Germany: LK Erding (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 10:48:00 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Erding", weeks=5);
2023-03-07T10:48:15.169736 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 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 49.4 LK Erding, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Erding new deaths (rolling 7d mean) Germany-LK Erding new deaths 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-LK Erding cases daily growth factor Germany-LK Erding cases daily growth factor (rolling mean) Germany-LK Erding estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Erding deaths daily growth factor Germany-LK Erding deaths daily growth factor (rolling mean) Germany-LK Erding 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 Erding doubling time cases (rolling mean) 0.00 27.92 55.85 daily change Germany-LK Erding new cases (rolling 7d mean) Germany-LK Erding new cases 0.000 0.287 0.574 0.861
In [4]:
overview(country="Germany", subregion="LK Erding");
2023-03-07T10:48:30.976978 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) 49.4 LK Erding, 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 2 4 6 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-LK Erding cases daily growth factor Germany-LK Erding cases daily growth factor (rolling mean) Germany-LK Erding estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Erding deaths daily growth factor Germany-LK Erding deaths daily growth factor (rolling mean) Germany-LK Erding 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-LK Erding doubling time cases (rolling mean) Germany-LK Erding doubling time deaths (rolling mean) 0 279 558 838 daily change Germany-LK Erding new cases (rolling 7d mean) Germany-LK Erding new cases 0.00 2.79 5.58 8.38 daily change Germany-LK Erding new deaths (rolling 7d mean) Germany-LK Erding new deaths 0.0 57.2 114.4 171.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Erding", dates="2020-03-15:");
2023-03-07T10:50:48.668194 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 Erding LK Erding 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 Erding Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Erding")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Erding")
print(f'Population of country="Germany", subregion="LK Erding": {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 Erding": 139622 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 77027 17 226 0
2023-03-03 77010 8 226 0
2023-03-02 77002 11 226 0
2023-03-01 76991 21 226 0
2023-02-28 76970 12 226 0
2023-02-27 76958 45 226 0
2023-02-24 76913 22 226 0
2023-02-23 76891 39 226 0
2023-02-22 76852 77 226 0
2023-02-20 76775 48 226 0
2023-02-17 76727 29 226 0
2023-02-16 76698 19 226 0
2023-02-15 76679 48 226 0
2023-02-14 76631 23 226 0
2023-02-13 76608 47 226 0
2023-02-10 76561 31 226 0
2023-02-09 76530 26 226 0
2023-02-08 76504 36 226 0
2023-02-07 76468 36 226 0
2023-02-06 76432 57 226 0
2023-02-03 76375 13 226 0
2023-02-02 76362 25 226 0
2023-02-01 76337 40 226 0
2023-01-31 76297 30 226 0
2023-01-30 76267 34 226 0
2023-01-27 76233 20 226 0
2023-01-26 76213 17 226 0
2023-01-25 76196 23 226 0
2023-01-24 76173 22 226 0
2023-01-23 76151 20 226 0
2023-01-20 76131 12 226 0
2023-01-19 76119 10 226 0
2023-01-18 76109 23 226 0
2023-01-17 76086 17 226 1
2023-01-16 76069 30 225 0
2023-01-13 76039 15 225 0
2023-01-12 76024 11 225 0
2023-01-11 76013 13 225 0
2023-01-10 76000 22 225 0
2023-01-09 75978 30 225 0
2023-01-05 75948 16 225 0
2023-01-04 75932 25 225 0
2023-01-03 75907 28 225 0
2023-01-02 75879 25 225 0
2022-12-30 75854 26 225 0
2022-12-29 75828 9 225 0
2022-12-28 75819 33 225 0
2022-12-27 75786 65 225 0
2022-12-23 75721 29 225 0
2022-12-22 75692 24 225 0
2022-12-21 75668 37 225 0
2022-12-20 75631 25 225 1
2022-12-19 75606 52 224 0
2022-12-16 75554 21 224 0
2022-12-15 75533 16 224 1
2022-12-14 75517 21 223 1
2022-12-13 75496 23 222 0
2022-12-12 75473 45 222 0
2022-12-09 75428 10 222 0
2022-12-08 75418 28 222 0
2022-12-07 75390 22 222 0
2022-12-06 75368 25 222 0
2022-12-05 75343 38 222 0
2022-12-02 75305 11 222 0
2022-12-01 75294 22 222 0
2022-11-30 75272 17 222 0
2022-11-29 75255 21 222 1
2022-11-28 75234 28 221 0
2022-11-25 75206 17 221 0
2022-11-24 75189 17 221 0
2022-11-23 75172 14 221 0
2022-11-22 75158 17 221 0
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2022-10-28 74652 47 218 1
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2022-10-24 74427 134 217 2
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2022-10-20 74220 81 215 0
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2022-10-18 74021 135 215 1
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2022-10-14 73597 145 214 0
2022-10-13 73452 226 214 0
2022-10-12 73226 197 214 2
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2022-10-07 72344 374 210 0
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2022-10-05 71595 75 210 1
2022-10-04 71520 521 209 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: 0:03:05.353316