Germany: SK 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 11:36:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Bamberg", weeks=5);
2023-03-07T11:36:45.605380 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 7-day incidence rate (per 100K people) 68.2 SK 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 10 20 30 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Bamberg cases daily growth factor Germany-SK Bamberg cases daily growth factor (rolling mean) Germany-SK Bamberg 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 Bamberg deaths daily growth factor Germany-SK Bamberg deaths daily growth factor (rolling mean) Germany-SK Bamberg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Bamberg doubling time cases (rolling mean) 0.00 7.77 15.55 23.32 31.10 daily change Germany-SK Bamberg new cases (rolling 7d mean) Germany-SK Bamberg new cases 0.000 0.389 0.777 daily change Germany-SK Bamberg new deaths (rolling 7d mean) Germany-SK Bamberg new deaths 0.000 0.381 0.763
In [4]:
overview(country="Germany", subregion="SK Bamberg");
2023-03-07T11:37:02.114905 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) 68.2 SK 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 0 200 400 600 800 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5 10 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-SK Bamberg cases daily growth factor Germany-SK Bamberg cases daily growth factor (rolling mean) Germany-SK Bamberg 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 Bamberg deaths daily growth factor Germany-SK Bamberg deaths daily growth factor (rolling mean) Germany-SK Bamberg 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 Bamberg doubling time cases (rolling mean) Germany-SK Bamberg doubling time deaths (rolling mean) 0.0 155.5 311.0 466.5 622.0 daily change Germany-SK Bamberg new cases (rolling 7d mean) Germany-SK Bamberg new cases 0.00 3.89 7.77 daily change Germany-SK Bamberg new deaths (rolling 7d mean) Germany-SK Bamberg new deaths 0.0 32.9 65.8 98.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Bamberg", dates="2020-03-15:");
2023-03-07T11:39:06.720572 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 Bamberg SK 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 SK Bamberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Bamberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Bamberg")
print(f'Population of country="Germany", subregion="SK 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="SK Bamberg": 77749 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 33751 4 183 0
2023-03-03 33747 2 183 0
2023-03-02 33745 8 183 0
2023-03-01 33737 13 183 0
2023-02-28 33724 26 183 0
2023-02-27 33698 13 183 0
2023-02-24 33685 11 183 0
2023-02-23 33674 12 183 0
2023-02-22 33662 17 183 0
2023-02-21 33645 10 183 0
2023-02-20 33635 12 183 0
2023-02-17 33623 7 183 0
2023-02-16 33616 19 183 0
2023-02-15 33597 18 183 1
2023-02-14 33579 32 182 0
2023-02-13 33547 16 182 0
2023-02-10 33531 15 182 0
2023-02-09 33516 13 182 0
2023-02-08 33503 13 182 0
2023-02-07 33490 27 182 0
2023-02-06 33463 17 182 0
2023-02-03 33446 5 182 0
2023-02-02 33441 10 182 0
2023-02-01 33431 16 182 0
2023-01-31 33415 17 182 0
2023-01-30 33398 16 182 1
2023-01-27 33382 8 181 0
2023-01-26 33374 16 181 0
2023-01-25 33358 13 181 0
2023-01-24 33345 13 181 0
2023-01-23 33332 12 181 0
2023-01-20 33320 7 181 0
2023-01-19 33313 4 181 0
2023-01-18 33309 7 181 0
2023-01-17 33302 23 181 0
2023-01-16 33279 12 181 1
2023-01-13 33267 8 180 0
2023-01-12 33259 9 180 0
2023-01-11 33250 11 180 0
2023-01-10 33239 15 180 0
2023-01-09 33224 12 180 0
2023-01-05 33212 4 180 0
2023-01-04 33208 7 180 0
2023-01-03 33201 12 180 0
2023-01-02 33189 20 180 0
2022-12-30 33169 4 180 0
2022-12-29 33165 18 180 0
2022-12-28 33147 18 180 0
2022-12-27 33129 28 180 0
2022-12-23 33101 24 180 0
2022-12-22 33077 13 180 0
2022-12-21 33064 16 180 1
2022-12-20 33048 17 179 0
2022-12-19 33031 20 179 0
2022-12-16 33011 14 179 0
2022-12-15 32997 15 179 0
2022-12-14 32982 18 179 0
2022-12-13 32964 25 179 0
2022-12-12 32939 18 179 0
2022-12-09 32921 13 179 0
2022-12-08 32908 15 179 0
2022-12-07 32893 15 179 0
2022-12-06 32878 13 179 0
2022-12-05 32865 21 179 1
2022-12-02 32844 12 178 0
2022-12-01 32832 6 178 0
2022-11-30 32826 16 178 0
2022-11-29 32810 25 178 0
2022-11-28 32785 24 178 1
2022-11-25 32761 12 177 0
2022-11-24 32749 17 177 0
2022-11-23 32732 14 177 0
2022-11-22 32718 11 177 0
2022-11-21 32707 24 177 0
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2022-10-25 32179 77 176 1
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2022-10-19 31913 105 172 0
2022-10-18 31808 119 172 0
2022-10-17 31689 107 172 0
2022-10-14 31582 79 172 1
2022-10-13 31503 78 171 1
2022-10-12 31425 156 170 0
2022-10-11 31269 182 170 0
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2022-10-07 30892 92 170 0
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2022-10-05 30704 156 170 1
2022-10-04 30548 127 169 0
2022-09-30 30421 78 169 0
2022-09-29 30343 74 169 1
2022-09-28 30269 60 168 0
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2022-09-26 30129 89 167 2
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2020-12-05 1003 41 53 9
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2020-11-14 679 34 35 3
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2020-03-31 83 8 6 3
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2020-03-28 68 2 2 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: 0:02:46.757399