Germany: LK Ludwigslust-Parchim (Mecklenburg-Vorpommern)¶

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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 12:52:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ludwigslust-Parchim", weeks=5);
2023-03-07T12:52:23.065390 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 7-day incidence rate (per 100K people) 58.5 LK Ludwigslust-Parchim, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 0.4 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 R & growth factor (based on cases) Germany-LK Ludwigslust-Parchim cases daily growth factor Germany-LK Ludwigslust-Parchim cases daily growth factor (rolling mean) Germany-LK Ludwigslust-Parchim 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 Ludwigslust-Parchim deaths daily growth factor Germany-LK Ludwigslust-Parchim deaths daily growth factor (rolling mean) Germany-LK Ludwigslust-Parchim 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-LK Ludwigslust-Parchim doubling time cases (rolling mean) 0.00 21.19 42.38 daily change Germany-LK Ludwigslust-Parchim new cases (rolling 7d mean) Germany-LK Ludwigslust-Parchim new cases 0.000 0.212 0.424 0.636 0.848 daily change Germany-LK Ludwigslust-Parchim new deaths (rolling 7d mean) Germany-LK Ludwigslust-Parchim new deaths 0.000 0.294 0.588 0.882
In [4]:
overview(country="Germany", subregion="LK Ludwigslust-Parchim");
2023-03-07T12:52:39.532710 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) 58.5 LK Ludwigslust-Parchim, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Ludwigslust-Parchim cases daily growth factor Germany-LK Ludwigslust-Parchim cases daily growth factor (rolling mean) Germany-LK Ludwigslust-Parchim 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 Ludwigslust-Parchim deaths daily growth factor Germany-LK Ludwigslust-Parchim deaths daily growth factor (rolling mean) Germany-LK Ludwigslust-Parchim estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Ludwigslust-Parchim doubling time cases (rolling mean) Germany-LK Ludwigslust-Parchim doubling time deaths (rolling mean) 0 424 848 1271 daily change Germany-LK Ludwigslust-Parchim new cases (rolling 7d mean) Germany-LK Ludwigslust-Parchim new cases 0.00 4.24 8.48 12.71 16.95 daily change Germany-LK Ludwigslust-Parchim new deaths (rolling 7d mean) Germany-LK Ludwigslust-Parchim new deaths 0.0 129.2 258.3 387.5 516.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ludwigslust-Parchim", dates="2020-03-15:");
2023-03-07T12:55:05.101859 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 Ludwigslust-Parchim LK Ludwigslust-Parchim 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 Ludwigslust-Parchim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ludwigslust-Parchim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ludwigslust-Parchim")
print(f'Population of country="Germany", subregion="LK Ludwigslust-Parchim": {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 Ludwigslust-Parchim": 211899 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 99314 23 558 0
2023-03-04 99291 1 558 0
2023-03-03 99290 7 558 0
2023-03-02 99283 13 558 0
2023-03-01 99270 20 558 0
2023-02-28 99250 60 558 0
2023-02-27 99190 42 558 0
2023-02-24 99148 23 558 0
2023-02-23 99125 28 558 0
2023-02-22 99097 29 558 0
2023-02-21 99068 46 558 0
2023-02-20 99022 24 558 0
2023-02-17 98998 21 558 0
2023-02-16 98977 16 558 0
2023-02-15 98961 43 558 0
2023-02-14 98918 15 558 0
2023-02-13 98903 22 558 0
2023-02-10 98881 23 558 1
2023-02-09 98858 26 557 0
2023-02-08 98832 27 557 0
2023-02-07 98805 38 557 0
2023-02-06 98767 28 557 0
2023-02-03 98739 31 557 0
2023-02-02 98708 27 557 0
2023-02-01 98681 20 557 0
2023-01-31 98661 37 557 0
2023-01-30 98624 27 557 0
2023-01-27 98597 15 557 0
2023-01-26 98582 15 557 0
2023-01-25 98567 30 557 0
2023-01-24 98537 39 557 0
2023-01-23 98498 22 557 0
2023-01-20 98476 9 557 0
2023-01-19 98467 25 557 0
2023-01-18 98442 19 557 0
2023-01-17 98423 45 557 0
2023-01-16 98378 23 557 0
2023-01-13 98355 30 557 0
2023-01-12 98325 31 557 1
2023-01-11 98294 39 556 0
2023-01-10 98255 73 556 0
2023-01-09 98182 68 556 1
2023-01-06 98114 50 555 0
2023-01-05 98064 62 555 1
2023-01-04 98002 88 554 1
2023-01-03 97914 143 553 1
2023-01-02 97771 74 552 1
2022-12-30 97697 77 551 4
2022-12-29 97620 129 547 0
2022-12-28 97491 185 547 3
2022-12-27 97306 162 544 4
2022-12-23 97144 101 540 2
2022-12-22 97043 134 538 2
2022-12-21 96909 161 536 2
2022-12-20 96748 209 534 0
2022-12-19 96539 154 534 4
2022-12-16 96385 136 530 1
2022-12-15 96249 132 529 1
2022-12-14 96117 176 528 2
2022-12-13 95941 221 526 3
2022-12-12 95720 128 523 2
2022-12-09 95592 119 521 1
2022-12-08 95473 124 520 2
2022-12-07 95349 170 518 0
2022-12-06 95179 241 518 1
2022-12-05 94938 148 517 3
2022-12-02 94790 107 514 0
2022-12-01 94683 119 514 0
2022-11-30 94564 105 514 2
2022-11-29 94459 259 512 0
2022-11-28 94200 117 512 0
2022-11-25 94083 90 512 0
2022-11-24 93993 93 512 0
2022-11-23 93900 103 512 1
2022-11-22 93797 188 511 1
2022-11-21 93609 72 510 2
2022-11-18 93537 72 508 1
2022-11-17 93465 89 507 0
2022-11-16 93376 76 507 0
2022-11-15 93300 172 507 1
2022-11-14 93128 96 506 0
2022-11-11 93032 105 506 0
2022-11-10 92927 100 506 0
2022-11-09 92827 107 506 0
2022-11-08 92720 218 506 1
2022-11-07 92502 111 505 1
2022-11-04 92391 139 504 0
2022-11-03 92252 195 504 0
2022-11-02 92057 299 504 0
2022-11-01 91758 194 504 2
2022-10-28 91564 132 502 3
2022-10-27 91432 177 499 1
2022-10-26 91255 223 498 0
2022-10-25 91032 432 498 0
2022-10-24 90600 236 498 2
2022-10-21 90364 191 496 0
2022-10-20 90173 210 496 3
2022-10-19 89963 272 493 0
2022-10-18 89691 601 493 1
2022-10-17 89090 267 492 1
2022-10-14 88823 185 491 0
2022-10-13 88638 188 491 0
2022-10-12 88450 232 491 1
2022-10-11 88218 406 490 1
2022-10-10 87812 254 489 2
2022-10-07 87558 227 487 0
2022-10-06 87331 326 487 1
2022-10-05 87005 542 486 1
2022-10-04 86463 251 485 1
2022-09-30 86212 182 484 0
2022-09-29 86030 201 484 0
2022-09-28 85829 218 484 0
2022-09-27 85611 308 484 1
2022-09-26 85303 195 483 0
2022-09-23 85108 107 483 0
2022-09-22 85001 136 483 2
2022-09-21 84865 121 481 0
2022-09-20 84744 243 481 0
2022-09-19 84501 168 481 0
2022-09-16 84333 121 481 0
2022-09-15 84212 135 481 0
2022-09-14 84077 157 481 0
2022-09-13 83920 237 481 1
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2022-09-09 83547 93 478 0
2022-09-08 83454 105 478 0
2022-09-07 83349 106 478 0
2022-09-06 83243 198 478 2
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2022-09-02 82933 80 476 1
2022-09-01 82853 105 475 0
2022-08-31 82748 110 475 0
2022-08-30 82638 231 475 2
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2022-08-22 81633 116 470 0
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2022-08-18 81403 120 470 0
2022-08-17 81283 133 470 2
2022-08-16 81150 291 468 0
2022-08-15 80859 139 468 0
2022-08-12 80720 127 468 0
2022-08-11 80593 104 468 0
2022-08-10 80489 166 468 0
2022-08-09 80323 319 468 0
2022-08-08 80004 142 468 1
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2021-01-05 2042 137 81 7
2021-01-04 1905 12 74 1
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2021-01-02 1890 6 73 1
2021-01-01 1884 6 72 1
2020-12-31 1878 52 71 1
2020-12-30 1826 51 70 2
2020-12-29 1775 129 68 18
2020-12-28 1646 11 50 2
2020-12-27 1635 2 48 1
2020-12-26 1633 6 47 0
2020-12-25 1627 16 47 2
2020-12-24 1611 32 45 1
2020-12-23 1579 35 44 2
2020-12-22 1544 62 42 5
2020-12-21 1482 6 37 0
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2020-12-19 1465 26 37 1
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2020-12-07 988 9 18 1
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2020-09-23 165 1 3 0
2020-09-22 164 6 3 0
2020-09-19 158 1 3 0
2020-09-18 157 1 3 0
2020-09-17 156 3 3 0
2020-09-15 153 4 3 0
2020-09-11 149 2 3 0
2020-09-09 147 1 3 0
2020-09-04 146 1 3 0
2020-08-28 145 3 3 0
2020-08-26 142 2 3 0
2020-08-17 140 2 3 0
2020-08-11 138 2 3 0
2020-08-08 136 1 3 0
2020-08-07 135 3 3 0
2020-08-06 132 7 3 0
2020-08-05 125 1 3 0
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2020-08-02 121 2 3 0
2020-08-01 119 8 3 0
2020-07-31 111 2 3 0
2020-07-30 109 2 3 0
2020-07-29 107 9 3 1
2020-07-28 98 1 2 0
2020-07-27 97 1 2 0
2020-07-22 96 2 2 0
2020-07-13 94 1 2 0
2020-07-02 93 1 2 0
2020-06-27 92 2 2 0
2020-06-17 90 5 2 0
2020-06-16 85 1 2 0
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2020-05-13 77 1 2 0
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2020-05-06 74 1 2 0
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2020-04-25 67 1 2 0
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2020-03-28 37 5 2 1
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2020-03-26 29 1 1 0
2020-03-25 28 1 1 0
2020-03-24 27 3 1 1
2020-03-21 24 2 0 0
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2020-03-18 15 5 0 0
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2020-03-15 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: 0:03:18.749722