Germany: SK Amberg (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 11:36:46 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Amberg", weeks=5);
2023-03-07T11:36:58.891842 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 57.2 SK Amberg, 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 60 80 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-SK Amberg new deaths (rolling 7d mean) Germany-SK Amberg 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 R & growth factor (based on cases) Germany-SK Amberg cases daily growth factor Germany-SK Amberg cases daily growth factor (rolling mean) Germany-SK Amberg 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-SK Amberg deaths daily growth factor Germany-SK Amberg deaths daily growth factor (rolling mean) Germany-SK Amberg 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-SK Amberg doubling time cases (rolling mean) 0.0 8.4 16.8 25.2 33.6 daily change Germany-SK Amberg new cases (rolling 7d mean) Germany-SK Amberg new cases 0.000 0.293 0.586 0.879
In [4]:
overview(country="Germany", subregion="SK Amberg");
2023-03-07T11:37:15.250218 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 2000 2000 7-day incidence rate (per 100K people) 57.2 SK Amberg, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 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-SK Amberg cases daily growth factor Germany-SK Amberg cases daily growth factor (rolling mean) Germany-SK Amberg 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-SK Amberg deaths daily growth factor Germany-SK Amberg deaths daily growth factor (rolling mean) Germany-SK Amberg 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-SK Amberg doubling time cases (rolling mean) Germany-SK Amberg doubling time deaths (rolling mean) 0 42 84 126 168 daily change Germany-SK Amberg new cases (rolling 7d mean) Germany-SK Amberg new cases 0.00 0.84 1.68 2.52 daily change Germany-SK Amberg new deaths (rolling 7d mean) Germany-SK Amberg new deaths 0.00 15.02 30.04 45.06 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Amberg", dates="2020-03-15:");
2023-03-07T11:39:19.064216 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: SK Amberg SK Amberg 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 SK Amberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Amberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Amberg")
print(f'Population of country="Germany", subregion="SK Amberg": {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="SK Amberg": 41994 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 19947 4 87 0
2023-03-04 19943 2 87 0
2023-03-03 19941 2 87 0
2023-03-02 19939 1 87 0
2023-03-01 19938 5 87 0
2023-02-28 19933 10 87 0
2023-02-27 19923 37 87 0
2023-02-26 19886 7 87 0
2023-02-25 19879 5 87 0
2023-02-24 19874 8 87 0
2023-02-23 19866 17 87 0
2023-02-22 19849 13 87 0
2023-02-21 19836 27 87 0
2023-02-20 19809 23 87 0
2023-02-19 19786 4 87 0
2023-02-18 19782 2 87 0
2023-02-17 19780 9 87 0
2023-02-16 19771 9 87 0
2023-02-15 19762 27 87 0
2023-02-14 19735 12 87 0
2023-02-13 19723 19 87 0
2023-02-12 19704 6 87 0
2023-02-11 19698 7 87 0
2023-02-10 19691 9 87 0
2023-02-09 19682 15 87 0
2023-02-08 19667 10 87 0
2023-02-07 19657 8 87 0
2023-02-06 19649 10 87 0
2023-02-05 19639 9 87 0
2023-02-04 19630 6 87 0
2023-02-03 19624 10 87 0
2023-02-02 19614 13 87 0
2023-02-01 19601 12 87 0
2023-01-31 19589 14 87 0
2023-01-30 19575 18 87 0
2023-01-29 19557 4 87 0
2023-01-28 19553 6 87 0
2023-01-27 19547 9 87 0
2023-01-26 19538 7 87 0
2023-01-25 19531 9 87 0
2023-01-24 19522 13 87 0
2023-01-23 19509 7 87 0
2023-01-22 19502 2 87 0
2023-01-21 19500 1 87 0
2023-01-20 19499 4 87 0
2023-01-19 19495 1 87 0
2023-01-18 19494 4 87 0
2023-01-17 19490 12 87 0
2023-01-16 19478 14 87 0
2023-01-15 19464 4 87 0
2023-01-14 19460 1 87 0
2023-01-13 19459 2 87 0
2023-01-12 19457 3 87 0
2023-01-11 19454 2 87 0
2023-01-10 19452 8 87 0
2023-01-09 19444 9 87 0
2023-01-07 19435 5 87 0
2023-01-06 19430 1 87 0
2023-01-05 19429 11 87 0
2023-01-04 19418 6 87 0
2023-01-03 19412 9 87 0
2023-01-02 19403 15 87 0
2023-01-01 19388 4 87 0
2022-12-31 19384 9 87 0
2022-12-30 19375 8 87 0
2022-12-29 19367 20 87 0
2022-12-28 19347 8 87 0
2022-12-27 19339 29 87 0
2022-12-26 19310 3 87 0
2022-12-25 19307 2 87 0
2022-12-24 19305 8 87 0
2022-12-23 19297 14 87 0
2022-12-22 19283 12 87 0
2022-12-21 19271 11 87 0
2022-12-20 19260 10 87 0
2022-12-19 19250 15 87 0
2022-12-18 19235 6 87 0
2022-12-17 19229 1 87 0
2022-12-16 19228 7 87 0
2022-12-15 19221 11 87 0
2022-12-14 19210 10 87 0
2022-12-13 19200 9 87 0
2022-12-12 19191 14 87 1
2022-12-11 19177 4 86 0
2022-12-10 19173 8 86 0
2022-12-09 19165 8 86 0
2022-12-08 19157 7 86 0
2022-12-07 19150 7 86 0
2022-12-06 19143 20 86 0
2022-12-05 19123 24 86 0
2022-12-04 19099 4 86 0
2022-12-03 19095 4 86 1
2022-12-02 19091 9 85 0
2022-12-01 19082 12 85 0
2022-11-30 19070 18 85 0
2022-11-29 19052 18 85 1
2022-11-28 19034 21 84 0
2022-11-27 19013 2 84 0
2022-11-26 19011 9 84 0
2022-11-25 19002 5 84 0
2022-11-24 18997 12 84 0
2022-11-23 18985 13 84 0
2022-11-22 18972 11 84 0
2022-11-21 18961 16 84 0
2022-11-20 18945 5 84 0
2022-11-19 18940 3 84 0
2022-11-18 18937 12 84 0
2022-11-17 18925 4 84 0
2022-11-16 18921 4 84 0
2022-11-15 18917 8 84 0
2022-11-14 18909 7 84 0
2022-11-13 18902 6 84 0
2022-11-12 18896 7 84 0
2022-11-11 18889 20 84 0
2022-11-10 18869 7 84 0
2022-11-09 18862 14 84 0
2022-11-08 18848 17 84 0
2022-11-07 18831 19 84 0
2022-11-06 18812 12 84 0
2022-11-05 18800 4 84 1
2022-11-04 18796 11 83 0
2022-11-03 18785 10 83 0
2022-11-02 18775 18 83 0
2022-11-01 18757 5 83 0
2022-10-31 18752 22 83 0
2022-10-30 18730 11 83 0
2022-10-29 18719 12 83 0
2022-10-28 18707 17 83 0
2022-10-27 18690 23 83 0
2022-10-26 18667 30 83 0
2022-10-25 18637 32 83 0
2022-10-24 18605 35 83 0
2022-10-23 18570 12 83 0
2022-10-22 18558 25 83 0
2022-10-21 18533 31 83 0
2022-10-20 18502 34 83 0
2022-10-19 18468 49 83 0
2022-10-18 18419 59 83 0
2022-10-17 18360 70 83 0
2022-10-16 18290 32 83 0
2022-10-15 18258 42 83 0
2022-10-14 18216 34 83 0
2022-10-13 18182 37 83 0
2022-10-12 18145 59 83 0
2022-10-11 18086 64 83 1
2022-10-10 18022 62 82 0
2022-10-09 17960 33 82 0
2022-10-08 17927 40 82 0
2022-10-07 17887 51 82 0
2022-10-06 17836 62 82 0
2022-10-05 17774 96 82 0
2022-10-04 17678 82 82 0
2022-10-03 17596 34 82 0
2022-10-02 17562 29 82 1
2022-10-01 17533 21 81 0
2022-09-30 17512 58 81 0
2022-09-29 17454 34 81 0
2022-09-28 17420 46 81 0
2022-09-27 17374 44 81 0
2022-09-26 17330 49 81 1
2022-09-25 17281 19 80 0
2022-09-24 17262 8 80 0
2022-09-23 17254 40 80 0
2022-09-22 17214 25 80 0
2022-09-21 17189 24 80 0
2022-09-20 17165 33 80 0
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2022-08-05 16431 34 80 0
2022-08-04 16397 31 80 1
2022-08-03 16366 47 79 0
2022-08-02 16319 69 79 0
2022-08-01 16250 51 79 0
2022-07-31 16199 21 79 1
2022-07-30 16178 15 78 0
2022-07-29 16163 65 78 0
2022-07-28 16098 25 78 0
2022-07-27 16073 65 78 0
2022-07-26 16008 66 78 0
2022-07-25 15942 62 78 0
2022-07-24 15880 44 78 0
2022-07-23 15836 36 78 0
2022-07-22 15800 67 78 1
2022-07-21 15733 65 77 0
2022-07-20 15668 69 77 1
2022-07-19 15599 94 76 0
2022-07-18 15505 66 76 0
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2022-07-16 15402 53 75 0
2022-07-15 15349 62 75 0
2022-07-14 15287 76 75 0
2022-07-13 15211 83 75 1
2022-07-12 15128 104 74 1
2022-07-11 15024 111 73 0
2022-07-10 14913 33 73 0
2022-07-09 14880 39 73 0
2022-07-08 14841 55 73 0
2022-07-07 14786 65 73 0
2022-07-06 14721 94 73 0
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2021-01-07 690 13 8 0
2021-01-06 677 5 8 0
2021-01-02 672 2 8 0
2021-01-01 670 2 8 0
2020-12-31 668 8 8 0
2020-12-30 660 6 8 0
2020-12-29 654 8 8 0
2020-12-28 646 1 8 0
2020-12-27 645 7 8 0
2020-12-25 638 5 8 0
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2020-12-16 577 14 8 0
2020-12-15 563 3 8 0
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2020-12-13 559 1 8 0
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2020-10-15 156 3 4 0
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2020-10-12 150 1 4 0
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2020-10-09 147 1 4 0
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2020-10-07 139 2 4 0
2020-10-06 137 2 4 0
2020-10-05 135 3 4 0
2020-10-03 132 2 4 0
2020-10-02 130 1 4 0
2020-10-01 129 2 4 0
2020-09-30 127 3 4 0
2020-09-29 124 1 4 0
2020-09-28 123 2 4 0
2020-09-26 121 3 4 0
2020-09-25 118 3 4 0
2020-09-24 115 2 4 0
2020-09-21 113 1 4 0
2020-09-19 112 2 4 0
2020-09-16 110 4 4 0
2020-09-09 106 1 4 0
2020-09-07 105 2 4 0
2020-08-31 103 1 4 0
2020-08-30 102 1 4 0
2020-08-18 101 3 4 0
2020-08-17 98 1 4 0
2020-08-14 97 2 4 0
2020-08-11 95 1 4 0
2020-07-24 94 1 4 0
2020-07-18 93 1 4 0
2020-07-15 92 2 4 0
2020-07-14 90 1 4 0
2020-07-02 89 1 4 0
2020-06-17 88 1 4 0
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2020-06-04 84 1 4 0
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2020-05-19 78 1 4 0
2020-05-17 77 1 4 0
2020-05-14 76 1 4 0
2020-05-10 75 1 4 0
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2020-05-06 72 1 4 0
2020-04-30 71 1 4 0
2020-04-25 70 1 4 0
2020-04-22 69 1 4 0
2020-04-20 68 1 4 0
2020-04-19 67 1 4 0
2020-04-18 66 3 4 0
2020-04-17 63 2 4 0
2020-04-15 61 2 4 0
2020-04-13 59 1 4 1
2020-04-11 58 6 3 1
2020-04-10 52 4 2 0
2020-04-08 48 4 2 0
2020-04-06 44 6 2 0
2020-04-05 38 1 2 0
2020-04-03 37 10 2 0
2020-04-02 27 4 2 0
2020-04-01 23 4 2 1
2020-03-30 19 5 1 0
2020-03-29 14 4 1 1
2020-03-27 10 3 0 0
2020-03-24 7 1 0 0
2020-03-22 6 2 0 0
2020-03-21 4 2 0 0
2020-03-16 2 1 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: 0:02:43.741880