Germany: LK Weißenburg-Gunzenhausen (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 11:36:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Weißenburg-Gunzenhausen", weeks=5);
2023-03-07T11:36:20.184212 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 7-day incidence rate (per 100K people) 68.0 LK Weißenburg-Gunzenhausen, 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 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 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 Weißenburg-Gunzenhausen cases daily growth factor Germany-LK Weißenburg-Gunzenhausen cases daily growth factor (rolling mean) Germany-LK Weißenburg-Gunzenhausen 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 Weißenburg-Gunzenhausen deaths daily growth factor Germany-LK Weißenburg-Gunzenhausen deaths daily growth factor (rolling mean) Germany-LK Weißenburg-Gunzenhausen 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 Weißenburg-Gunzenhausen doubling time cases (rolling mean) 0.00 9.55 19.10 28.66 38.21 daily change Germany-LK Weißenburg-Gunzenhausen new cases (rolling 7d mean) Germany-LK Weißenburg-Gunzenhausen new cases 0.000 0.239 0.478 0.716 0.955 daily change Germany-LK Weißenburg-Gunzenhausen new deaths (rolling 7d mean) Germany-LK Weißenburg-Gunzenhausen new deaths 0.000 0.314 0.628 0.941
In [4]:
overview(country="Germany", subregion="LK Weißenburg-Gunzenhausen");
2023-03-07T11:36:36.695070 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) 68.0 LK Weißenburg-Gunzenhausen, 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 5 10 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 Weißenburg-Gunzenhausen cases daily growth factor Germany-LK Weißenburg-Gunzenhausen cases daily growth factor (rolling mean) Germany-LK Weißenburg-Gunzenhausen 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 Weißenburg-Gunzenhausen deaths daily growth factor Germany-LK Weißenburg-Gunzenhausen deaths daily growth factor (rolling mean) Germany-LK Weißenburg-Gunzenhausen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 10000 20000 cases doubling time [days] Germany-LK Weißenburg-Gunzenhausen doubling time cases (rolling mean) Germany-LK Weißenburg-Gunzenhausen doubling time deaths (rolling mean) 0.0 191.0 382.1 daily change Germany-LK Weißenburg-Gunzenhausen new cases (rolling 7d mean) Germany-LK Weißenburg-Gunzenhausen new cases 0.00 4.78 9.55 daily change Germany-LK Weißenburg-Gunzenhausen new deaths (rolling 7d mean) Germany-LK Weißenburg-Gunzenhausen new deaths 0.0 58.6 117.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Weißenburg-Gunzenhausen", dates="2020-03-15:");
2023-03-07T11:38:48.652662 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 Weißenburg-Gunzenhausen LK Weißenburg-Gunzenhausen 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 Weißenburg-Gunzenhausen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Weißenburg-Gunzenhausen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Weißenburg-Gunzenhausen")
print(f'Population of country="Germany", subregion="LK Weißenburg-Gunzenhausen": {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 Weißenburg-Gunzenhausen": 95523 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 49973 9 207 0
2023-03-03 49964 7 207 0
2023-03-02 49957 3 207 0
2023-03-01 49954 17 207 0
2023-02-28 49937 29 207 0
2023-02-27 49908 31 207 0
2023-02-24 49877 11 207 0
2023-02-23 49866 22 207 0
2023-02-22 49844 21 207 0
2023-02-21 49823 26 207 0
2023-02-20 49797 33 207 1
2023-02-17 49764 17 206 0
2023-02-16 49747 20 206 0
2023-02-15 49727 12 206 0
2023-02-14 49715 30 206 0
2023-02-13 49685 5 206 0
2023-02-12 49680 8 206 0
2023-02-11 49672 8 206 0
2023-02-10 49664 30 206 0
2023-02-09 49634 16 206 0
2023-02-08 49618 25 206 0
2023-02-07 49593 25 206 0
2023-02-06 49568 45 206 0
2023-02-03 49523 27 206 0
2023-02-02 49496 11 206 0
2023-02-01 49485 21 206 0
2023-01-31 49464 29 206 0
2023-01-30 49435 29 206 0
2023-01-27 49406 12 206 0
2023-01-26 49394 22 206 0
2023-01-25 49372 11 206 0
2023-01-24 49361 30 206 0
2023-01-23 49331 12 206 0
2023-01-20 49319 10 206 0
2023-01-19 49309 8 206 0
2023-01-18 49301 7 206 0
2023-01-17 49294 14 206 0
2023-01-16 49280 17 206 0
2023-01-13 49263 17 206 0
2023-01-12 49246 12 206 0
2023-01-11 49234 15 206 0
2023-01-10 49219 15 206 0
2023-01-09 49204 34 206 0
2023-01-05 49170 3 206 0
2023-01-04 49167 14 206 1
2023-01-03 49153 9 205 0
2023-01-02 49144 21 205 0
2022-12-30 49123 14 205 0
2022-12-29 49109 11 205 0
2022-12-28 49098 17 205 0
2022-12-27 49081 41 205 0
2022-12-23 49040 19 205 1
2022-12-22 49021 21 204 0
2022-12-21 49000 19 204 0
2022-12-20 48981 28 204 0
2022-12-19 48953 36 204 0
2022-12-16 48917 14 204 0
2022-12-15 48903 14 204 0
2022-12-14 48889 16 204 0
2022-12-13 48873 17 204 0
2022-12-12 48856 35 204 1
2022-12-11 48821 1 203 0
2022-12-09 48820 18 203 0
2022-12-08 48802 15 203 0
2022-12-07 48787 80 203 0
2022-12-06 48707 5 203 0
2022-12-03 48702 1 203 0
2022-12-02 48701 1 203 0
2022-12-01 48700 2 203 0
2022-11-29 48698 23 203 0
2022-11-28 48675 14 203 0
2022-11-27 48661 2 203 0
2022-11-26 48659 7 203 0
2022-11-25 48652 18 203 0
2022-11-24 48634 18 203 0
2022-11-23 48616 13 203 0
2022-11-22 48603 19 203 0
2022-11-21 48584 15 203 1
2022-11-20 48569 2 202 0
2022-11-19 48567 2 202 0
2022-11-18 48565 20 202 0
2022-11-17 48545 8 202 0
2022-11-16 48537 17 202 0
2022-11-15 48520 24 202 0
2022-11-14 48496 16 202 0
2022-11-13 48480 1 202 0
2022-11-12 48479 6 202 0
2022-11-11 48473 8 202 0
2022-11-10 48465 18 202 0
2022-11-09 48447 21 202 0
2022-11-08 48426 25 202 0
2022-11-07 48401 21 202 0
2022-11-06 48380 16 202 0
2022-11-05 48364 2 202 0
2022-11-04 48362 19 202 0
2022-11-03 48343 34 202 1
2022-11-02 48309 32 201 0
2022-11-01 48277 19 201 0
2022-10-31 48258 45 201 0
2022-10-29 48213 10 201 0
2022-10-28 48203 36 201 1
2022-10-27 48167 39 200 0
2022-10-26 48128 44 200 0
2022-10-25 48084 57 200 0
2022-10-24 48027 166 200 0
2022-10-22 47861 1 200 0
2022-10-21 47860 46 200 1
2022-10-20 47814 74 199 1
2022-10-19 47740 78 198 0
2022-10-18 47662 141 198 0
2022-10-17 47521 155 198 0
2022-10-15 47366 1 198 0
2022-10-14 47365 94 198 0
2022-10-13 47271 106 198 0
2022-10-12 47165 105 198 0
2022-10-11 47060 112 198 0
2022-10-10 46948 167 198 0
2022-10-09 46781 56 198 0
2022-10-08 46725 72 198 0
2022-10-07 46653 173 198 1
2022-10-06 46480 154 197 1
2022-10-05 46326 181 196 0
2022-10-04 46145 156 196 2
2022-10-03 45989 55 194 0
2022-10-02 45934 33 194 0
2022-10-01 45901 57 194 0
2022-09-30 45844 87 194 0
2022-09-29 45757 101 194 0
2022-09-28 45656 104 194 0
2022-09-27 45552 158 194 0
2022-09-26 45394 124 194 2
2022-09-25 45270 52 192 0
2022-09-24 45218 9 192 0
2022-09-23 45209 93 192 0
2022-09-22 45116 73 192 0
2022-09-21 45043 118 192 0
2022-09-20 44925 64 192 0
2022-09-19 44861 128 192 0
2022-09-16 44733 88 192 0
2022-09-15 44645 37 192 0
2022-09-14 44608 94 192 1
2022-09-13 44514 126 191 0
2022-09-12 44388 133 191 0
2022-09-11 44255 1 191 0
2022-09-09 44254 41 191 0
2022-09-08 44213 82 191 1
2022-09-07 44131 103 190 0
2022-09-06 44028 124 190 0
2022-09-05 43904 261 190 0
2022-09-03 43643 1 190 0
2022-09-02 43642 108 190 0
2022-09-01 43534 128 190 0
2022-08-31 43406 85 190 0
2022-08-30 43321 133 190 0
2022-08-29 43188 100 190 0
2022-08-28 43088 28 190 0
2022-08-27 43060 28 190 0
2022-08-26 43032 44 190 0
2022-08-25 42988 53 190 0
2022-08-24 42935 57 190 0
2022-08-23 42878 58 190 0
2022-08-22 42820 40 190 0
2022-08-21 42780 10 190 1
2022-08-20 42770 15 189 0
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2022-07-23 41317 32 187 0
2022-07-22 41285 106 187 0
2022-07-21 41179 96 187 0
2022-07-20 41083 154 187 0
2022-07-19 40929 118 187 0
2022-07-18 40811 283 187 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: 0:03:01.319443