Germany: LK Mansfeld-Südharz (Sachsen-Anhalt)¶

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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 15:36:15 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Mansfeld-Südharz", weeks=5);
2023-03-07T15:36:29.079028 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) 75.6 LK Mansfeld-Südharz, 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 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Mansfeld-Südharz cases daily growth factor Germany-LK Mansfeld-Südharz cases daily growth factor (rolling mean) Germany-LK Mansfeld-Südharz 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 Mansfeld-Südharz deaths daily growth factor Germany-LK Mansfeld-Südharz deaths daily growth factor (rolling mean) Germany-LK Mansfeld-Südharz estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Mansfeld-Südharz doubling time cases (rolling mean) 0.00 13.23 26.46 39.70 daily change Germany-LK Mansfeld-Südharz new cases (rolling 7d mean) Germany-LK Mansfeld-Südharz new cases 0.000 0.265 0.529 0.794 daily change Germany-LK Mansfeld-Südharz new deaths (rolling 7d mean) Germany-LK Mansfeld-Südharz new deaths 0.000 0.336 0.671
In [4]:
overview(country="Germany", subregion="LK Mansfeld-Südharz");
2023-03-07T15:36:49.261631 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 7-day incidence rate (per 100K people) 75.6 LK Mansfeld-Südharz, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Mansfeld-Südharz cases daily growth factor Germany-LK Mansfeld-Südharz cases daily growth factor (rolling mean) Germany-LK Mansfeld-Südharz 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 Mansfeld-Südharz deaths daily growth factor Germany-LK Mansfeld-Südharz deaths daily growth factor (rolling mean) Germany-LK Mansfeld-Südharz 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 8000 cases doubling time [days] Germany-LK Mansfeld-Südharz doubling time cases (rolling mean) Germany-LK Mansfeld-Südharz doubling time deaths (rolling mean) 0.0 264.6 529.3 daily change Germany-LK Mansfeld-Südharz new cases (rolling 7d mean) Germany-LK Mansfeld-Südharz new cases 0.00 3.31 6.62 9.92 13.23 daily change Germany-LK Mansfeld-Südharz new deaths (rolling 7d mean) Germany-LK Mansfeld-Südharz new deaths 0.0 99.7 199.3 299.0 398.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Mansfeld-Südharz", dates="2020-03-15:");
2023-03-07T15:39:30.201584 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 Mansfeld-Südharz LK Mansfeld-Südharz 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 Mansfeld-Südharz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Mansfeld-Südharz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Mansfeld-Südharz")
print(f'Population of country="Germany", subregion="LK Mansfeld-Südharz": {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 Mansfeld-Südharz": 132317 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 62667 17 390 0
2023-03-03 62650 20 390 0
2023-03-02 62630 6 390 0
2023-03-01 62624 15 390 0
2023-02-28 62609 42 390 0
2023-02-27 62567 12 390 0
2023-02-25 62555 10 390 0
2023-02-24 62545 32 390 0
2023-02-23 62513 33 390 0
2023-02-22 62480 19 390 0
2023-02-21 62461 47 390 0
2023-02-20 62414 24 390 1
2023-02-18 62390 6 389 0
2023-02-17 62384 26 389 0
2023-02-16 62358 26 389 0
2023-02-15 62332 16 389 0
2023-02-14 62316 50 389 0
2023-02-13 62266 19 389 0
2023-02-10 62247 13 389 0
2023-02-09 62234 8 389 0
2023-02-08 62226 8 389 0
2023-02-07 62218 33 389 0
2023-02-06 62185 12 389 0
2023-02-03 62173 21 389 0
2023-02-02 62152 20 389 0
2023-02-01 62132 24 389 0
2023-01-31 62108 38 389 0
2023-01-30 62070 17 389 0
2023-01-27 62053 12 389 0
2023-01-26 62041 13 389 0
2023-01-25 62028 13 389 0
2023-01-24 62015 33 389 0
2023-01-23 61982 22 389 0
2023-01-20 61960 10 389 0
2023-01-19 61950 17 389 0
2023-01-18 61933 11 389 0
2023-01-17 61922 24 389 0
2023-01-16 61898 17 389 0
2023-01-13 61881 13 389 0
2023-01-12 61868 9 389 0
2023-01-11 61859 44 389 0
2023-01-10 61815 213 389 0
2023-01-09 61602 16 389 0
2023-01-08 61586 11 389 0
2023-01-05 61575 20 389 0
2023-01-04 61555 22 389 0
2023-01-03 61533 34 389 0
2023-01-02 61499 17 389 0
2022-12-31 61482 12 389 0
2022-12-30 61470 23 389 0
2022-12-29 61447 16 389 0
2022-12-28 61431 40 389 1
2022-12-27 61391 114 388 0
2022-12-23 61277 40 388 0
2022-12-22 61237 74 388 0
2022-12-21 61163 90 388 0
2022-12-20 61073 78 388 0
2022-12-19 60995 92 388 0
2022-12-16 60903 63 388 0
2022-12-15 60840 54 388 0
2022-12-14 60786 57 388 0
2022-12-13 60729 97 388 0
2022-12-12 60632 100 388 0
2022-12-10 60532 5 388 0
2022-12-09 60527 51 388 0
2022-12-08 60476 28 388 0
2022-12-07 60448 88 388 0
2022-12-06 60360 63 388 0
2022-12-05 60297 74 388 0
2022-12-02 60223 54 388 0
2022-12-01 60169 55 388 0
2022-11-30 60114 76 388 0
2022-11-29 60038 110 388 0
2022-11-28 59928 70 388 0
2022-11-25 59858 44 388 0
2022-11-24 59814 61 388 0
2022-11-23 59753 42 388 0
2022-11-22 59711 68 388 0
2022-11-21 59643 44 388 0
2022-11-18 59599 40 388 0
2022-11-17 59559 45 388 0
2022-11-16 59514 58 388 0
2022-11-15 59456 62 388 1
2022-11-14 59394 68 387 0
2022-11-11 59326 27 387 0
2022-11-10 59299 35 387 0
2022-11-09 59264 56 387 0
2022-11-08 59208 86 387 1
2022-11-07 59122 81 386 0
2022-11-04 59041 57 386 1
2022-11-03 58984 61 385 0
2022-11-02 58923 135 385 1
2022-11-01 58788 108 384 0
2022-10-28 58680 69 384 0
2022-10-27 58611 115 384 0
2022-10-26 58496 76 384 0
2022-10-25 58420 197 384 1
2022-10-24 58223 159 383 1
2022-10-21 58064 140 382 0
2022-10-20 57924 182 382 2
2022-10-19 57742 237 380 1
2022-10-18 57505 253 379 0
2022-10-17 57252 376 379 0
2022-10-14 56876 178 379 0
2022-10-13 56698 203 379 0
2022-10-12 56495 205 379 0
2022-10-11 56290 249 379 1
2022-10-10 56041 309 378 2
2022-10-07 55732 331 376 0
2022-10-06 55401 343 376 0
2022-10-05 55058 228 376 0
2022-10-04 54830 230 376 0
2022-10-01 54600 4 376 0
2022-09-30 54596 154 376 0
2022-09-29 54442 121 376 1
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2022-09-26 53825 230 373 1
2022-09-24 53595 1 372 0
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2022-09-22 53528 116 372 0
2022-09-21 53412 97 372 0
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2022-09-09 52743 89 371 0
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2022-09-05 52455 66 371 0
2022-09-02 52389 40 371 1
2022-09-01 52349 68 370 0
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2022-08-11 51268 82 365 0
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2021-02-26 4048 6 168 0
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2021-01-29 3615 29 150 5
2021-01-28 3586 55 145 2
2021-01-27 3531 68 143 4
2021-01-26 3463 71 139 5
2021-01-25 3392 88 134 9
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2021-01-22 3281 51 125 2
2021-01-21 3230 74 123 2
2021-01-20 3156 35 121 1
2021-01-19 3121 10 120 0
2021-01-18 3111 25 120 3
2021-01-17 3086 6 117 0
2021-01-16 3080 45 117 3
2021-01-15 3035 101 114 4
2021-01-14 2934 92 110 4
2021-01-13 2842 112 106 6
2021-01-12 2730 148 100 14
2021-01-11 2582 95 86 6
2021-01-10 2487 38 80 0
2021-01-09 2449 77 80 5
2021-01-08 2372 78 75 3
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2021-01-03 2196 75 67 2
2021-01-02 2121 61 65 4
2021-01-01 2060 129 61 0
2020-12-31 1931 28 61 4
2020-12-30 1903 85 57 5
2020-12-29 1818 74 52 3
2020-12-28 1744 46 49 1
2020-12-27 1698 21 48 2
2020-12-26 1677 24 46 1
2020-12-25 1653 42 45 0
2020-12-24 1611 39 45 1
2020-12-23 1572 88 44 5
2020-12-22 1484 88 39 3
2020-12-21 1396 30 36 2
2020-12-20 1366 78 34 7
2020-12-19 1288 16 27 0
2020-12-18 1272 52 27 0
2020-12-17 1220 35 27 4
2020-12-16 1185 65 23 1
2020-12-15 1120 51 22 2
2020-12-14 1069 6 20 0
2020-12-13 1063 34 20 0
2020-12-12 1029 75 20 2
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2020-12-10 915 25 17 0
2020-12-09 890 16 17 0
2020-12-08 874 32 17 2
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2020-12-06 814 19 14 0
2020-12-05 795 30 14 0
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2020-12-01 677 25 7 0
2020-11-30 652 9 7 0
2020-11-29 643 1 7 0
2020-11-28 642 36 7 0
2020-11-27 606 14 7 0
2020-11-26 592 71 7 4
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2020-11-23 480 1 3 0
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2020-11-18 377 10 3 0
2020-11-17 367 8 3 0
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2020-11-10 303 16 2 0
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2020-11-08 281 18 2 0
2020-11-07 263 6 2 0
2020-11-06 257 20 2 0
2020-11-05 237 14 2 0
2020-11-04 223 11 2 0
2020-11-03 212 13 2 0
2020-11-02 199 3 2 0
2020-11-01 196 3 2 0
2020-10-31 193 2 2 0
2020-10-30 191 15 2 0
2020-10-28 176 14 2 0
2020-10-27 162 7 2 0
2020-10-26 155 1 2 0
2020-10-25 154 11 2 0
2020-10-23 143 19 2 0
2020-10-21 124 11 2 0
2020-10-20 113 8 2 0
2020-10-19 105 1 2 0
2020-10-17 104 2 2 0
2020-10-16 102 6 2 0
2020-10-15 96 5 2 0
2020-10-14 91 1 2 0
2020-10-13 90 1 2 0
2020-10-12 89 1 2 0
2020-10-11 88 1 2 0
2020-10-07 87 1 2 0
2020-09-29 86 1 2 0
2020-09-26 85 1 2 0
2020-09-25 84 1 2 0
2020-09-24 83 1 2 0
2020-09-21 82 1 2 0
2020-09-18 81 1 2 0
2020-09-17 80 1 2 0
2020-09-11 79 3 2 0
2020-09-08 76 1 2 0
2020-09-01 75 1 2 0
2020-08-30 74 1 2 0
2020-08-26 73 1 2 0
2020-08-21 72 1 2 0
2020-08-11 71 1 2 0
2020-08-08 70 1 2 0
2020-07-26 69 1 2 0
2020-07-24 68 1 2 0
2020-07-18 67 2 2 0
2020-07-15 65 2 2 0
2020-07-14 63 3 2 0
2020-07-10 60 5 2 0
2020-07-09 55 1 2 0
2020-06-30 54 1 2 0
2020-06-13 53 1 2 0
2020-06-05 52 1 2 0
2020-06-02 51 1 2 0
2020-05-25 50 1 2 0
2020-05-21 49 2 2 0
2020-05-19 47 1 2 0
2020-05-13 46 1 2 0
2020-05-12 45 2 2 0
2020-05-10 43 1 2 0
2020-05-06 42 4 2 0
2020-05-02 38 1 2 0
2020-04-30 37 1 2 0
2020-04-26 36 1 2 0
2020-04-20 35 1 2 1
2020-04-11 34 1 1 0
2020-04-10 33 1 1 0
2020-04-09 32 1 1 0
2020-04-08 31 2 1 0
2020-04-04 29 1 1 0
2020-04-03 28 2 1 0
2020-04-02 26 3 1 0
2020-03-31 23 4 1 0
2020-03-30 19 4 1 0
2020-03-27 15 1 1 0
2020-03-26 14 2 1 0
2020-03-24 12 1 1 0
2020-03-21 11 5 1 1
2020-03-19 6 3 0 0
2020-03-17 3 2 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: 0:03:47.771964