Germany: LK Regen (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 11:12:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Regen", weeks=5);
2023-03-07T11:13:09.958458 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 68.7 LK Regen, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Regen cases daily growth factor Germany-LK Regen cases daily growth factor (rolling mean) Germany-LK Regen estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Regen deaths daily growth factor Germany-LK Regen deaths daily growth factor (rolling mean) Germany-LK Regen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Regen doubling time cases (rolling mean) 0.00 15.44 30.87 daily change Germany-LK Regen new cases (rolling 7d mean) Germany-LK Regen new cases 0.000 0.386 0.772 daily change Germany-LK Regen new deaths (rolling 7d mean) Germany-LK Regen new deaths 0.00 0.39 0.78
In [4]:
overview(country="Germany", subregion="LK Regen");
2023-03-07T11:13:25.278601 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) 68.7 LK Regen, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Regen cases daily growth factor Germany-LK Regen cases daily growth factor (rolling mean) Germany-LK Regen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Regen deaths daily growth factor Germany-LK Regen deaths daily growth factor (rolling mean) Germany-LK Regen 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 Regen doubling time cases (rolling mean) Germany-LK Regen doubling time deaths (rolling mean) 0.0 154.4 308.7 463.1 daily change Germany-LK Regen new cases (rolling 7d mean) Germany-LK Regen new cases 0.000 1.544 3.087 4.631 6.174 daily change Germany-LK Regen new deaths (rolling 7d mean) Germany-LK Regen new deaths 0.0 71.9 143.9 215.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Regen", dates="2020-03-15:");
2023-03-07T11:16:24.415226 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 Regen LK Regen 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 Regen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Regen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Regen")
print(f'Population of country="Germany", subregion="LK Regen": {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 Regen": 77176 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 42582 11 288 0
2023-03-03 42571 6 288 1
2023-03-02 42565 7 287 0
2023-03-01 42558 12 287 0
2023-02-28 42546 17 287 0
2023-02-27 42529 29 287 1
2023-02-24 42500 20 286 0
2023-02-23 42480 21 286 0
2023-02-22 42459 45 286 0
2023-02-20 42414 38 286 0
2023-02-17 42376 23 286 0
2023-02-16 42353 26 286 0
2023-02-15 42327 21 286 0
2023-02-14 42306 28 286 0
2023-02-13 42278 19 286 0
2023-02-10 42259 15 286 0
2023-02-09 42244 18 286 0
2023-02-08 42226 19 286 0
2023-02-07 42207 42 286 0
2023-02-06 42165 30 286 0
2023-02-03 42135 8 286 0
2023-02-02 42127 16 286 0
2023-02-01 42111 21 286 0
2023-01-31 42090 24 286 0
2023-01-30 42066 19 286 1
2023-01-27 42047 12 285 0
2023-01-26 42035 21 285 0
2023-01-25 42014 17 285 0
2023-01-24 41997 13 285 0
2023-01-23 41984 21 285 0
2023-01-20 41963 9 285 0
2023-01-19 41954 7 285 0
2023-01-18 41947 7 285 0
2023-01-17 41940 10 285 1
2023-01-16 41930 17 284 0
2023-01-13 41913 5 284 0
2023-01-12 41908 10 284 0
2023-01-11 41898 4 284 0
2023-01-10 41894 11 284 0
2023-01-09 41883 20 284 0
2023-01-05 41863 8 284 0
2023-01-04 41855 16 284 0
2023-01-03 41839 24 284 0
2023-01-02 41815 16 284 0
2022-12-30 41799 13 284 0
2022-12-29 41786 19 284 0
2022-12-28 41767 4 284 0
2022-12-27 41763 30 284 1
2022-12-23 41733 9 283 0
2022-12-22 41724 10 283 1
2022-12-21 41714 9 282 1
2022-12-20 41705 15 281 0
2022-12-19 41690 17 281 0
2022-12-16 41673 10 281 0
2022-12-15 41663 8 281 0
2022-12-14 41655 12 281 0
2022-12-13 41643 18 281 0
2022-12-12 41625 16 281 1
2022-12-09 41609 6 280 0
2022-12-08 41603 12 280 0
2022-12-07 41591 10 280 0
2022-12-06 41581 17 280 0
2022-12-05 41564 18 280 0
2022-12-02 41546 8 280 0
2022-12-01 41538 8 280 0
2022-11-30 41530 14 280 0
2022-11-29 41516 17 280 0
2022-11-28 41499 19 280 0
2022-11-25 41480 4 280 0
2022-11-24 41476 8 280 0
2022-11-23 41468 4 280 0
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2022-11-18 41431 3 280 0
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2022-10-24 40999 73 279 1
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2022-10-18 40772 108 278 0
2022-10-17 40664 141 278 0
2022-10-14 40523 79 278 1
2022-10-13 40444 105 277 0
2022-10-12 40339 115 277 0
2022-10-11 40224 141 277 1
2022-10-10 40083 188 276 0
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2022-10-04 39484 199 276 3
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2021-01-03 3133 34 109 2
2021-01-02 3099 6 107 0
2021-01-01 3093 43 107 2
2020-12-31 3050 35 105 0
2020-12-30 3015 61 105 2
2020-12-29 2954 42 103 1
2020-12-28 2912 43 102 0
2020-12-27 2869 9 102 1
2020-12-26 2860 7 101 1
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2020-12-24 2799 35 99 2
2020-12-23 2764 88 97 2
2020-12-22 2676 39 95 1
2020-12-21 2637 16 94 0
2020-12-20 2621 75 94 1
2020-12-19 2546 62 93 3
2020-12-18 2484 50 90 0
2020-12-17 2434 64 90 4
2020-12-16 2370 146 86 6
2020-12-15 2224 14 80 1
2020-12-14 2210 14 79 0
2020-12-13 2196 77 79 3
2020-12-12 2119 94 76 6
2020-12-11 2025 92 70 7
2020-12-10 1933 77 63 6
2020-12-09 1856 111 57 3
2020-12-08 1745 28 54 0
2020-12-07 1717 22 54 7
2020-12-06 1695 45 47 0
2020-12-05 1650 82 47 1
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2020-12-03 1483 95 42 5
2020-12-02 1388 95 37 3
2020-12-01 1293 27 34 2
2020-11-30 1266 14 32 5
2020-11-29 1252 61 27 5
2020-11-28 1191 31 22 0
2020-11-27 1160 54 22 2
2020-11-26 1106 86 20 5
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2020-11-21 926 29 14 0
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2020-11-18 848 68 14 0
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2020-05-10 183 6 11 2
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2020-05-03 163 1 8 0
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2020-04-16 133 3 4 0
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2020-04-08 108 4 2 0
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2020-04-04 91 12 1 0
2020-04-03 79 9 1 0
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2020-03-20 24 1 0 0
2020-03-19 23 3 0 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: 0:03:54.238111