Germany: LK Neuburg-Schrobenhausen (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 11:13:00 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neuburg-Schrobenhausen", weeks=5);
2023-03-07T11:13:11.819211 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 80 100 100 120 120 7-day incidence rate (per 100K people) 46.7 LK Neuburg-Schrobenhausen, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Neuburg-Schrobenhausen new deaths (rolling 7d mean) Germany-LK Neuburg-Schrobenhausen 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-LK Neuburg-Schrobenhausen cases daily growth factor Germany-LK Neuburg-Schrobenhausen cases daily growth factor (rolling mean) Germany-LK Neuburg-Schrobenhausen 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-LK Neuburg-Schrobenhausen deaths daily growth factor Germany-LK Neuburg-Schrobenhausen deaths daily growth factor (rolling mean) Germany-LK Neuburg-Schrobenhausen 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 Neuburg-Schrobenhausen doubling time cases (rolling mean) 0.00 9.85 19.70 29.55 39.40 daily change Germany-LK Neuburg-Schrobenhausen new cases (rolling 7d mean) Germany-LK Neuburg-Schrobenhausen new cases 0.000 0.324 0.648 0.972
In [4]:
overview(country="Germany", subregion="LK Neuburg-Schrobenhausen");
2023-03-07T11:13:30.205695 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) 46.7 LK Neuburg-Schrobenhausen, 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 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 Neuburg-Schrobenhausen cases daily growth factor Germany-LK Neuburg-Schrobenhausen cases daily growth factor (rolling mean) Germany-LK Neuburg-Schrobenhausen 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-LK Neuburg-Schrobenhausen deaths daily growth factor Germany-LK Neuburg-Schrobenhausen deaths daily growth factor (rolling mean) Germany-LK Neuburg-Schrobenhausen 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 Neuburg-Schrobenhausen doubling time cases (rolling mean) Germany-LK Neuburg-Schrobenhausen doubling time deaths (rolling mean) 0 197 394 591 daily change Germany-LK Neuburg-Schrobenhausen new cases (rolling 7d mean) Germany-LK Neuburg-Schrobenhausen new cases 0.00 1.97 3.94 daily change Germany-LK Neuburg-Schrobenhausen new deaths (rolling 7d mean) Germany-LK Neuburg-Schrobenhausen new deaths 0.0 35.1 70.1 105.2 140.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neuburg-Schrobenhausen", dates="2020-03-15:");
2023-03-07T11:16:10.277940 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 Neuburg-Schrobenhausen LK Neuburg-Schrobenhausen 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 Neuburg-Schrobenhausen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neuburg-Schrobenhausen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neuburg-Schrobenhausen")
print(f'Population of country="Germany", subregion="LK Neuburg-Schrobenhausen": {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 Neuburg-Schrobenhausen": 98503 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 51322 10 165 0
2023-03-03 51312 7 165 0
2023-03-01 51305 22 165 0
2023-02-28 51283 7 165 0
2023-02-27 51276 40 165 0
2023-02-24 51236 6 165 0
2023-02-23 51230 9 165 0
2023-02-22 51221 25 165 0
2023-02-21 51196 7 165 0
2023-02-20 51189 34 165 0
2023-02-17 51155 13 165 0
2023-02-16 51142 24 165 0
2023-02-15 51118 17 165 0
2023-02-14 51101 16 165 0
2023-02-13 51085 37 165 0
2023-02-10 51048 10 165 0
2023-02-09 51038 15 165 0
2023-02-08 51023 34 165 0
2023-02-07 50989 23 165 0
2023-02-06 50966 40 165 0
2023-02-03 50926 13 165 0
2023-02-02 50913 16 165 0
2023-02-01 50897 16 165 0
2023-01-31 50881 25 165 0
2023-01-30 50856 42 165 0
2023-01-27 50814 5 165 0
2023-01-26 50809 15 165 0
2023-01-25 50794 24 165 0
2023-01-24 50770 18 165 0
2023-01-23 50752 26 165 0
2023-01-20 50726 10 165 0
2023-01-19 50716 16 165 0
2023-01-18 50700 9 165 0
2023-01-17 50691 14 165 0
2023-01-16 50677 30 165 0
2023-01-13 50647 4 165 0
2023-01-12 50643 10 165 0
2023-01-11 50633 21 165 1
2023-01-10 50612 22 164 0
2023-01-09 50590 42 164 0
2023-01-05 50548 7 164 0
2023-01-04 50541 10 164 0
2023-01-03 50531 18 164 0
2023-01-02 50513 33 164 0
2022-12-30 50480 15 164 0
2022-12-29 50465 18 164 0
2022-12-28 50447 11 164 0
2022-12-27 50436 45 164 0
2022-12-23 50391 14 164 0
2022-12-22 50377 15 164 0
2022-12-21 50362 11 164 0
2022-12-20 50351 26 164 0
2022-12-19 50325 31 164 0
2022-12-16 50294 10 164 0
2022-12-15 50284 17 164 0
2022-12-14 50267 22 164 0
2022-12-13 50245 31 164 0
2022-12-12 50214 31 164 0
2022-12-09 50183 11 164 0
2022-12-08 50172 21 164 0
2022-12-07 50151 24 164 0
2022-12-06 50127 16 164 0
2022-12-05 50111 37 164 0
2022-12-02 50074 11 164 0
2022-12-01 50063 14 164 0
2022-11-30 50049 16 164 0
2022-11-29 50033 21 164 0
2022-11-28 50012 29 164 0
2022-11-25 49983 9 164 0
2022-11-24 49974 8 164 0
2022-11-23 49966 10 164 0
2022-11-22 49956 28 164 0
2022-11-21 49928 26 164 0
2022-11-18 49902 10 164 0
2022-11-17 49892 11 164 0
2022-11-16 49881 12 164 0
2022-11-15 49869 10 164 0
2022-11-14 49859 23 164 0
2022-11-11 49836 17 164 0
2022-11-10 49819 19 164 0
2022-11-09 49800 30 164 0
2022-11-08 49770 32 164 0
2022-11-07 49738 70 164 0
2022-11-04 49668 16 164 0
2022-11-03 49652 51 164 0
2022-11-02 49601 63 164 0
2022-10-31 49538 68 164 1
2022-10-28 49470 17 163 0
2022-10-27 49453 44 163 0
2022-10-26 49409 73 163 0
2022-10-25 49336 39 163 0
2022-10-24 49297 98 163 0
2022-10-21 49199 56 163 0
2022-10-20 49143 78 163 0
2022-10-19 49065 77 163 0
2022-10-18 48988 119 163 0
2022-10-17 48869 257 163 0
2022-10-14 48612 55 163 0
2022-10-13 48557 119 163 0
2022-10-12 48438 180 163 0
2022-10-11 48258 137 163 0
2022-10-10 48121 301 163 0
2022-10-07 47820 133 163 0
2022-10-06 47687 141 163 0
2022-10-05 47546 213 163 0
2022-10-04 47333 126 163 0
2022-10-02 47207 149 163 1
2022-09-30 47058 67 162 0
2022-09-29 46991 128 162 0
2022-09-28 46863 144 162 0
2022-09-27 46719 109 162 0
2022-09-26 46610 195 162 0
2022-09-23 46415 50 162 0
2022-09-22 46365 92 162 0
2022-09-21 46273 65 162 0
2022-09-20 46208 86 162 0
2022-09-19 46122 150 162 0
2022-09-16 45972 30 162 0
2022-09-15 45942 57 162 0
2022-09-14 45885 61 162 0
2022-09-13 45824 72 162 0
2022-09-12 45752 84 162 0
2022-09-09 45668 24 162 0
2022-09-08 45644 28 162 0
2022-09-07 45616 47 162 0
2022-09-06 45569 70 162 0
2022-09-05 45499 116 162 0
2022-09-02 45383 58 162 0
2022-09-01 45325 50 162 0
2022-08-31 45275 46 162 0
2022-08-30 45229 49 162 0
2022-08-29 45180 85 162 0
2022-08-26 45095 26 162 0
2022-08-25 45069 26 162 0
2022-08-24 45043 44 162 0
2022-08-23 44999 57 162 0
2022-08-22 44942 66 162 0
2022-08-19 44876 25 162 0
2022-08-18 44851 29 162 0
2022-08-17 44822 63 162 0
2022-08-16 44759 31 162 0
2022-08-14 44728 40 162 0
2022-08-13 44688 2 162 0
2022-08-12 44686 18 162 0
2022-08-11 44668 31 162 1
2022-08-10 44637 48 161 0
2022-08-09 44589 45 161 0
2022-08-08 44544 112 161 0
2022-08-05 44432 46 161 0
2022-08-04 44386 59 161 0
2022-08-03 44327 72 161 0
2022-08-02 44255 96 161 0
2022-08-01 44159 221 161 0
2022-07-29 43938 47 161 0
2022-07-28 43891 92 161 0
2022-07-27 43799 122 161 0
2022-07-26 43677 197 161 1
2022-07-25 43480 308 160 0
2022-07-22 43172 63 160 0
2022-07-21 43109 140 160 0
2022-07-20 42969 244 160 0
2022-07-19 42725 165 160 1
2022-07-18 42560 295 159 1
2022-07-17 42265 1 158 0
2022-07-15 42264 117 158 0
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2022-07-08 41286 102 158 0
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2022-07-01 40505 39 158 0
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2022-06-07 38980 59 156 0
2022-06-05 38921 76 156 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: 0:03:21.698039