Germany: LK Hohenlohekreis (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 09:55:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hohenlohekreis", weeks=5);
2023-03-07T09:55:20.417833 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 42.4 LK Hohenlohekreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 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 Hohenlohekreis new deaths (rolling 7d mean) Germany-LK Hohenlohekreis new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Hohenlohekreis cases daily growth factor Germany-LK Hohenlohekreis cases daily growth factor (rolling mean) Germany-LK Hohenlohekreis 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 Hohenlohekreis deaths daily growth factor Germany-LK Hohenlohekreis deaths daily growth factor (rolling mean) Germany-LK Hohenlohekreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Hohenlohekreis doubling time cases (rolling mean) 0.00 5.67 11.33 17.00 22.66 daily change Germany-LK Hohenlohekreis new cases (rolling 7d mean) Germany-LK Hohenlohekreis new cases 0.000 0.226 0.452 0.678 0.905
In [4]:
overview(country="Germany", subregion="LK Hohenlohekreis");
2023-03-07T09:55:39.583865 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) 42.4 LK Hohenlohekreis, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Hohenlohekreis cases daily growth factor Germany-LK Hohenlohekreis cases daily growth factor (rolling mean) Germany-LK Hohenlohekreis 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 Hohenlohekreis deaths daily growth factor Germany-LK Hohenlohekreis deaths daily growth factor (rolling mean) Germany-LK Hohenlohekreis 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 Hohenlohekreis doubling time cases (rolling mean) Germany-LK Hohenlohekreis doubling time deaths (rolling mean) 0.0 226.6 453.3 679.9 daily change Germany-LK Hohenlohekreis new cases (rolling 7d mean) Germany-LK Hohenlohekreis new cases 0.000 1.133 2.266 3.400 4.533 daily change Germany-LK Hohenlohekreis new deaths (rolling 7d mean) Germany-LK Hohenlohekreis new deaths 0.0 25.5 51.1 76.6 102.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hohenlohekreis", dates="2020-03-15:");
2023-03-07T09:57:52.137888 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 Hohenlohekreis LK Hohenlohekreis 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 Hohenlohekreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hohenlohekreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hohenlohekreis")
print(f'Population of country="Germany", subregion="LK Hohenlohekreis": {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 Hohenlohekreis": 113318 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 54984 3 198 0
2023-03-04 54981 5 198 0
2023-03-03 54976 9 198 0
2023-03-02 54967 4 198 0
2023-03-01 54963 6 198 0
2023-02-28 54957 21 198 0
2023-02-27 54936 12 198 0
2023-02-25 54924 5 198 0
2023-02-24 54919 17 198 0
2023-02-23 54902 15 198 0
2023-02-22 54887 11 198 0
2023-02-21 54876 25 198 0
2023-02-20 54851 12 198 0
2023-02-19 54839 2 198 0
2023-02-18 54837 6 198 0
2023-02-17 54831 17 198 0
2023-02-16 54814 8 198 0
2023-02-15 54806 17 198 0
2023-02-14 54789 24 198 0
2023-02-13 54765 20 198 0
2023-02-11 54745 4 198 0
2023-02-09 54741 5 198 0
2023-02-08 54736 16 198 0
2023-02-07 54720 15 198 0
2023-02-06 54705 19 198 0
2023-02-04 54686 6 198 0
2023-02-03 54680 8 198 0
2023-02-02 54672 14 198 0
2023-02-01 54658 11 198 0
2023-01-31 54647 14 198 0
2023-01-30 54633 26 198 0
2023-01-29 54607 1 198 0
2023-01-28 54606 3 198 0
2023-01-27 54603 12 198 0
2023-01-26 54591 5 198 0
2023-01-25 54586 27 198 0
2023-01-24 54559 10 198 0
2023-01-23 54549 13 198 0
2023-01-22 54536 1 198 0
2023-01-21 54535 3 198 0
2023-01-20 54532 6 198 0
2023-01-19 54526 6 198 0
2023-01-18 54520 13 198 0
2023-01-17 54507 2 198 0
2023-01-16 54505 9 198 0
2023-01-15 54496 2 198 0
2023-01-14 54494 3 198 0
2023-01-13 54491 11 198 0
2023-01-11 54480 2 198 0
2023-01-10 54478 4 198 0
2023-01-09 54474 21 198 0
2023-01-08 54453 1 198 0
2023-01-07 54452 7 198 0
2023-01-06 54445 7 198 0
2023-01-05 54438 9 198 0
2023-01-04 54429 12 198 0
2023-01-03 54417 22 198 0
2023-01-02 54395 10 198 0
2023-01-01 54385 1 198 0
2022-12-31 54384 31 198 0
2022-12-29 54353 41 198 0
2022-12-28 54312 10 198 0
2022-12-27 54302 10 198 0
2022-12-26 54292 26 198 0
2022-12-23 54266 21 198 0
2022-12-22 54245 32 198 0
2022-12-21 54213 36 198 0
2022-12-20 54177 31 198 0
2022-12-19 54146 7 198 0
2022-12-18 54139 13 198 0
2022-12-17 54126 1 198 0
2022-12-16 54125 41 198 0
2022-12-15 54084 27 198 0
2022-12-14 54057 45 198 0
2022-12-13 54012 14 198 0
2022-12-12 53998 14 198 0
2022-12-11 53984 26 198 0
2022-12-10 53958 12 198 0
2022-12-09 53946 27 198 0
2022-12-08 53919 25 198 0
2022-12-07 53894 23 198 0
2022-12-06 53871 45 198 0
2022-12-05 53826 5 198 0
2022-12-04 53821 14 198 0
2022-12-02 53807 11 198 0
2022-12-01 53796 26 198 0
2022-11-30 53770 7 198 0
2022-11-29 53763 55 198 0
2022-11-28 53708 7 198 0
2022-11-27 53701 16 198 0
2022-11-25 53685 10 198 0
2022-11-24 53675 35 198 1
2022-11-23 53640 27 197 0
2022-11-22 53613 40 197 0
2022-11-21 53573 10 197 0
2022-11-20 53563 10 197 0
2022-11-19 53553 12 197 0
2022-11-18 53541 21 197 0
2022-11-17 53520 33 197 0
2022-11-16 53487 40 197 0
2022-11-15 53447 42 197 0
2022-11-14 53405 9 197 0
2022-11-13 53396 10 197 0
2022-11-12 53386 19 197 0
2022-11-11 53367 41 197 0
2022-11-10 53326 42 197 0
2022-11-09 53284 34 197 0
2022-11-08 53250 36 197 0
2022-11-07 53214 13 197 0
2022-11-06 53201 28 197 0
2022-11-05 53173 1 197 0
2022-11-04 53172 46 197 0
2022-11-03 53126 34 197 0
2022-11-02 53092 68 197 0
2022-11-01 53024 5 197 0
2022-10-31 53019 14 197 0
2022-10-30 53005 59 197 0
2022-10-29 52946 13 197 0
2022-10-28 52933 38 197 0
2022-10-27 52895 98 197 0
2022-10-26 52797 28 197 0
2022-10-25 52769 155 197 0
2022-10-24 52614 44 197 0
2022-10-23 52570 66 197 0
2022-10-21 52504 123 197 0
2022-10-20 52381 175 197 0
2022-10-19 52206 145 197 0
2022-10-18 52061 138 197 0
2022-10-17 51923 46 197 1
2022-10-16 51877 110 196 0
2022-10-15 51767 87 196 0
2022-10-14 51680 208 196 0
2022-10-13 51472 251 196 0
2022-10-12 51221 250 196 0
2022-10-11 50971 153 196 0
2022-10-10 50818 182 196 0
2022-10-09 50636 91 196 0
2022-10-08 50545 20 196 0
2022-10-07 50525 214 196 0
2022-10-06 50311 319 196 0
2022-10-05 49992 49 196 0
2022-10-04 49943 179 196 0
2022-10-03 49764 19 196 0
2022-10-02 49745 30 196 0
2022-10-01 49715 26 196 0
2022-09-30 49689 104 196 0
2022-09-29 49585 108 196 0
2022-09-28 49477 107 196 0
2022-09-27 49370 127 196 1
2022-09-26 49243 87 195 0
2022-09-25 49156 83 195 0
2022-09-23 49073 57 195 0
2022-09-22 49016 62 195 0
2022-09-21 48954 67 195 0
2022-09-20 48887 68 195 1
2022-09-19 48819 32 194 0
2022-09-18 48787 61 194 0
2022-09-17 48726 1 194 0
2022-09-16 48725 19 194 0
2022-09-15 48706 58 194 0
2022-09-14 48648 28 194 0
2022-09-13 48620 33 194 0
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2022-09-11 48561 4 194 0
2022-09-10 48557 12 194 0
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2022-09-08 48506 18 194 0
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2022-08-10 47351 108 193 0
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2022-08-05 47028 90 193 0
2022-08-04 46938 28 193 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: 0:03:00.412755