Germany: LK Bamberg (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 10:33:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bamberg", weeks=5);
2023-03-07T10:33:22.892715 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) 96.8 LK Bamberg, 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 0.2 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 Bamberg cases daily growth factor Germany-LK Bamberg cases daily growth factor (rolling mean) Germany-LK Bamberg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Bamberg deaths daily growth factor Germany-LK Bamberg deaths daily growth factor (rolling mean) Germany-LK Bamberg 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 Bamberg doubling time cases (rolling mean) 0.00 29.54 59.08 daily change Germany-LK Bamberg new cases (rolling 7d mean) Germany-LK Bamberg new cases 0.000 0.295 0.591 0.886 daily change Germany-LK Bamberg new deaths (rolling 7d mean) Germany-LK Bamberg new deaths 0.000 0.207 0.414 0.620 0.827
In [4]:
overview(country="Germany", subregion="LK Bamberg");
2023-03-07T10:33:39.802176 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) 96.8 LK Bamberg, 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 0 500 1000 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 Bamberg cases daily growth factor Germany-LK Bamberg cases daily growth factor (rolling mean) Germany-LK Bamberg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Bamberg deaths daily growth factor Germany-LK Bamberg deaths daily growth factor (rolling mean) Germany-LK Bamberg 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 Bamberg doubling time cases (rolling mean) Germany-LK Bamberg doubling time deaths (rolling mean) 0 738 1477 daily change Germany-LK Bamberg new cases (rolling 7d mean) Germany-LK Bamberg new cases 0.000 1.477 2.954 4.431 5.908 daily change Germany-LK Bamberg new deaths (rolling 7d mean) Germany-LK Bamberg new deaths 0.0 63.6 127.1 190.7 254.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bamberg", dates="2020-03-15:");
2023-03-07T10:36:48.970587 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 Bamberg LK Bamberg 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 Bamberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bamberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bamberg")
print(f'Population of country="Germany", subregion="LK Bamberg": {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 Bamberg": 147697 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 80885 19 288 0
2023-03-03 80866 6 288 0
2023-03-02 80860 12 288 0
2023-03-01 80848 31 288 0
2023-02-28 80817 75 288 1
2023-02-27 80742 74 287 0
2023-02-24 80668 23 287 0
2023-02-23 80645 30 287 0
2023-02-22 80615 52 287 1
2023-02-21 80563 32 286 0
2023-02-20 80531 54 286 0
2023-02-17 80477 24 286 0
2023-02-16 80453 29 286 0
2023-02-15 80424 25 286 0
2023-02-14 80399 41 286 0
2023-02-13 80358 52 286 0
2023-02-10 80306 26 286 0
2023-02-09 80280 27 286 0
2023-02-08 80253 27 286 0
2023-02-07 80226 78 286 0
2023-02-06 80148 45 286 0
2023-02-03 80103 22 286 0
2023-02-02 80081 27 286 0
2023-02-01 80054 40 286 0
2023-01-31 80014 32 286 0
2023-01-30 79982 31 286 0
2023-01-29 79951 1 286 0
2023-01-27 79950 16 286 0
2023-01-26 79934 22 286 0
2023-01-25 79912 25 286 0
2023-01-24 79887 45 286 1
2023-01-23 79842 35 285 0
2023-01-20 79807 17 285 0
2023-01-19 79790 15 285 0
2023-01-18 79775 23 285 0
2023-01-17 79752 31 285 0
2023-01-16 79721 26 285 0
2023-01-13 79695 16 285 0
2023-01-12 79679 20 285 0
2023-01-11 79659 27 285 0
2023-01-10 79632 39 285 0
2023-01-09 79593 43 285 0
2023-01-07 79550 1 285 0
2023-01-05 79549 17 285 0
2023-01-04 79532 17 285 0
2023-01-03 79515 26 285 0
2023-01-02 79489 31 285 0
2022-12-30 79458 12 285 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: 0:03:51.327891