Germany: LK Osterholz (Niedersachsen)¶

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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 13:30:27 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Osterholz", weeks=5);
2023-03-07T13:30:41.596524 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 75.6 LK Osterholz, 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 Osterholz new deaths (rolling 7d mean) Germany-LK Osterholz new deaths 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Osterholz cases daily growth factor Germany-LK Osterholz cases daily growth factor (rolling mean) Germany-LK Osterholz 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 Osterholz deaths daily growth factor Germany-LK Osterholz deaths daily growth factor (rolling mean) Germany-LK Osterholz 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 Osterholz doubling time cases (rolling mean) 0.00 11.51 23.01 34.52 46.02 daily change Germany-LK Osterholz new cases (rolling 7d mean) Germany-LK Osterholz new cases 0.000 0.217 0.435 0.652 0.870
In [4]:
overview(country="Germany", subregion="LK Osterholz");
2023-03-07T13:30:57.610094 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 75.6 LK Osterholz, 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 0 100 200 300 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Osterholz cases daily growth factor Germany-LK Osterholz cases daily growth factor (rolling mean) Germany-LK Osterholz 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 Osterholz deaths daily growth factor Germany-LK Osterholz deaths daily growth factor (rolling mean) Germany-LK Osterholz estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Osterholz doubling time cases (rolling mean) Germany-LK Osterholz doubling time deaths (rolling mean) 0.0 115.1 230.1 345.2 daily change Germany-LK Osterholz new cases (rolling 7d mean) Germany-LK Osterholz new cases 0.000 2.301 4.602 daily change Germany-LK Osterholz new deaths (rolling 7d mean) Germany-LK Osterholz new deaths 0.00 7.05 14.10 21.15 28.20 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Osterholz", dates="2020-03-15:");
2023-03-07T13:33:23.051216 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 Osterholz LK Osterholz 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Osterholz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Osterholz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Osterholz")
print(f'Population of country="Germany", subregion="LK Osterholz": {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 Osterholz": 115054 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 52892 6 116 0
2023-03-05 52886 6 116 0
2023-03-04 52880 10 116 0
2023-03-03 52870 11 116 0
2023-03-02 52859 9 116 0
2023-03-01 52850 18 116 0
2023-02-28 52832 27 116 0
2023-02-27 52805 18 116 0
2023-02-26 52787 5 116 0
2023-02-25 52782 11 116 0
2023-02-24 52771 17 116 0
2023-02-23 52754 22 116 0
2023-02-22 52732 52 116 0
2023-02-21 52680 47 116 0
2023-02-20 52633 14 116 0
2023-02-19 52619 3 116 0
2023-02-18 52616 5 116 0
2023-02-17 52611 15 116 0
2023-02-16 52596 24 116 0
2023-02-15 52572 13 116 0
2023-02-14 52559 32 116 0
2023-02-13 52527 25 116 0
2023-02-12 52502 3 116 0
2023-02-11 52499 11 116 0
2023-02-10 52488 32 116 0
2023-02-09 52456 6 116 0
2023-02-08 52450 19 116 0
2023-02-07 52431 43 116 0
2023-02-06 52388 23 116 0
2023-02-05 52365 2 116 0
2023-02-04 52363 5 116 0
2023-02-03 52358 18 116 0
2023-02-02 52340 42 116 0
2023-02-01 52298 13 116 0
2023-01-31 52285 52 116 0
2023-01-30 52233 17 116 0
2023-01-29 52216 3 116 0
2023-01-28 52213 2 116 0
2023-01-27 52211 24 116 0
2023-01-26 52187 19 116 0
2023-01-25 52168 31 116 0
2023-01-24 52137 29 116 0
2023-01-23 52108 31 116 0
2023-01-22 52077 1 116 0
2023-01-21 52076 1 116 0
2023-01-20 52075 12 116 0
2023-01-19 52063 8 116 0
2023-01-18 52055 14 116 0
2023-01-17 52041 33 116 0
2023-01-16 52008 18 116 0
2023-01-15 51990 7 116 0
2023-01-14 51983 11 116 0
2023-01-13 51972 29 116 1
2023-01-12 51943 18 115 0
2023-01-11 51925 36 115 0
2023-01-10 51889 44 115 0
2023-01-09 51845 24 115 0
2023-01-08 51821 7 115 1
2023-01-07 51814 17 114 0
2023-01-06 51797 37 114 1
2023-01-05 51760 37 113 0
2023-01-04 51723 31 113 0
2023-01-03 51692 75 113 0
2023-01-02 51617 55 113 0
2023-01-01 51562 14 113 0
2022-12-31 51548 23 113 2
2022-12-30 51525 56 111 0
2022-12-29 51469 57 111 0
2022-12-28 51412 105 111 1
2022-12-27 51307 102 110 0
2022-12-26 51205 8 110 0
2022-12-25 51197 16 110 0
2022-12-24 51181 42 110 0
2022-12-23 51139 107 110 0
2022-12-22 51032 71 110 0
2022-12-21 50961 152 110 0
2022-12-20 50809 176 110 0
2022-12-19 50633 39 110 0
2022-12-18 50594 38 110 1
2022-12-17 50556 71 109 0
2022-12-16 50485 83 109 1
2022-12-15 50402 120 108 1
2022-12-14 50282 94 107 0
2022-12-13 50188 116 107 0
2022-12-12 50072 43 107 0
2022-12-11 50029 22 107 0
2022-12-10 50007 53 107 0
2022-12-09 49954 99 107 0
2022-12-08 49855 80 107 0
2022-12-07 49775 67 107 1
2022-12-06 49708 92 106 0
2022-12-05 49616 66 106 0
2022-12-04 49550 16 106 0
2022-12-03 49534 39 106 0
2022-12-02 49495 69 106 0
2022-12-01 49426 63 106 1
2022-11-30 49363 65 105 0
2022-11-29 49298 145 105 0
2022-11-28 49153 32 105 0
2022-11-27 49121 18 105 1
2022-11-26 49103 26 104 0
2022-11-25 49077 40 104 0
2022-11-24 49037 37 104 0
2022-11-23 49000 46 104 0
2022-11-22 48954 60 104 0
2022-11-21 48894 63 104 0
2022-11-20 48831 17 104 0
2022-11-19 48814 9 104 0
2022-11-18 48805 56 104 0
2022-11-17 48749 46 104 0
2022-11-16 48703 47 104 0
2022-11-15 48656 82 104 0
2022-11-14 48574 57 104 0
2022-11-13 48517 18 104 0
2022-11-12 48499 60 104 1
2022-11-11 48439 24 103 0
2022-11-10 48415 59 103 0
2022-11-09 48356 80 103 0
2022-11-08 48276 90 103 1
2022-11-07 48186 76 102 0
2022-11-06 48110 32 102 0
2022-11-05 48078 37 102 0
2022-11-04 48041 104 102 0
2022-11-03 47937 104 102 0
2022-11-02 47833 172 102 0
2022-11-01 47661 77 102 1
2022-10-31 47584 31 101 0
2022-10-30 47553 23 101 0
2022-10-29 47530 28 101 0
2022-10-28 47502 101 101 0
2022-10-27 47401 96 101 0
2022-10-26 47305 113 101 0
2022-10-25 47192 156 101 0
2022-10-24 47036 105 101 0
2022-10-23 46931 78 101 0
2022-10-22 46853 61 101 0
2022-10-21 46792 192 101 2
2022-10-20 46600 123 99 0
2022-10-19 46477 121 99 0
2022-10-18 46356 273 99 0
2022-10-17 46083 136 99 0
2022-10-16 45947 61 99 0
2022-10-15 45886 118 99 0
2022-10-14 45768 175 99 1
2022-10-13 45593 167 98 0
2022-10-12 45426 208 98 0
2022-10-11 45218 237 98 0
2022-10-10 44981 250 98 0
2022-10-09 44731 54 98 0
2022-10-08 44677 62 98 0
2022-10-07 44615 119 98 0
2022-10-06 44496 252 98 1
2022-10-05 44244 301 97 0
2022-10-04 43943 88 97 0
2022-10-03 43855 19 97 0
2022-10-02 43836 50 97 0
2022-10-01 43786 48 97 1
2022-09-30 43738 124 96 0
2022-09-29 43614 146 96 0
2022-09-28 43468 143 96 0
2022-09-27 43325 174 96 0
2022-09-26 43151 58 96 0
2022-09-25 43093 16 96 0
2022-09-24 43077 60 96 0
2022-09-23 43017 111 96 0
2022-09-22 42906 106 96 0
2022-09-21 42800 135 96 0
2022-09-20 42665 113 96 0
2022-09-19 42552 68 96 0
2022-09-18 42484 29 96 0
2022-09-17 42455 63 96 0
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2022-09-12 42024 25 96 0
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2022-09-09 41912 102 96 0
2022-09-08 41810 56 96 0
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2022-09-06 41663 161 96 1
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2022-09-04 41454 20 95 1
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2022-08-30 41209 108 94 0
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2022-08-28 41024 7 94 0
2022-08-27 41017 14 94 0
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2022-08-25 40911 76 94 0
2022-08-24 40835 84 94 0
2022-08-23 40751 181 94 0
2022-08-22 40570 44 94 0
2022-08-21 40526 16 94 0
2022-08-20 40510 53 94 0
2022-08-19 40457 109 94 0
2022-08-18 40348 89 94 0
2022-08-17 40259 61 94 0
2022-08-16 40198 101 94 0
2022-08-15 40097 77 94 1
2022-08-14 40020 27 93 0
2022-08-13 39993 33 93 0
2022-08-12 39960 85 93 0
2022-08-11 39875 82 93 0
2022-08-10 39793 101 93 0
2022-08-09 39692 94 93 0
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2022-08-06 39472 39 93 0
2022-08-05 39433 71 93 0
2022-08-04 39362 88 93 1
2022-08-03 39274 182 92 0
2022-08-02 39092 105 92 0
2022-08-01 38987 115 92 0
2022-07-31 38872 35 92 0
2022-07-30 38837 50 92 1
2022-07-29 38787 88 91 0
2022-07-28 38699 176 91 0
2022-07-27 38523 145 91 0
2022-07-26 38378 283 91 0
2022-07-25 38095 75 91 0
2022-07-24 38020 51 91 0
2022-07-23 37969 114 91 1
2022-07-22 37855 192 90 0
2022-07-21 37663 175 90 0
2022-07-20 37488 236 90 0
2022-07-19 37252 282 90 0
2022-07-18 36970 97 90 0
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2022-07-13 36309 196 90 0
2022-07-12 36113 251 90 0
2022-07-11 35862 133 90 0
2022-07-10 35729 50 90 0
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2020-12-01 967 41 20 6
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2020-03-19 24 2 0 0
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2020-03-14 14 1 0 0
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2020-03-11 10 4 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: 0:03:07.314546