Germany: LK Emmendingen (Baden-Württemberg)¶

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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 09:44:31 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Emmendingen", weeks=5);
2023-03-07T09:44:46.937609 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 101.2 LK Emmendingen, 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 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-LK Emmendingen cases daily growth factor Germany-LK Emmendingen cases daily growth factor (rolling mean) Germany-LK Emmendingen 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Emmendingen deaths daily growth factor Germany-LK Emmendingen deaths daily growth factor (rolling mean) Germany-LK Emmendingen 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-LK Emmendingen doubling time cases (rolling mean) 0.0 33.6 67.2 daily change Germany-LK Emmendingen new cases (rolling 7d mean) Germany-LK Emmendingen new cases 0.000 0.336 0.672 daily change Germany-LK Emmendingen new deaths (rolling 7d mean) Germany-LK Emmendingen new deaths 0.000 0.269 0.538 0.806
In [4]:
overview(country="Germany", subregion="LK Emmendingen");
2023-03-07T09:45:05.387156 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) 101.2 LK Emmendingen, 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 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 Emmendingen cases daily growth factor Germany-LK Emmendingen cases daily growth factor (rolling mean) Germany-LK Emmendingen 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Emmendingen deaths daily growth factor Germany-LK Emmendingen deaths daily growth factor (rolling mean) Germany-LK Emmendingen 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 Emmendingen doubling time cases (rolling mean) Germany-LK Emmendingen doubling time deaths (rolling mean) 0 336 672 daily change Germany-LK Emmendingen new cases (rolling 7d mean) Germany-LK Emmendingen new cases 0.00 3.36 6.72 10.08 daily change Germany-LK Emmendingen new deaths (rolling 7d mean) Germany-LK Emmendingen new deaths 0.0 73.8 147.5 221.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Emmendingen", dates="2020-03-15:");
2023-03-07T09:50:04.030470 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 Emmendingen LK Emmendingen 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 Emmendingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Emmendingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Emmendingen")
print(f'Population of country="Germany", subregion="LK Emmendingen": {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 Emmendingen": 167945 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 81217 3 297 0
2023-03-05 81214 3 297 0
2023-03-04 81211 9 297 0
2023-03-03 81202 13 297 0
2023-03-02 81189 21 297 0
2023-03-01 81168 54 297 0
2023-02-28 81114 67 297 0
2023-02-27 81047 33 297 0
2023-02-26 81014 56 297 0
2023-02-25 80958 26 297 0
2023-02-24 80932 65 297 0
2023-02-23 80867 64 297 0
2023-02-22 80803 86 297 0
2023-02-21 80717 73 297 0
2023-02-20 80644 21 297 0
2023-02-19 80623 2 297 0
2023-02-18 80621 34 297 0
2023-02-17 80587 34 297 0
2023-02-16 80553 48 297 1
2023-02-15 80505 46 296 0
2023-02-14 80459 81 296 1
2023-02-13 80378 19 295 1
2023-02-12 80359 15 294 0
2023-02-11 80344 33 294 0
2023-02-10 80311 35 294 0
2023-02-09 80276 73 294 0
2023-02-08 80203 49 294 0
2023-02-07 80154 90 294 1
2023-02-06 80064 23 293 0
2023-02-05 80041 8 293 0
2023-02-04 80033 31 293 0
2023-02-03 80002 39 293 0
2023-02-02 79963 50 293 0
2023-02-01 79913 37 293 0
2023-01-31 79876 45 293 1
2023-01-30 79831 11 292 0
2023-01-29 79820 8 292 0
2023-01-28 79812 24 292 0
2023-01-27 79788 14 292 0
2023-01-26 79774 34 292 0
2023-01-25 79740 32 292 0
2023-01-24 79708 25 292 0
2023-01-23 79683 11 292 0
2023-01-22 79672 6 292 0
2023-01-21 79666 14 292 0
2023-01-20 79652 13 292 0
2023-01-19 79639 32 292 0
2023-01-18 79607 11 292 1
2023-01-17 79596 35 291 1
2023-01-16 79561 17 290 0
2023-01-15 79544 18 290 0
2023-01-13 79526 25 290 0
2023-01-12 79501 29 290 0
2023-01-11 79472 29 290 0
2023-01-10 79443 28 290 1
2023-01-09 79415 11 289 0
2023-01-08 79404 6 289 0
2023-01-07 79398 9 289 0
2023-01-06 79389 17 289 0
2023-01-05 79372 31 289 0
2023-01-04 79341 28 289 0
2023-01-03 79313 66 289 0
2023-01-02 79247 27 289 0
2023-01-01 79220 30 289 0
2022-12-31 79190 7 289 0
2022-12-30 79183 34 289 0
2022-12-29 79149 46 289 0
2022-12-28 79103 46 289 0
2022-12-27 79057 34 289 0
2022-12-26 79023 16 289 1
2022-12-25 79007 39 288 0
2022-12-24 78968 40 288 2
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2022-12-05 77796 34 278 1
2022-12-04 77762 4 277 0
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2022-12-02 77707 42 277 0
2022-12-01 77665 48 277 0
2022-11-30 77617 53 277 1
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2022-11-24 77364 54 274 0
2022-11-23 77310 59 274 0
2022-11-22 77251 89 274 0
2022-11-21 77162 23 274 1
2022-11-20 77139 15 273 0
2022-11-19 77124 48 273 0
2022-11-18 77076 73 273 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: 0:06:16.686403