Germany: LK Konstanz (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 09:54:32 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Konstanz", weeks=5);
2023-03-07T09:54:47.574526 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 150 150 7-day incidence rate (per 100K people) 52.4 LK Konstanz, 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 0.1 0.2 0.3 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Konstanz cases daily growth factor Germany-LK Konstanz cases daily growth factor (rolling mean) Germany-LK Konstanz 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-LK Konstanz deaths daily growth factor Germany-LK Konstanz deaths daily growth factor (rolling mean) Germany-LK Konstanz 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 Konstanz doubling time cases (rolling mean) 0.0 28.8 57.6 86.4 115.2 daily change Germany-LK Konstanz new cases (rolling 7d mean) Germany-LK Konstanz new cases 0.000 0.288 0.576 0.864 daily change Germany-LK Konstanz new deaths (rolling 7d mean) Germany-LK Konstanz new deaths 0.000 0.310 0.619 0.929
In [4]:
overview(country="Germany", subregion="LK Konstanz");
2023-03-07T09:55:07.039354 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) 52.4 LK Konstanz, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Konstanz cases daily growth factor Germany-LK Konstanz cases daily growth factor (rolling mean) Germany-LK Konstanz 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-LK Konstanz deaths daily growth factor Germany-LK Konstanz deaths daily growth factor (rolling mean) Germany-LK Konstanz estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 cases doubling time [days] Germany-LK Konstanz doubling time cases (rolling mean) Germany-LK Konstanz doubling time deaths (rolling mean) 0 576 1152 1729 daily change Germany-LK Konstanz new cases (rolling 7d mean) Germany-LK Konstanz new cases 0.000 2.881 5.762 daily change Germany-LK Konstanz new deaths (rolling 7d mean) Germany-LK Konstanz new deaths 0.0 138.9 277.9 416.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Konstanz", dates="2020-03-15:");
2023-03-07T09:57:14.278103 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 Konstanz LK Konstanz 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 Konstanz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Konstanz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Konstanz")
print(f'Population of country="Germany", subregion="LK Konstanz": {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 Konstanz": 288097 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 124902 7 453 0
2023-03-05 124895 10 453 0
2023-03-03 124885 14 453 0
2023-03-02 124871 21 453 0
2023-03-01 124850 44 453 0
2023-02-28 124806 55 453 0
2023-02-27 124751 120 453 0
2023-02-26 124631 1 453 0
2023-02-25 124630 1 453 0
2023-02-24 124629 71 453 0
2023-02-23 124558 65 453 0
2023-02-22 124493 93 453 0
2023-02-21 124400 93 453 0
2023-02-20 124307 7 453 0
2023-02-17 124300 20 453 0
2023-02-16 124280 28 453 0
2023-02-15 124252 40 453 0
2023-02-14 124212 51 453 0
2023-02-13 124161 42 453 0
2023-02-11 124119 2 453 0
2023-02-10 124117 31 453 0
2023-02-09 124086 22 453 0
2023-02-08 124064 34 453 0
2023-02-07 124030 66 453 1
2023-02-06 123964 37 452 0
2023-02-04 123927 3 452 0
2023-02-03 123924 29 452 0
2023-02-02 123895 26 452 0
2023-02-01 123869 33 452 0
2023-01-31 123836 33 452 0
2023-01-30 123803 24 452 0
2023-01-28 123779 2 452 0
2023-01-27 123777 23 452 0
2023-01-26 123754 15 452 0
2023-01-25 123739 38 452 0
2023-01-24 123701 30 452 0
2023-01-23 123671 24 452 0
2023-01-21 123647 2 452 0
2023-01-20 123645 9 452 0
2023-01-19 123636 14 452 0
2023-01-18 123622 21 452 0
2023-01-17 123601 34 452 0
2023-01-16 123567 33 452 0
2023-01-14 123534 1 452 0
2023-01-13 123533 30 452 0
2023-01-12 123503 21 452 0
2023-01-11 123482 36 452 0
2023-01-10 123446 46 452 0
2023-01-09 123400 41 452 0
2023-01-05 123359 36 452 0
2023-01-04 123323 40 452 0
2023-01-03 123283 41 452 0
2023-01-02 123242 96 452 0
2022-12-30 123146 1 452 0
2022-12-29 123145 120 452 0
2022-12-28 123025 6 452 0
2022-12-27 123019 100 452 0
2022-12-26 122919 2 452 0
2022-12-25 122917 4 452 0
2022-12-24 122913 1 452 0
2022-12-23 122912 60 452 0
2022-12-22 122852 60 452 0
2022-12-21 122792 63 452 0
2022-12-20 122729 123 452 0
2022-12-19 122606 94 452 0
2022-12-18 122512 3 452 0
2022-12-17 122509 2 452 0
2022-12-16 122507 45 452 0
2022-12-15 122462 86 452 0
2022-12-14 122376 62 452 0
2022-12-13 122314 80 452 1
2022-12-12 122234 104 451 0
2022-12-09 122130 56 451 0
2022-12-08 122074 49 451 0
2022-12-07 122025 79 451 0
2022-12-06 121946 97 451 0
2022-12-05 121849 74 451 0
2022-12-04 121775 1 451 0
2022-12-03 121774 1 451 0
2022-12-02 121773 61 451 0
2022-12-01 121712 86 451 0
2022-11-30 121626 60 451 0
2022-11-29 121566 85 451 0
2022-11-28 121481 69 451 0
2022-11-27 121412 1 451 0
2022-11-26 121411 4 451 0
2022-11-25 121407 54 451 0
2022-11-24 121353 61 451 1
2022-11-23 121292 54 450 0
2022-11-22 121238 87 450 1
2022-11-21 121151 131 449 0
2022-11-18 121020 80 449 0
2022-11-17 120940 45 449 0
2022-11-16 120895 99 449 0
2022-11-15 120796 116 449 0
2022-11-14 120680 112 449 0
2022-11-13 120568 2 449 0
2022-11-12 120566 6 449 0
2022-11-11 120560 86 449 0
2022-11-10 120474 117 449 0
2022-11-09 120357 99 449 0
2022-11-08 120258 171 449 0
2022-11-07 120087 185 449 0
2022-11-06 119902 2 449 0
2022-11-05 119900 5 449 0
2022-11-04 119895 105 449 0
2022-11-03 119790 152 449 1
2022-11-02 119638 220 448 0
2022-11-01 119418 8 448 0
2022-10-31 119410 193 448 1
2022-10-30 119217 1 447 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: 0:02:58.771984