Germany: LK Ansbach (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 10:25:40 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ansbach", weeks=5);
2023-03-07T10:25:55.096653 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 7-day incidence rate (per 100K people) 88.0 LK Ansbach, 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 Ansbach cases daily growth factor Germany-LK Ansbach cases daily growth factor (rolling mean) Germany-LK Ansbach 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 Ansbach deaths daily growth factor Germany-LK Ansbach deaths daily growth factor (rolling mean) Germany-LK Ansbach 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 Ansbach doubling time cases (rolling mean) 0.00 18.63 37.26 55.88 74.51 daily change Germany-LK Ansbach new cases (rolling 7d mean) Germany-LK Ansbach new cases 0.000 0.373 0.745 daily change Germany-LK Ansbach new deaths (rolling 7d mean) Germany-LK Ansbach new deaths 0.00 0.22 0.44 0.66 0.88
In [4]:
overview(country="Germany", subregion="LK Ansbach");
2023-03-07T10:26:12.112704 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) 88.0 LK Ansbach, 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 1 2 3 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 Ansbach cases daily growth factor Germany-LK Ansbach cases daily growth factor (rolling mean) Germany-LK Ansbach 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 Ansbach deaths daily growth factor Germany-LK Ansbach deaths daily growth factor (rolling mean) Germany-LK Ansbach 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 Ansbach doubling time cases (rolling mean) Germany-LK Ansbach doubling time deaths (rolling mean) 0.0 186.3 372.6 558.8 745.1 daily change Germany-LK Ansbach new cases (rolling 7d mean) Germany-LK Ansbach new cases 0.000 1.863 3.726 5.588 daily change Germany-LK Ansbach new deaths (rolling 7d mean) Germany-LK Ansbach new deaths 0.0 70.6 141.2 211.8 282.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ansbach", dates="2020-03-15:");
2023-03-07T10:28:18.980099 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 Ansbach LK Ansbach 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Ansbach Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ansbach")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ansbach")
print(f'Population of country="Germany", subregion="LK Ansbach": {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 Ansbach": 186279 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 96576 20 298 0
2023-03-04 96556 2 298 0
2023-03-03 96554 14 298 0
2023-03-02 96540 18 298 0
2023-03-01 96522 82 298 0
2023-02-28 96440 28 298 0
2023-02-27 96412 53 298 0
2023-02-26 96359 2 298 0
2023-02-25 96357 18 298 0
2023-02-24 96339 19 298 0
2023-02-23 96320 45 298 0
2023-02-22 96275 71 298 0
2023-02-21 96204 40 298 0
2023-02-20 96164 48 298 0
2023-02-18 96116 7 298 0
2023-02-17 96109 53 298 1
2023-02-16 96056 33 297 0
2023-02-15 96023 33 297 0
2023-02-14 95990 41 297 0
2023-02-13 95949 23 297 0
2023-02-11 95926 10 297 0
2023-02-10 95916 21 297 0
2023-02-09 95895 35 297 0
2023-02-08 95860 43 297 0
2023-02-07 95817 41 297 0
2023-02-06 95776 47 297 0
2023-02-05 95729 1 297 0
2023-02-04 95728 3 297 0
2023-02-03 95725 23 297 0
2023-02-02 95702 39 297 0
2023-02-01 95663 30 297 0
2023-01-31 95633 42 297 0
2023-01-30 95591 40 297 0
2023-01-29 95551 1 297 0
2023-01-28 95550 2 297 0
2023-01-27 95548 24 297 0
2023-01-26 95524 29 297 0
2023-01-25 95495 24 297 0
2023-01-24 95471 26 297 0
2023-01-23 95445 27 297 0
2023-01-21 95418 1 297 0
2023-01-20 95417 10 297 0
2023-01-19 95407 11 297 0
2023-01-18 95396 21 297 0
2023-01-17 95375 13 297 0
2023-01-16 95362 18 297 0
2023-01-14 95344 6 297 0
2023-01-13 95338 19 297 0
2023-01-12 95319 11 297 0
2023-01-11 95308 18 297 0
2023-01-10 95290 25 297 1
2023-01-09 95265 49 296 0
2023-01-05 95216 28 296 0
2023-01-04 95188 29 296 0
2023-01-03 95159 26 296 1
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2022-12-27 94957 57 295 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: 0:02:56.200239