Germany: SK Chemnitz (Sachsen)¶

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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 15:27:22 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Chemnitz", weeks=5);
2023-03-07T15:27:37.174166 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 7-day incidence rate (per 100K people) 50.2 SK Chemnitz, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Chemnitz cases daily growth factor Germany-SK Chemnitz cases daily growth factor (rolling mean) Germany-SK Chemnitz estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-SK Chemnitz deaths daily growth factor Germany-SK Chemnitz deaths daily growth factor (rolling mean) Germany-SK Chemnitz estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Chemnitz doubling time cases (rolling mean) 0.00 12.16 24.31 36.47 daily change Germany-SK Chemnitz new cases (rolling 7d mean) Germany-SK Chemnitz new cases 0.000 0.486 0.972 1.459 1.945 daily change Germany-SK Chemnitz new deaths (rolling 7d mean) Germany-SK Chemnitz new deaths 0.000 0.219 0.439 0.658 0.877
In [4]:
overview(country="Germany", subregion="SK Chemnitz");
2023-03-07T15:27:52.485364 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) 50.2 SK Chemnitz, 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Chemnitz cases daily growth factor Germany-SK Chemnitz cases daily growth factor (rolling mean) Germany-SK Chemnitz estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-SK Chemnitz deaths daily growth factor Germany-SK Chemnitz deaths daily growth factor (rolling mean) Germany-SK Chemnitz 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-SK Chemnitz doubling time cases (rolling mean) Germany-SK Chemnitz doubling time deaths (rolling mean) 0 486 972 1459 daily change Germany-SK Chemnitz new cases (rolling 7d mean) Germany-SK Chemnitz new cases 0.00 4.86 9.72 daily change Germany-SK Chemnitz new deaths (rolling 7d mean) Germany-SK Chemnitz new deaths 0.0 329.3 658.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Chemnitz", dates="2020-03-15:");
2023-03-07T15:30:25.987803 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 Chemnitz SK Chemnitz 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 Chemnitz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Chemnitz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Chemnitz")
print(f'Population of country="Germany", subregion="SK Chemnitz": {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 Chemnitz": 243105 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 107882 14 919 0
2023-03-04 107868 9 919 0
2023-03-03 107859 11 919 0
2023-03-02 107848 22 919 0
2023-03-01 107826 40 919 0
2023-02-28 107786 26 919 0
2023-02-27 107760 22 919 0
2023-02-25 107738 18 919 0
2023-02-24 107720 16 919 2
2023-02-23 107704 29 917 0
2023-02-22 107675 31 917 0
2023-02-21 107644 23 917 0
2023-02-20 107621 19 917 0
2023-02-19 107602 1 917 0
2023-02-18 107601 12 917 0
2023-02-17 107589 12 917 0
2023-02-16 107577 30 917 0
2023-02-15 107547 23 917 1
2023-02-14 107524 44 916 0
2023-02-13 107480 17 916 1
2023-02-11 107463 22 915 0
2023-02-10 107441 15 915 0
2023-02-09 107426 20 915 0
2023-02-08 107406 24 915 0
2023-02-07 107382 14 915 0
2023-02-06 107368 21 915 0
2023-02-03 107347 7 915 0
2023-02-02 107340 14 915 1
2023-02-01 107326 21 914 0
2023-01-31 107305 33 914 0
2023-01-30 107272 12 914 0
2023-01-28 107260 20 914 1
2023-01-27 107240 11 913 0
2023-01-26 107229 22 913 1
2023-01-25 107207 26 912 0
2023-01-24 107181 16 912 0
2023-01-23 107165 18 912 0
2023-01-22 107147 1 912 0
2023-01-20 107146 16 912 0
2023-01-19 107130 22 912 0
2023-01-18 107108 16 912 0
2023-01-17 107092 29 912 0
2023-01-16 107063 27 912 0
2023-01-13 107036 5 912 0
2023-01-12 107031 29 912 0
2023-01-11 107002 16 912 2
2023-01-10 106986 41 910 0
2023-01-09 106945 20 910 0
2023-01-07 106925 25 910 0
2023-01-06 106900 34 910 0
2023-01-05 106866 38 910 0
2023-01-04 106828 58 910 0
2023-01-03 106770 74 910 0
2023-01-02 106696 47 910 3
2022-12-31 106649 19 907 0
2022-12-30 106630 29 907 1
2022-12-29 106601 54 906 1
2022-12-28 106547 78 905 1
2022-12-27 106469 44 904 0
2022-12-25 106425 42 904 0
2022-12-23 106383 49 904 0
2022-12-22 106334 77 904 0
2022-12-21 106257 60 904 0
2022-12-20 106197 84 904 0
2022-12-19 106113 38 904 1
2022-12-17 106075 86 903 2
2022-12-16 105989 55 901 0
2022-12-15 105934 66 901 1
2022-12-14 105868 131 900 0
2022-12-13 105737 52 900 0
2022-12-12 105685 36 900 1
2022-12-10 105649 99 899 1
2022-12-09 105550 57 898 0
2022-12-08 105493 93 898 0
2022-12-07 105400 88 898 0
2022-12-06 105312 120 898 0
2022-12-05 105192 134 898 2
2022-12-04 105058 2 896 0
2022-12-03 105056 1 896 0
2022-12-02 105055 58 896 0
2022-12-01 104997 91 896 0
2022-11-30 104906 142 896 0
2022-11-29 104764 58 896 2
2022-11-28 104706 43 894 0
2022-11-26 104663 68 894 0
2022-11-25 104595 52 894 0
2022-11-24 104543 60 894 0
2022-11-23 104483 122 894 0
2022-11-22 104361 49 894 1
2022-11-21 104312 58 893 0
2022-11-19 104254 80 893 0
2022-11-18 104174 37 893 1
2022-11-17 104137 145 892 0
2022-11-15 103992 71 892 0
2022-11-14 103921 66 892 2
2022-11-12 103855 74 890 0
2022-11-11 103781 53 890 0
2022-11-10 103728 53 890 0
2022-11-09 103675 158 890 0
2022-11-08 103517 87 890 0
2022-11-07 103430 64 890 0
2022-11-05 103366 70 890 0
2022-11-04 103296 49 890 0
2022-11-03 103247 188 890 0
2022-11-02 103059 134 890 0
2022-11-01 102925 36 890 0
2022-10-31 102889 16 890 0
2022-10-29 102873 115 890 0
2022-10-28 102758 100 890 0
2022-10-27 102658 134 890 1
2022-10-26 102524 169 889 0
2022-10-25 102355 264 889 1
2022-10-24 102091 134 888 1
2022-10-22 101957 110 887 0
2022-10-21 101847 171 887 0
2022-10-20 101676 182 887 2
2022-10-19 101494 281 885 0
2022-10-18 101213 222 885 0
2022-10-17 100991 174 885 1
2022-10-16 100817 2 884 0
2022-10-15 100815 180 884 0
2022-10-14 100635 152 884 0
2022-10-13 100483 321 884 1
2022-10-12 100162 374 883 0
2022-10-11 99788 259 883 0
2022-10-10 99529 229 883 3
2022-10-09 99300 3 880 0
2022-10-08 99297 318 880 0
2022-10-07 98979 171 880 0
2022-10-06 98808 560 880 0
2022-10-05 98248 346 880 0
2022-10-04 97902 67 880 0
2022-10-03 97835 210 880 1
2022-10-02 97625 4 879 0
2022-10-01 97621 192 879 0
2022-09-30 97429 97 879 0
2022-09-29 97332 302 879 1
2022-09-28 97030 312 878 1
2022-09-27 96718 338 877 0
2022-09-26 96380 121 877 0
2022-09-25 96259 3 877 0
2022-09-24 96256 181 877 0
2022-09-23 96075 147 877 0
2022-09-22 95928 198 877 0
2022-09-21 95730 195 877 0
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2022-09-19 95332 85 877 1
2022-09-18 95247 2 876 0
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2022-09-16 95123 62 876 0
2022-09-15 95061 138 876 0
2022-09-14 94923 180 876 0
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2022-09-12 94553 163 875 0
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2022-09-01 93670 124 873 1
2022-08-31 93546 107 872 0
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2022-08-21 92600 1 872 0
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2022-08-19 92597 76 872 0
2022-08-18 92521 121 872 1
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2022-08-12 91850 101 870 0
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2022-08-07 91069 1 868 0
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2022-08-05 91064 98 868 0
2022-08-04 90966 156 868 0
2022-08-03 90810 220 868 0
2022-08-02 90590 212 868 0
2022-08-01 90378 294 868 0
2022-07-31 90084 2 868 0
2022-07-30 90082 1 868 0
2022-07-29 90081 128 868 0
2022-07-28 89953 256 868 1
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2022-07-26 89424 269 866 0
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2022-07-24 88748 1 865 0
2022-07-23 88747 16 865 0
2022-07-22 88731 231 865 3
2022-07-21 88500 223 862 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: 0:04:25.512344