Germany: SK Kempten (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 11:44:05 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Kempten", weeks=5);
2023-03-07T11:44:16.772139 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 50.7 SK Kempten, 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 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-SK Kempten new deaths (rolling 7d mean) Germany-SK Kempten 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 R & growth factor (based on cases) Germany-SK Kempten cases daily growth factor Germany-SK Kempten cases daily growth factor (rolling mean) Germany-SK Kempten 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-SK Kempten deaths daily growth factor Germany-SK Kempten deaths daily growth factor (rolling mean) Germany-SK Kempten 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-SK Kempten doubling time cases (rolling mean) 0.00 6.91 13.81 daily change Germany-SK Kempten new cases (rolling 7d mean) Germany-SK Kempten new cases 0.000 0.266 0.532 0.798
In [4]:
overview(country="Germany", subregion="SK Kempten");
2023-03-07T11:44:33.406484 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) 50.7 SK Kempten, 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 2 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-SK Kempten cases daily growth factor Germany-SK Kempten cases daily growth factor (rolling mean) Germany-SK Kempten 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-SK Kempten deaths daily growth factor Germany-SK Kempten deaths daily growth factor (rolling mean) Germany-SK Kempten 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-SK Kempten doubling time cases (rolling mean) Germany-SK Kempten doubling time deaths (rolling mean) 0.0 138.1 276.2 daily change Germany-SK Kempten new cases (rolling 7d mean) Germany-SK Kempten new cases 0.000 1.381 2.762 daily change Germany-SK Kempten new deaths (rolling 7d mean) Germany-SK Kempten new deaths 0.0 25.5 51.0 76.5 102.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Kempten", dates="2020-03-15:");
2023-03-07T11:46:45.801094 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: SK Kempten SK Kempten 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 SK Kempten Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Kempten")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Kempten")
print(f'Population of country="Germany", subregion="SK Kempten": {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="SK Kempten": 69053 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 34810 3 131 0
2023-03-05 34807 1 131 0
2023-03-04 34806 1 131 0
2023-03-03 34805 5 131 0
2023-03-02 34800 7 131 0
2023-03-01 34793 7 131 0
2023-02-28 34786 11 131 0
2023-02-27 34775 13 131 0
2023-02-26 34762 3 131 0
2023-02-25 34759 2 131 0
2023-02-24 34757 11 131 0
2023-02-23 34746 10 131 0
2023-02-22 34736 9 131 0
2023-02-21 34727 17 131 0
2023-02-20 34710 18 131 0
2023-02-19 34692 2 131 0
2023-02-18 34690 1 131 0
2023-02-17 34689 10 131 0
2023-02-16 34679 14 131 0
2023-02-15 34665 8 131 0
2023-02-14 34657 17 131 0
2023-02-13 34640 7 131 0
2023-02-12 34633 3 131 0
2023-02-11 34630 3 131 0
2023-02-10 34627 9 131 0
2023-02-09 34618 11 131 0
2023-02-08 34607 12 131 0
2023-02-07 34595 10 131 0
2023-02-06 34585 8 131 0
2023-02-04 34577 2 131 0
2023-02-03 34575 9 131 0
2023-02-02 34566 5 131 0
2023-02-01 34561 14 131 0
2023-01-31 34547 14 131 0
2023-01-30 34533 10 131 0
2023-01-29 34523 4 131 0
2023-01-28 34519 1 131 0
2023-01-27 34518 7 131 0
2023-01-26 34511 7 131 0
2023-01-25 34504 6 131 0
2023-01-24 34498 6 131 1
2023-01-23 34492 9 130 0
2023-01-20 34483 4 130 0
2023-01-19 34479 7 130 0
2023-01-18 34472 4 130 0
2023-01-17 34468 6 130 0
2023-01-16 34462 3 130 0
2023-01-14 34459 1 130 0
2023-01-13 34458 4 130 0
2023-01-12 34454 4 130 0
2023-01-11 34450 4 130 0
2023-01-10 34446 6 130 0
2023-01-09 34440 4 130 0
2023-01-08 34436 6 130 0
2023-01-07 34430 7 130 0
2023-01-06 34423 1 130 0
2023-01-05 34422 5 130 0
2023-01-04 34417 13 130 0
2023-01-03 34404 8 130 1
2023-01-02 34396 8 129 0
2023-01-01 34388 3 129 0
2022-12-31 34385 5 129 0
2022-12-30 34380 9 129 1
2022-12-29 34371 7 128 0
2022-12-28 34364 35 128 0
2022-12-27 34329 16 128 0
2022-12-26 34313 1 128 0
2022-12-25 34312 4 128 0
2022-12-24 34308 5 128 0
2022-12-23 34303 15 128 0
2022-12-22 34288 15 128 0
2022-12-21 34273 14 128 0
2022-12-20 34259 9 128 0
2022-12-19 34250 22 128 0
2022-12-18 34228 1 128 0
2022-12-17 34227 6 128 0
2022-12-16 34221 8 128 0
2022-12-15 34213 6 128 0
2022-12-14 34207 19 128 0
2022-12-13 34188 26 128 0
2022-12-12 34162 14 128 0
2022-12-11 34148 6 128 0
2022-12-10 34142 1 128 0
2022-12-09 34141 12 128 0
2022-12-08 34129 11 128 0
2022-12-07 34118 21 128 0
2022-12-06 34097 16 128 0
2022-12-05 34081 32 128 1
2022-12-04 34049 5 127 0
2022-12-03 34044 4 127 0
2022-12-02 34040 15 127 1
2022-12-01 34025 7 126 0
2022-11-30 34018 18 126 0
2022-11-29 34000 13 126 2
2022-11-28 33987 13 124 0
2022-11-27 33974 2 124 0
2022-11-26 33972 4 124 0
2022-11-25 33968 14 124 0
2022-11-24 33954 4 124 0
2022-11-23 33950 20 124 0
2022-11-22 33930 16 124 1
2022-11-21 33914 28 123 0
2022-11-20 33886 2 123 0
2022-11-19 33884 3 123 0
2022-11-18 33881 24 123 0
2022-11-17 33857 21 123 0
2022-11-16 33836 17 123 0
2022-11-15 33819 16 123 1
2022-11-14 33803 32 122 0
2022-11-13 33771 4 122 1
2022-11-12 33767 2 121 0
2022-11-11 33765 26 121 1
2022-11-10 33739 15 120 0
2022-11-09 33724 32 120 0
2022-11-08 33692 10 120 0
2022-11-07 33682 31 120 0
2022-11-06 33651 6 120 0
2022-11-05 33645 2 120 0
2022-11-04 33643 13 120 0
2022-11-03 33630 11 120 0
2022-11-02 33619 27 120 0
2022-11-01 33592 11 120 0
2022-10-31 33581 43 120 0
2022-10-30 33538 4 120 0
2022-10-29 33534 11 120 0
2022-10-28 33523 37 120 0
2022-10-27 33486 13 120 0
2022-10-26 33473 60 120 0
2022-10-25 33413 23 120 0
2022-10-24 33390 55 120 0
2022-10-23 33335 12 120 0
2022-10-22 33323 7 120 0
2022-10-21 33316 74 120 0
2022-10-20 33242 24 120 0
2022-10-19 33218 92 120 0
2022-10-18 33126 58 120 0
2022-10-17 33068 89 120 0
2022-10-16 32979 6 120 1
2022-10-15 32973 48 119 1
2022-10-14 32925 110 118 3
2022-10-13 32815 41 115 0
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2021-01-05 1407 14 24 0
2021-01-04 1393 6 24 0
2021-01-02 1387 10 24 0
2020-12-31 1377 8 24 0
2020-12-30 1369 17 24 0
2020-12-29 1352 27 24 1
2020-12-28 1325 2 23 0
2020-12-27 1323 8 23 0
2020-12-26 1315 5 23 0
2020-12-25 1310 4 23 0
2020-12-24 1306 25 23 0
2020-12-23 1281 28 23 1
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2020-12-21 1230 3 22 0
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2020-12-14 1071 7 19 0
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2020-12-07 931 2 18 0
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2020-11-06 460 34 12 2
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2020-10-16 214 9 10 0
2020-10-15 205 2 10 0
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2020-10-09 197 3 10 0
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2020-10-05 188 1 10 0
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2020-09-22 183 1 10 0
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2020-09-18 180 2 10 0
2020-09-17 178 2 10 0
2020-09-15 176 1 10 0
2020-09-14 175 1 10 0
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2020-09-10 172 6 10 0
2020-09-09 166 3 10 0
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2020-09-01 152 2 10 0
2020-08-31 150 3 10 0
2020-08-28 147 1 10 0
2020-08-27 146 3 10 0
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2020-08-24 138 1 10 0
2020-08-21 137 1 10 0
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2020-08-18 132 6 10 0
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2020-07-31 117 3 10 0
2020-07-29 114 6 10 0
2020-07-16 108 2 10 0
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2020-04-17 96 1 10 0
2020-04-16 95 1 10 0
2020-04-15 94 3 10 2
2020-04-14 91 1 8 0
2020-04-13 90 2 8 0
2020-04-11 88 5 8 0
2020-04-10 83 16 8 3
2020-04-09 67 1 5 0
2020-04-08 66 6 5 1
2020-04-07 60 4 4 0
2020-04-05 56 2 4 0
2020-04-04 54 3 4 0
2020-04-03 51 3 4 0
2020-04-02 48 4 4 0
2020-04-01 44 1 4 1
2020-03-31 43 1 3 0
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2020-03-29 40 3 3 0
2020-03-28 37 3 3 0
2020-03-27 34 5 3 0
2020-03-25 29 5 3 0
2020-03-24 24 3 3 1
2020-03-23 21 2 2 0
2020-03-21 19 1 2 0
2020-03-19 18 2 2 0
2020-03-17 16 1 2 0
2020-03-16 15 1 2 0
2020-03-15 14 2 2 1
2020-03-14 12 7 1 1
2020-03-13 5 1 0 0
2020-03-12 4 1 0 0
2020-03-09 3 2 0 0

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: 0:02:52.511012