Germany: LK Haßberge (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 10:55:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Haßberge", weeks=5);
2023-03-07T10:55:29.634644 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) 64.1 LK Haßberge, 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 Haßberge cases daily growth factor Germany-LK Haßberge cases daily growth factor (rolling mean) Germany-LK Haßberge 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 Haßberge deaths daily growth factor Germany-LK Haßberge deaths daily growth factor (rolling mean) Germany-LK Haßberge estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Haßberge doubling time cases (rolling mean) 0.00 8.43 16.86 25.29 33.71 daily change Germany-LK Haßberge new cases (rolling 7d mean) Germany-LK Haßberge new cases 0.000 0.211 0.421 0.632 0.843 daily change Germany-LK Haßberge new deaths (rolling 7d mean) Germany-LK Haßberge new deaths 0.000 0.211 0.422 0.633 0.845
In [4]:
overview(country="Germany", subregion="LK Haßberge");
2023-03-07T10:55:48.450798 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) 64.1 LK Haßberge, 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 0 250 500 750 1000 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 Haßberge cases daily growth factor Germany-LK Haßberge cases daily growth factor (rolling mean) Germany-LK Haßberge 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 Haßberge deaths daily growth factor Germany-LK Haßberge deaths daily growth factor (rolling mean) Germany-LK Haßberge 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 Haßberge doubling time cases (rolling mean) Germany-LK Haßberge doubling time deaths (rolling mean) 0 211 421 632 843 daily change Germany-LK Haßberge new cases (rolling 7d mean) Germany-LK Haßberge new cases 0.00 4.21 8.43 daily change Germany-LK Haßberge new deaths (rolling 7d mean) Germany-LK Haßberge new deaths 0.0 37.3 74.6 111.9 149.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Haßberge", dates="2020-03-15:");
2023-03-07T10:58:17.429038 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 Haßberge LK Haßberge 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 Haßberge Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Haßberge")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Haßberge")
print(f'Population of country="Germany", subregion="LK Haßberge": {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 Haßberge": 84284 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 45927 11 179 0
2023-03-03 45916 7 179 0
2023-03-02 45909 9 179 0
2023-03-01 45900 9 179 0
2023-02-28 45891 18 179 0
2023-02-27 45873 42 179 0
2023-02-24 45831 16 179 0
2023-02-23 45815 13 179 0
2023-02-22 45802 22 179 0
2023-02-21 45780 11 179 0
2023-02-20 45769 21 179 0
2023-02-17 45748 9 179 0
2023-02-16 45739 12 179 0
2023-02-15 45727 19 179 1
2023-02-14 45708 17 178 0
2023-02-13 45691 32 178 0
2023-02-10 45659 10 178 0
2023-02-09 45649 13 178 0
2023-02-08 45636 16 178 0
2023-02-07 45620 32 178 0
2023-02-06 45588 39 178 0
2023-02-03 45549 10 178 0
2023-02-02 45539 11 178 0
2023-02-01 45528 15 178 1
2023-01-31 45513 14 177 0
2023-01-30 45499 9 177 0
2023-01-27 45490 9 177 0
2023-01-26 45481 8 177 0
2023-01-25 45473 9 177 0
2023-01-24 45464 22 177 0
2023-01-23 45442 10 177 0
2023-01-20 45432 3 177 1
2023-01-19 45429 10 176 0
2023-01-18 45419 3 176 0
2023-01-17 45416 7 176 0
2023-01-16 45409 9 176 2
2023-01-13 45400 3 174 0
2023-01-12 45397 13 174 0
2023-01-11 45384 8 174 0
2023-01-10 45376 14 174 1
2023-01-09 45362 10 173 0
2023-01-05 45352 7 173 0
2023-01-04 45345 14 173 0
2023-01-03 45331 22 173 0
2023-01-02 45309 18 173 0
2022-12-30 45291 10 173 0
2022-12-29 45281 11 173 0
2022-12-28 45270 18 173 0
2022-12-27 45252 12 173 0
2022-12-23 45240 9 173 0
2022-12-22 45231 21 173 0
2022-12-21 45210 22 173 0
2022-12-20 45188 23 173 0
2022-12-19 45165 21 173 0
2022-12-16 45144 21 173 1
2022-12-15 45123 6 172 0
2022-12-14 45117 16 172 0
2022-12-13 45101 27 172 0
2022-12-12 45074 20 172 1
2022-12-09 45054 10 171 0
2022-12-08 45044 11 171 0
2022-12-07 45033 16 171 0
2022-12-06 45017 28 171 0
2022-12-05 44989 21 171 0
2022-12-02 44968 17 171 0
2022-12-01 44951 6 171 0
2022-11-30 44945 20 171 0
2022-11-29 44925 24 171 0
2022-11-28 44901 32 171 0
2022-11-25 44869 17 171 0
2022-11-24 44852 12 171 1
2022-11-23 44840 31 170 0
2022-11-22 44809 15 170 0
2022-11-21 44794 14 170 0
2022-11-18 44780 15 170 0
2022-11-17 44765 15 170 0
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2022-11-15 44732 37 170 0
2022-11-14 44695 38 170 0
2022-11-11 44657 24 170 1
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2022-11-09 44605 34 169 0
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2022-11-01 44316 1 168 0
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2022-10-27 44234 55 167 0
2022-10-26 44179 73 167 0
2022-10-25 44106 128 167 0
2022-10-24 43978 113 167 0
2022-10-21 43865 70 167 0
2022-10-20 43795 101 167 0
2022-10-19 43694 167 167 0
2022-10-18 43527 168 167 0
2022-10-17 43359 129 167 0
2022-10-14 43230 103 167 0
2022-10-13 43127 83 167 1
2022-10-12 43044 182 166 2
2022-10-11 42862 136 164 0
2022-10-10 42726 148 164 0
2022-10-07 42578 113 164 0
2022-10-06 42465 186 164 0
2022-10-05 42279 203 164 0
2022-10-04 42076 109 164 0
2022-09-30 41967 93 164 0
2022-09-29 41874 118 164 0
2022-09-28 41756 76 164 0
2022-09-27 41680 97 164 0
2022-09-26 41583 63 164 1
2022-09-23 41520 40 163 0
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2022-08-29 40870 22 163 1
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2020-12-27 1692 4 45 2
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2020-12-17 1468 25 38 2
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2020-12-11 1362 16 35 2
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2020-12-07 1253 11 33 1
2020-12-06 1242 13 32 0
2020-12-05 1229 13 32 0
2020-12-04 1216 25 32 0
2020-12-03 1191 42 32 0
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2020-11-30 1093 5 31 0
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2020-11-27 1048 44 28 1
2020-11-26 1004 42 27 0
2020-11-25 962 45 27 2
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2020-11-23 894 9 25 0
2020-11-22 885 20 25 0
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2020-07-21 172 1 6 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: 0:03:09.625563