Germany: SK Passau (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 12:06:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Passau", weeks=5);
2023-03-07T12:07:04.408539 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 105.5 SK Passau, 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 1.5 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-SK Passau cases daily growth factor Germany-SK Passau cases daily growth factor (rolling mean) Germany-SK Passau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Passau deaths daily growth factor Germany-SK Passau deaths daily growth factor (rolling mean) Germany-SK Passau estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-SK Passau doubling time cases (rolling mean) 0.00 10.62 21.24 daily change Germany-SK Passau new cases (rolling 7d mean) Germany-SK Passau new cases 0.000 0.265 0.531 0.796 daily change Germany-SK Passau new deaths (rolling 7d mean) Germany-SK Passau new deaths 0.000 0.282 0.563 0.845
In [4]:
overview(country="Germany", subregion="SK Passau");
2023-03-07T12:07:26.887827 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) 105.5 SK Passau, 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 5 10 15 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-SK Passau cases daily growth factor Germany-SK Passau cases daily growth factor (rolling mean) Germany-SK Passau estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Passau deaths daily growth factor Germany-SK Passau deaths daily growth factor (rolling mean) Germany-SK Passau 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 cases doubling time [days] Germany-SK Passau doubling time cases (rolling mean) Germany-SK Passau doubling time deaths (rolling mean) 0.0 106.2 212.4 318.6 daily change Germany-SK Passau new cases (rolling 7d mean) Germany-SK Passau new cases 0.00 2.65 5.31 7.96 daily change Germany-SK Passau new deaths (rolling 7d mean) Germany-SK Passau new deaths 0.0 37.2 74.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Passau", dates="2020-03-15:");
2023-03-07T12:09:36.078987 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 Passau SK Passau 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Passau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Passau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Passau")
print(f'Population of country="Germany", subregion="SK Passau": {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 Passau": 53093 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 26331 1 167 0
2023-03-05 26330 2 167 0
2023-03-04 26328 1 167 0
2023-03-03 26327 12 167 0
2023-03-02 26315 6 167 0
2023-03-01 26309 13 167 0
2023-02-28 26296 21 167 0
2023-02-27 26275 14 167 0
2023-02-26 26261 1 167 0
2023-02-25 26260 3 167 0
2023-02-24 26257 11 167 0
2023-02-23 26246 13 167 0
2023-02-22 26233 10 167 0
2023-02-21 26223 13 167 0
2023-02-20 26210 6 167 0
2023-02-19 26204 3 167 0
2023-02-18 26201 9 167 0
2023-02-17 26192 8 167 0
2023-02-16 26184 17 167 0
2023-02-15 26167 16 167 0
2023-02-14 26151 22 167 0
2023-02-13 26129 8 167 0
2023-02-12 26121 4 167 0
2023-02-11 26117 1 167 0
2023-02-10 26116 14 167 0
2023-02-09 26102 6 167 0
2023-02-08 26096 28 167 0
2023-02-07 26068 25 167 0
2023-02-06 26043 14 167 0
2023-02-05 26029 3 167 0
2023-02-04 26026 6 167 0
2023-02-03 26020 9 167 0
2023-02-02 26011 14 167 1
2023-02-01 25997 23 166 0
2023-01-31 25974 14 166 0
2023-01-30 25960 15 166 0
2023-01-29 25945 4 166 0
2023-01-28 25941 7 166 0
2023-01-27 25934 10 166 0
2023-01-26 25924 20 166 0
2023-01-25 25904 19 166 0
2023-01-24 25885 27 166 0
2023-01-23 25858 4 166 0
2023-01-22 25854 3 166 0
2023-01-21 25851 1 166 0
2023-01-20 25850 11 166 0
2023-01-19 25839 7 166 0
2023-01-18 25832 7 166 0
2023-01-17 25825 13 166 0
2023-01-16 25812 5 166 0
2023-01-15 25807 3 166 0
2023-01-14 25804 4 166 0
2023-01-13 25800 9 166 0
2023-01-12 25791 4 166 0
2023-01-11 25787 6 166 0
2023-01-10 25781 8 166 0
2023-01-09 25773 12 166 0
2023-01-08 25761 6 166 0
2023-01-07 25755 1 166 0
2023-01-06 25754 5 166 0
2023-01-05 25749 4 166 0
2023-01-04 25745 2 166 0
2023-01-03 25743 9 166 0
2023-01-02 25734 7 166 0
2023-01-01 25727 2 166 0
2022-12-31 25725 6 166 0
2022-12-30 25719 6 166 0
2022-12-29 25713 9 166 0
2022-12-28 25704 20 166 0
2022-12-27 25684 10 166 0
2022-12-26 25674 1 166 0
2022-12-25 25673 5 166 0
2022-12-23 25668 12 166 0
2022-12-22 25656 8 166 0
2022-12-21 25648 14 166 0
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2022-12-04 25497 5 166 0
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2022-12-02 25489 5 166 0
2022-12-01 25484 7 166 0
2022-11-30 25477 13 166 0
2022-11-29 25464 14 166 0
2022-11-28 25450 8 166 0
2022-11-27 25442 2 166 0
2022-11-26 25440 8 166 0
2022-11-25 25432 16 166 0
2022-11-24 25416 4 166 0
2022-11-23 25412 20 166 0
2022-11-22 25392 19 166 0
2022-11-21 25373 3 166 0
2022-11-20 25370 1 166 0
2022-11-19 25369 1 166 0
2022-11-18 25368 11 166 0
2022-11-17 25357 6 166 0
2022-11-16 25351 21 166 0
2022-11-15 25330 24 166 0
2022-11-14 25306 9 166 0
2022-11-13 25297 3 166 0
2022-11-12 25294 4 166 0
2022-11-11 25290 20 166 0
2022-11-10 25270 23 166 0
2022-11-09 25247 17 166 0
2022-11-08 25230 31 166 0
2022-11-07 25199 15 166 0
2022-11-06 25184 7 166 0
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2022-11-04 25162 32 166 0
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2022-10-28 25057 34 166 0
2022-10-27 25023 19 166 0
2022-10-26 25004 48 166 0
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2022-10-24 24905 59 166 3
2022-10-23 24846 9 163 0
2022-10-22 24837 7 163 0
2022-10-21 24830 11 163 0
2022-10-20 24819 78 163 1
2022-10-19 24741 52 162 0
2022-10-18 24689 81 162 0
2022-10-17 24608 34 162 0
2022-10-16 24574 8 162 0
2022-10-15 24566 29 162 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: 0:03:05.690501