Germany: LK Roth (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 11:22:15 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Roth", weeks=5);
2023-03-07T11:22:28.205229 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 90.2 LK Roth, 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 0.5 1.0 1.5 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 Roth cases daily growth factor Germany-LK Roth cases daily growth factor (rolling mean) Germany-LK Roth estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Roth deaths daily growth factor Germany-LK Roth deaths daily growth factor (rolling mean) Germany-LK Roth 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 Roth doubling time cases (rolling mean) Germany-LK Roth doubling time deaths (rolling mean) 0.00 25.50 51.01 daily change Germany-LK Roth new cases (rolling 7d mean) Germany-LK Roth new cases 0.000 0.638 1.275 1.913 daily change Germany-LK Roth new deaths (rolling 7d mean) Germany-LK Roth new deaths 0.0 83.7 167.3 251.0 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Roth");
2023-03-07T11:22:42.142492 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) 90.2 LK Roth, 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 0.0 2.5 5.0 7.5 10.0 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 Roth cases daily growth factor Germany-LK Roth cases daily growth factor (rolling mean) Germany-LK Roth estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Roth deaths daily growth factor Germany-LK Roth deaths daily growth factor (rolling mean) Germany-LK Roth 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 8000 cases doubling time [days] Germany-LK Roth doubling time cases (rolling mean) Germany-LK Roth doubling time deaths (rolling mean) 0 255 510 765 daily change Germany-LK Roth new cases (rolling 7d mean) Germany-LK Roth new cases 0.00 3.19 6.38 9.56 12.75 daily change Germany-LK Roth new deaths (rolling 7d mean) Germany-LK Roth new deaths 0.0 58.7 117.3 176.0 234.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Roth", dates="2020-03-15:");
2023-03-07T11:25:17.104888 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 Roth LK Roth 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 Roth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Roth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Roth")
print(f'Population of country="Germany", subregion="LK Roth": {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 Roth": 127520 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 67118 25 280 0
2023-03-03 67093 16 280 0
2023-03-02 67077 18 280 0
2023-03-01 67059 29 280 0
2023-02-28 67030 27 280 0
2023-02-27 67003 65 280 0
2023-02-24 66938 22 280 0
2023-02-23 66916 37 280 0
2023-02-22 66879 31 280 1
2023-02-21 66848 67 279 0
2023-02-20 66781 32 279 1
2023-02-17 66749 31 278 2
2023-02-16 66718 30 276 0
2023-02-15 66688 46 276 0
2023-02-14 66642 45 276 1
2023-02-13 66597 46 275 0
2023-02-10 66551 24 275 0
2023-02-09 66527 24 275 0
2023-02-08 66503 36 275 0
2023-02-07 66467 32 275 0
2023-02-06 66435 46 275 0
2023-02-03 66389 31 275 0
2023-02-02 66358 29 275 0
2023-02-01 66329 34 275 1
2023-01-31 66295 38 274 0
2023-01-30 66257 34 274 0
2023-01-27 66223 14 274 0
2023-01-26 66209 23 274 0
2023-01-25 66186 16 274 0
2023-01-24 66170 24 274 0
2023-01-23 66146 17 274 0
2023-01-20 66129 6 274 0
2023-01-19 66123 13 274 0
2023-01-18 66110 17 274 0
2023-01-17 66093 36 274 0
2023-01-16 66057 25 274 0
2023-01-13 66032 12 274 0
2023-01-12 66020 17 274 0
2023-01-11 66003 26 274 0
2023-01-10 65977 24 274 0
2023-01-09 65953 31 274 0
2023-01-05 65922 18 274 0
2023-01-04 65904 26 274 0
2023-01-03 65878 29 274 0
2023-01-02 65849 40 274 0
2022-12-30 65809 30 274 0
2022-12-29 65779 38 274 0
2022-12-28 65741 26 274 0
2022-12-27 65715 91 274 1
2022-12-24 65624 1 273 0
2022-12-23 65623 26 273 0
2022-12-22 65597 57 273 1
2022-12-21 65540 57 272 1
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2022-12-19 65437 57 270 1
2022-12-18 65380 1 269 0
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2022-12-15 65345 30 269 1
2022-12-14 65315 44 268 0
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2022-12-12 65247 33 268 1
2022-12-09 65214 24 267 0
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2022-10-24 63456 194 264 3
2022-10-21 63262 102 261 0
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2022-10-16 62360 2 260 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: 0:03:18.885385