Germany: LK Oberallgäu (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 11:12:33 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Oberallgäu", weeks=5);
2023-03-07T11:12:49.087502 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 57.9 LK Oberallgäu, 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 0.0 0.2 0.4 0.6 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 Oberallgäu cases daily growth factor Germany-LK Oberallgäu cases daily growth factor (rolling mean) Germany-LK Oberallgäu 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 Oberallgäu deaths daily growth factor Germany-LK Oberallgäu deaths daily growth factor (rolling mean) Germany-LK Oberallgäu 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 Oberallgäu doubling time cases (rolling mean) 0.00 7.86 15.72 23.58 31.44 daily change Germany-LK Oberallgäu new cases (rolling 7d mean) Germany-LK Oberallgäu new cases 0.000 0.314 0.629 0.943 daily change Germany-LK Oberallgäu new deaths (rolling 7d mean) Germany-LK Oberallgäu new deaths 0.000 0.299 0.599 0.898
In [4]:
overview(country="Germany", subregion="LK Oberallgäu");
2023-03-07T11:13:08.945164 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) 57.9 LK Oberallgäu, 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 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 R & growth factor (based on cases) Germany-LK Oberallgäu cases daily growth factor Germany-LK Oberallgäu cases daily growth factor (rolling mean) Germany-LK Oberallgäu 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 Oberallgäu deaths daily growth factor Germany-LK Oberallgäu deaths daily growth factor (rolling mean) Germany-LK Oberallgäu 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 Oberallgäu doubling time cases (rolling mean) Germany-LK Oberallgäu doubling time deaths (rolling mean) 0.0 314.4 628.8 daily change Germany-LK Oberallgäu new cases (rolling 7d mean) Germany-LK Oberallgäu new cases 0.000 1.572 3.144 4.716 6.288 daily change Germany-LK Oberallgäu new deaths (rolling 7d mean) Germany-LK Oberallgäu new deaths 0.0 52.4 104.8 157.2 209.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Oberallgäu", dates="2020-03-15:");
2023-03-07T11:17:11.075736 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 Oberallgäu LK Oberallgäu 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 Oberallgäu Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Oberallgäu")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Oberallgäu")
print(f'Population of country="Germany", subregion="LK Oberallgäu": {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 Oberallgäu": 157202 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 78293 6 232 0
2023-03-05 78287 4 232 0
2023-03-04 78283 4 232 0
2023-03-03 78279 15 232 0
2023-03-02 78264 7 232 0
2023-03-01 78257 20 232 0
2023-02-28 78237 35 232 0
2023-02-27 78202 30 232 0
2023-02-26 78172 6 232 0
2023-02-25 78166 11 232 0
2023-02-24 78155 24 232 0
2023-02-23 78131 26 232 0
2023-02-22 78105 22 232 0
2023-02-21 78083 35 232 0
2023-02-20 78048 26 232 0
2023-02-19 78022 3 232 0
2023-02-18 78019 6 232 0
2023-02-17 78013 21 232 0
2023-02-16 77992 19 232 0
2023-02-15 77973 32 232 0
2023-02-14 77941 30 232 0
2023-02-13 77911 22 232 0
2023-02-12 77889 3 232 0
2023-02-11 77886 5 232 0
2023-02-10 77881 30 232 0
2023-02-09 77851 20 232 0
2023-02-08 77831 20 232 1
2023-02-07 77811 32 231 0
2023-02-06 77779 35 231 0
2023-02-05 77744 3 231 0
2023-02-04 77741 3 231 0
2023-02-03 77738 18 231 0
2023-02-02 77720 18 231 0
2023-02-01 77702 28 231 0
2023-01-31 77674 27 231 0
2023-01-30 77647 20 231 0
2023-01-29 77627 5 231 1
2023-01-28 77622 4 230 0
2023-01-27 77618 18 230 0
2023-01-26 77600 8 230 0
2023-01-25 77592 15 230 0
2023-01-24 77577 19 230 0
2023-01-23 77558 16 230 0
2023-01-22 77542 6 230 0
2023-01-21 77536 8 230 0
2023-01-20 77528 15 230 0
2023-01-19 77513 11 230 0
2023-01-18 77502 19 230 0
2023-01-17 77483 19 230 0
2023-01-16 77464 21 230 0
2023-01-15 77443 2 230 0
2023-01-14 77441 3 230 0
2023-01-13 77438 10 230 0
2023-01-12 77428 6 230 0
2023-01-11 77422 14 230 0
2023-01-10 77408 11 230 0
2023-01-09 77397 17 230 0
2023-01-08 77380 5 230 1
2023-01-07 77375 3 229 0
2023-01-06 77372 5 229 0
2023-01-05 77367 19 229 0
2023-01-04 77348 37 229 0
2023-01-03 77311 31 229 0
2023-01-02 77280 23 229 2
2023-01-01 77257 8 227 0
2022-12-31 77249 9 227 0
2022-12-30 77240 24 227 0
2022-12-29 77216 23 227 0
2022-12-28 77193 49 227 1
2022-12-27 77144 22 226 0
2022-12-26 77122 5 226 0
2022-12-25 77117 4 226 0
2022-12-24 77113 6 226 0
2022-12-23 77107 38 226 0
2022-12-22 77069 19 226 0
2022-12-21 77050 30 226 0
2022-12-20 77020 36 226 1
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2022-12-17 76949 8 224 0
2022-12-16 76941 33 224 2
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2022-12-13 76819 45 222 1
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2022-12-11 76742 5 220 0
2022-12-10 76737 9 220 0
2022-12-09 76728 26 220 0
2022-12-08 76702 37 220 0
2022-12-07 76665 47 220 1
2022-12-06 76618 34 219 0
2022-12-05 76584 39 219 0
2022-12-04 76545 7 219 0
2022-12-03 76538 16 219 0
2022-12-02 76522 50 219 1
2022-12-01 76472 23 218 0
2022-11-30 76449 19 218 0
2022-11-29 76430 45 218 0
2022-11-28 76385 28 218 0
2022-11-27 76357 4 218 0
2022-11-26 76353 16 218 0
2022-11-25 76337 20 218 0
2022-11-24 76317 17 218 0
2022-11-23 76300 42 218 0
2022-11-22 76258 31 218 0
2022-11-21 76227 43 218 0
2022-11-20 76184 1 218 0
2022-11-19 76183 6 218 0
2022-11-18 76177 32 218 0
2022-11-17 76145 34 218 0
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2022-11-15 76085 35 218 1
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2022-11-10 75962 18 217 0
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2022-11-03 75733 67 216 0
2022-11-02 75666 55 216 0
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2022-10-31 75592 79 216 1
2022-10-30 75513 11 215 2
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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: 0:05:04.877536