Germany: LK Meißen (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 15:20:27 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Meißen", weeks=5);
2023-03-07T15:20:36.732011 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 20 20 30 30 40 40 50 50 7-day incidence rate (per 100K people) 12.5 LK Meißen, 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 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 Meißen new deaths (rolling 7d mean) Germany-LK Meißen new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Meißen cases daily growth factor Germany-LK Meißen cases daily growth factor (rolling mean) Germany-LK Meißen 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 Meißen deaths daily growth factor Germany-LK Meißen deaths daily growth factor (rolling mean) Germany-LK Meißen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 30000 cases doubling time [days] Germany-LK Meißen doubling time cases (rolling mean) 0.00 11.97 23.93 daily change Germany-LK Meißen new cases (rolling 7d mean) Germany-LK Meißen new cases 0.000 0.324 0.648 0.972
In [4]:
overview(country="Germany", subregion="LK Meißen");
2023-03-07T15:20:53.583508 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) 12.5 LK Meißen, 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 6 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Meißen cases daily growth factor Germany-LK Meißen cases daily growth factor (rolling mean) Germany-LK Meißen 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 Meißen deaths daily growth factor Germany-LK Meißen deaths daily growth factor (rolling mean) Germany-LK Meißen 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 30000 cases doubling time [days] Germany-LK Meißen doubling time cases (rolling mean) Germany-LK Meißen doubling time deaths (rolling mean) 0 479 957 1436 daily change Germany-LK Meißen new cases (rolling 7d mean) Germany-LK Meißen new cases 0.00 4.79 9.57 14.36 19.15 daily change Germany-LK Meißen new deaths (rolling 7d mean) Germany-LK Meißen new deaths 0 441 883 1324 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Meißen", dates="2020-03-15:");
2023-03-07T15:23:02.821090 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 Meißen LK Meißen 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 Meißen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Meißen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Meißen")
print(f'Population of country="Germany", subregion="LK Meißen": {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 Meißen": 239344 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 118754 2 1049 0
2023-03-04 118752 1 1049 0
2023-03-03 118751 5 1049 0
2023-03-02 118746 4 1049 0
2023-03-01 118742 3 1049 0
2023-02-28 118739 15 1049 0
2023-02-27 118724 7 1049 0
2023-02-25 118717 9 1049 0
2023-02-24 118708 22 1049 0
2023-02-23 118686 14 1049 0
2023-02-22 118672 26 1049 0
2023-02-21 118646 10 1049 0
2023-02-20 118636 13 1049 0
2023-02-19 118623 9 1049 0
2023-02-18 118614 16 1049 0
2023-02-17 118598 4 1049 0
2023-02-16 118594 18 1049 0
2023-02-15 118576 23 1049 0
2023-02-14 118553 16 1049 0
2023-02-13 118537 18 1049 0
2023-02-12 118519 21 1049 0
2023-02-11 118498 1 1049 0
2023-02-10 118497 8 1049 0
2023-02-09 118489 3 1049 0
2023-02-08 118486 16 1049 0
2023-02-07 118470 22 1049 0
2023-02-06 118448 30 1049 0
2023-02-05 118418 1 1049 0
2023-02-04 118417 2 1049 0
2023-02-03 118415 9 1049 0
2023-02-02 118406 12 1049 0
2023-02-01 118394 13 1049 0
2023-01-31 118381 26 1049 0
2023-01-30 118355 30 1049 0
2023-01-28 118325 12 1049 0
2023-01-27 118313 11 1049 0
2023-01-26 118302 11 1049 0
2023-01-25 118291 22 1049 0
2023-01-24 118269 36 1049 0
2023-01-23 118233 18 1049 0
2023-01-22 118215 2 1049 0
2023-01-21 118213 3 1049 0
2023-01-20 118210 4 1049 0
2023-01-19 118206 10 1049 0
2023-01-18 118196 19 1049 0
2023-01-17 118177 23 1049 0
2023-01-16 118154 10 1049 0
2023-01-15 118144 35 1049 2
2023-01-14 118109 3 1047 0
2023-01-13 118106 9 1047 0
2023-01-12 118097 6 1047 0
2023-01-11 118091 15 1047 0
2023-01-10 118076 28 1047 0
2023-01-09 118048 26 1047 0
2023-01-08 118022 4 1047 0
2023-01-07 118018 24 1047 0
2023-01-06 117994 13 1047 0
2023-01-05 117981 19 1047 0
2023-01-04 117962 25 1047 0
2023-01-03 117937 55 1047 0
2023-01-02 117882 184 1047 1
2023-01-01 117698 4 1046 0
2022-12-31 117694 14 1046 0
2022-12-30 117680 26 1046 0
2022-12-29 117654 31 1046 1
2022-12-28 117623 52 1045 1
2022-12-27 117571 52 1044 0
2022-12-26 117519 3 1044 0
2022-12-25 117516 6 1044 1
2022-12-24 117510 20 1043 0
2022-12-23 117490 27 1043 0
2022-12-22 117463 20 1043 0
2022-12-21 117443 51 1043 0
2022-12-20 117392 70 1043 0
2022-12-19 117322 81 1043 0
2022-12-18 117241 6 1043 0
2022-12-17 117235 32 1043 0
2022-12-16 117203 78 1043 0
2022-12-15 117125 84 1043 0
2022-12-14 117041 71 1043 0
2022-12-13 116970 147 1043 1
2022-12-12 116823 123 1042 2
2022-12-11 116700 8 1040 0
2022-12-10 116692 23 1040 0
2022-12-09 116669 77 1040 0
2022-12-08 116592 161 1040 1
2022-12-07 116431 98 1039 0
2022-12-06 116333 116 1039 0
2022-12-05 116217 122 1039 4
2022-12-04 116095 9 1035 0
2022-12-03 116086 50 1035 0
2022-12-02 116036 106 1035 0
2022-12-01 115930 70 1035 0
2022-11-30 115860 65 1035 1
2022-11-29 115795 140 1034 0
2022-11-28 115655 82 1034 0
2022-11-27 115573 7 1034 0
2022-11-26 115566 48 1034 0
2022-11-25 115518 44 1034 0
2022-11-24 115474 88 1034 1
2022-11-23 115386 82 1033 0
2022-11-22 115304 82 1033 1
2022-11-21 115222 59 1032 0
2022-11-20 115163 32 1032 0
2022-11-19 115131 17 1032 0
2022-11-18 115114 73 1032 0
2022-11-17 115041 99 1032 0
2022-11-16 114942 26 1032 0
2022-11-15 114916 55 1032 0
2022-11-14 114861 63 1032 1
2022-11-13 114798 27 1031 0
2022-11-12 114771 11 1031 0
2022-11-11 114760 78 1031 0
2022-11-10 114682 52 1031 0
2022-11-09 114630 69 1031 0
2022-11-08 114561 85 1031 0
2022-11-07 114476 132 1031 0
2022-11-06 114344 23 1031 0
2022-11-05 114321 26 1031 0
2022-11-04 114295 38 1031 0
2022-11-03 114257 94 1031 0
2022-11-02 114163 137 1031 0
2022-11-01 114026 141 1031 0
2022-10-31 113885 107 1031 2
2022-10-30 113778 8 1029 0
2022-10-29 113770 18 1029 0
2022-10-28 113752 76 1029 0
2022-10-27 113676 104 1029 1
2022-10-26 113572 123 1028 2
2022-10-25 113449 186 1026 1
2022-10-24 113263 147 1025 1
2022-10-23 113116 73 1024 4
2022-10-22 113043 18 1020 0
2022-10-21 113025 183 1020 0
2022-10-20 112842 197 1020 0
2022-10-19 112645 178 1020 1
2022-10-18 112467 307 1019 0
2022-10-17 112160 361 1019 4
2022-10-16 111799 22 1015 0
2022-10-15 111777 70 1015 0
2022-10-14 111707 126 1015 0
2022-10-13 111581 196 1015 1
2022-10-12 111385 274 1014 0
2022-10-11 111111 246 1014 0
2022-10-10 110865 348 1014 3
2022-10-09 110517 99 1011 0
2022-10-08 110418 56 1011 0
2022-10-07 110362 314 1011 0
2022-10-06 110048 380 1011 1
2022-10-05 109668 515 1010 4
2022-10-04 109153 278 1006 0
2022-10-03 108875 28 1006 0
2022-10-02 108847 25 1006 0
2022-10-01 108822 66 1006 0
2022-09-30 108756 249 1006 2
2022-09-29 108507 182 1004 0
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2022-09-23 107435 113 1002 0
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2022-08-14 103708 16 995 0
2022-08-13 103692 26 995 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: 0:02:46.438222