Germany: LK Cham (Bayern)¶

  • Homepage of project: https://oscovida.github.io
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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 10:33:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Cham", weeks=5);
2023-03-07T10:33:14.251720 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 125 125 150 150 175 175 200 200 225 225 7-day incidence rate (per 100K people) 118.3 LK Cham, 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 60 80 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 2.0 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-LK Cham cases daily growth factor Germany-LK Cham cases daily growth factor (rolling mean) Germany-LK Cham estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Cham deaths daily growth factor Germany-LK Cham deaths daily growth factor (rolling mean) Germany-LK Cham 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 Cham doubling time cases (rolling mean) 0.0 25.7 51.4 77.1 102.8 daily change Germany-LK Cham new cases (rolling 7d mean) Germany-LK Cham new cases 0.000 0.642 1.284 1.927 2.569 daily change Germany-LK Cham new deaths (rolling 7d mean) Germany-LK Cham new deaths 0.000 0.288 0.575 0.863
In [4]:
overview(country="Germany", subregion="LK Cham");
2023-03-07T10:33:32.708605 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) 118.3 LK Cham, 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 0 200 400 600 800 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 8 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-LK Cham cases daily growth factor Germany-LK Cham cases daily growth factor (rolling mean) Germany-LK Cham estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Cham deaths daily growth factor Germany-LK Cham deaths daily growth factor (rolling mean) Germany-LK Cham 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 Cham doubling time cases (rolling mean) Germany-LK Cham doubling time deaths (rolling mean) 0 257 514 771 1028 daily change Germany-LK Cham new cases (rolling 7d mean) Germany-LK Cham new cases 0.00 2.57 5.14 7.71 10.28 daily change Germany-LK Cham new deaths (rolling 7d mean) Germany-LK Cham new deaths 0.0 64.1 128.3 192.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Cham", dates="2020-03-15:");
2023-03-07T10:36:13.450710 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 Cham LK Cham 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Cham Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Cham")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Cham")
print(f'Population of country="Germany", subregion="LK Cham": {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 Cham": 128444 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 69080 30 328 0
2023-03-03 69050 28 328 0
2023-03-02 69022 35 328 0
2023-03-01 68987 14 328 0
2023-02-28 68973 45 328 0
2023-02-27 68928 68 328 0
2023-02-24 68860 31 328 0
2023-02-23 68829 41 328 0
2023-02-22 68788 105 328 0
2023-02-20 68683 50 328 3
2023-02-17 68633 41 325 0
2023-02-16 68592 32 325 0
2023-02-15 68560 33 325 0
2023-02-14 68527 59 325 0
2023-02-13 68468 52 325 0
2023-02-10 68416 38 325 0
2023-02-09 68378 31 325 0
2023-02-08 68347 43 325 0
2023-02-07 68304 71 325 0
2023-02-06 68233 41 325 0
2023-02-03 68192 35 325 0
2023-02-02 68157 34 325 0
2023-02-01 68123 35 325 0
2023-01-31 68088 59 325 0
2023-01-30 68029 37 325 0
2023-01-27 67992 24 325 0
2023-01-26 67968 28 325 0
2023-01-25 67940 29 325 0
2023-01-24 67911 40 325 0
2023-01-23 67871 28 325 0
2023-01-20 67843 28 325 0
2023-01-19 67815 21 325 0
2023-01-18 67794 24 325 0
2023-01-17 67770 40 325 0
2023-01-16 67730 36 325 1
2023-01-13 67694 15 324 0
2023-01-12 67679 17 324 0
2023-01-11 67662 38 324 0
2023-01-10 67624 59 324 0
2023-01-09 67565 42 324 0
2023-01-05 67523 27 324 0
2023-01-04 67496 31 324 1
2023-01-03 67465 30 323 0
2023-01-02 67435 31 323 0
2022-12-30 67404 21 323 0
2022-12-29 67383 24 323 1
2022-12-28 67359 45 322 0
2022-12-27 67314 47 322 0
2022-12-23 67267 16 322 1
2022-12-22 67251 23 321 0
2022-12-21 67228 31 321 0
2022-12-20 67197 58 321 0
2022-12-19 67139 53 321 1
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2022-12-12 66901 74 316 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: 0:03:32.977257