Germany-LK Aichach-Friedberg deaths daily growth factor (rolling mean) Germany-LK Aichach-Friedberg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Aichach-Friedberg doubling time cases (rolling mean) 0.00 13.55 27.11 40.66 daily change Germany-LK Aichach-Friedberg new cases (rolling 7d mean) Germany-LK Aichach-Friedberg new cases 0.000 0.271 0.542 0.813 daily change Germany-LK Aichach-Friedberg new deaths (rolling 7d mean) Germany-LK Aichach-Friedberg new deaths 0.000 0.260 0.521 0.781
In [4]:
overview(country="Germany", subregion="LK Aichach-Friedberg");
2023-03-07T10:26:22.504282 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) 53.1 LK Aichach-Friedberg, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Aichach-Friedberg cases daily growth factor Germany-LK Aichach-Friedberg cases daily growth factor (rolling mean) Germany-LK Aichach-Friedberg 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 R & growth factor (based on deaths) Germany-LK Aichach-Friedberg deaths daily growth factor Germany-LK Aichach-Friedberg deaths daily growth factor (rolling mean) Germany-LK Aichach-Friedberg 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 Aichach-Friedberg doubling time cases (rolling mean) Germany-LK Aichach-Friedberg doubling time deaths (rolling mean) 0 271 542 813 daily change Germany-LK Aichach-Friedberg new cases (rolling 7d mean) Germany-LK Aichach-Friedberg new cases 0.000 1.355 2.711 4.066 5.422 daily change Germany-LK Aichach-Friedberg new deaths (rolling 7d mean) Germany-LK Aichach-Friedberg new deaths 0.0 27.5 54.9 82.4 109.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Aichach-Friedberg", dates="2020-03-15:");
2023-03-07T10:28:33.824202 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 Aichach-Friedberg LK Aichach-Friedberg 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 Aichach-Friedberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Aichach-Friedberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Aichach-Friedberg")
print(f'Population of country="Germany", subregion="LK Aichach-Friedberg": {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 Aichach-Friedberg": 135538 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 68181 12 183 0
2023-03-03 68169 4 183 0
2023-03-02 68165 7 183 0
2023-03-01 68158 26 183 0
2023-02-28 68132 23 183 0
2023-02-27 68109 35 183 0
2023-02-24 68074 15 183 1
2023-02-23 68059 22 182 0
2023-02-22 68037 36 182 0
2023-02-21 68001 19 182 0
2023-02-20 67982 22 182 1
2023-02-17 67960 20 181 0
2023-02-16 67940 15 181 0
2023-02-15 67925 33 181 0
2023-02-14 67892 16 181 0
2023-02-13 67876 54 181 0
2023-02-10 67822 16 181 0
2023-02-09 67806 18 181 0
2023-02-08 67788 24 181 0
2023-02-07 67764 33 181 0
2023-02-06 67731 37 181 0
2023-02-03 67694 16 181 0
2023-02-02 67678 23 181 0
2023-02-01 67655 20 181 0
2023-01-31 67635 30 181 0
2023-01-30 67605 32 181 0
2023-01-27 67573 14 181 0
2023-01-26 67559 10 181 0
2023-01-25 67549 24 181 0
2023-01-24 67525 20 181 0
2023-01-23 67505 19 181 0
2023-01-20 67486 12 181 0
2023-01-19 67474 16 181 0
2023-01-18 67458 15 181 0
2023-01-17 67443 10 181 0
2023-01-16 67433 11 181 0
2023-01-13 67422 7 181 0
2023-01-12 67415 19 181 0
2023-01-11 67396 6 181 0
2023-01-10 67390 18 181 0
2023-01-09 67372 50 181 0
2023-01-05 67322 17 181 0
2023-01-04 67305 32 181 0
2023-01-03 67273 36 181 0
2023-01-02 67237 36 181 0
2022-12-30 67201 19 181 0
2022-12-29 67182 23 181 0
2022-12-28 67159 36 181 0
2022-12-27 67123 55 181 0
2022-12-23 67068 23 181 0
2022-12-22 67045 26 181 0
2022-12-21 67019 34 181 0
2022-12-20 66985 17 181 0
2022-12-19 66968 52 181 1
2022-12-16 66916 25 180 0
2022-12-15 66891 25 180 0
2022-12-14 66866 45 180 0
2022-12-13 66821 33 180 0
2022-12-12 66788 48 180 0
2022-12-11 66740 1 180 0
2022-12-10 66739 1 180 0
2022-12-09 66738 9 180 0
2022-12-08 66729 26 180 0
2022-12-07 66703 28 180 0
2022-12-06 66675 36 180 0
2022-12-05 66639 35 180 0
2022-12-02 66604 35 180 0
2022-12-01 66569 14 180 0
2022-11-30 66555 25 180 0
2022-11-29 66530 29 180 0
2022-11-28 66501 40 180 0
2022-11-25 66461 28 180 0
2022-11-24 66433 27 180 0
2022-11-23 66406 26 180 1
2022-11-22 66380 42 179 0
2022-11-21 66338 73 179 0
2022-11-18 66265 28 179 0
2022-11-17 66237 25 179 0
2022-11-16 66212 50 179 0
2022-11-15 66162 52 179 0
2022-11-14 66110 47 179 0
2022-11-11 66063 15 179 0
2022-11-10 66048 26 179 0
2022-11-09 66022 42 179 0
2022-11-08 65980 70 179 0
2022-11-07 65910 106 179 1
2022-11-04 65804 46 178 1
2022-11-03 65758 75 177 0
2022-11-02 65683 83 177 0
2022-10-31 65600 125 177 0
2022-10-28 65475 58 177 0
2022-10-27 65417 75 177 1
2022-10-26 65342 112 176 1
2022-10-25 65230 122 175 0
2022-10-24 65108 198 175 0
2022-10-21 64910 130 175 0
2022-10-20 64780 112 175 0
2022-10-19 64668 186 175 1
2022-10-18 64482 197 174 2
2022-10-17 64285 334 172 0
2022-10-16 63951 3 172 0
2022-10-15 63948 2 172 0
2022-10-14 63946 184 172 0
2022-10-13 63762 209 172 0
2022-10-12 63553 230 172 0
2022-10-11 63323 298 172 0
2022-10-10 63025 523 172 0
2022-10-09 62502 14 172 0
2022-10-08 62488 2 172 0
2022-10-07 62486 240 172 0
2022-10-06 62246 281 172 1
2022-10-05 61965 340 171 0
2022-10-04 61625 501 171 0
2022-10-03 61124 4 171 0
2022-10-02 61120 1 171 0
2022-09-30 61119 142 171 0
2022-09-29 60977 183 171 0
2022-09-28 60794 221 171 0
2022-09-27 60573 165 171 0
2022-09-26 60408 209 171 0
2022-09-24 60199 10 171 0
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2022-09-22 60114 94 171 0
2022-09-21 60020 82 171 0
2022-09-20 59938 77 171 0
2022-09-19 59861 120 171 0
2022-09-18 59741 1 171 0
2022-09-16 59740 48 171 0
2022-09-15 59692 58 171 0
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2022-09-03 59125 1 171 0
2022-09-02 59124 42 171 0
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2022-08-22 58584 144 171 0
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2022-08-18 58373 57 171 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: 0:03:14.346333