Germany: LK Wittmund (Niedersachsen)¶

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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 13:39:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wittmund", weeks=5);
2023-03-07T13:39:16.290183 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 7-day incidence rate (per 100K people) 54.0 LK Wittmund, 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 40 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 Wittmund new deaths (rolling 7d mean) Germany-LK Wittmund new deaths 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 Wittmund cases daily growth factor Germany-LK Wittmund cases daily growth factor (rolling mean) Germany-LK Wittmund 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 Wittmund deaths daily growth factor Germany-LK Wittmund deaths daily growth factor (rolling mean) Germany-LK Wittmund estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Wittmund doubling time cases (rolling mean) 0.00 5.75 11.49 17.24 22.98 daily change Germany-LK Wittmund new cases (rolling 7d mean) Germany-LK Wittmund new cases 0.000 0.244 0.488 0.731 0.975
In [4]:
overview(country="Germany", subregion="LK Wittmund");
2023-03-07T13:39:33.478317 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 4000 4000 7-day incidence rate (per 100K people) 54.0 LK Wittmund, 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 0 250 500 750 1000 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Wittmund cases daily growth factor Germany-LK Wittmund cases daily growth factor (rolling mean) Germany-LK Wittmund 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 Wittmund deaths daily growth factor Germany-LK Wittmund deaths daily growth factor (rolling mean) Germany-LK Wittmund 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 Wittmund doubling time cases (rolling mean) Germany-LK Wittmund doubling time deaths (rolling mean) 0.0 143.6 287.3 430.9 574.5 daily change Germany-LK Wittmund new cases (rolling 7d mean) Germany-LK Wittmund new cases 0.000 1.149 2.298 3.447 daily change Germany-LK Wittmund new deaths (rolling 7d mean) Germany-LK Wittmund new deaths 0.00 15.16 30.31 45.47 60.63 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wittmund", dates="2020-03-15:");
2023-03-07T13:41:36.129535 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 1000 1000 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Wittmund LK Wittmund 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Wittmund Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wittmund")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wittmund")
print(f'Population of country="Germany", subregion="LK Wittmund": {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 Wittmund": 57455 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 28636 4 92 0
2023-03-03 28632 2 92 0
2023-03-02 28630 1 92 0
2023-03-01 28629 4 92 0
2023-02-28 28625 20 92 0
2023-02-27 28605 19 92 0
2023-02-24 28586 1 92 0
2023-02-23 28585 5 92 0
2023-02-22 28580 15 92 0
2023-02-21 28565 15 92 0
2023-02-20 28550 18 92 0
2023-02-17 28532 2 92 0
2023-02-16 28530 11 92 0
2023-02-15 28519 11 92 0
2023-02-14 28508 10 92 0
2023-02-13 28498 18 92 0
2023-02-11 28480 1 92 0
2023-02-10 28479 5 92 0
2023-02-09 28474 5 92 0
2023-02-08 28469 5 92 0
2023-02-07 28464 13 92 0
2023-02-06 28451 25 92 0
2023-02-03 28426 12 92 0
2023-02-02 28414 7 92 0
2023-02-01 28407 27 92 0
2023-01-31 28380 26 92 0
2023-01-30 28354 26 92 0
2023-01-27 28328 12 92 0
2023-01-26 28316 21 92 0
2023-01-25 28295 16 92 0
2023-01-24 28279 20 92 0
2023-01-23 28259 30 92 0
2023-01-20 28229 15 92 1
2023-01-19 28214 12 91 0
2023-01-18 28202 6 91 0
2023-01-17 28196 20 91 0
2023-01-16 28176 18 91 0
2023-01-13 28158 7 91 0
2023-01-12 28151 16 91 0
2023-01-11 28135 33 91 0
2023-01-10 28102 17 91 0
2023-01-09 28085 45 91 0
2023-01-08 28040 1 91 0
2023-01-07 28039 1 91 0
2023-01-06 28038 4 91 0
2023-01-05 28034 23 91 1
2023-01-04 28011 21 90 1
2023-01-03 27990 40 89 0
2023-01-02 27950 44 89 1
2022-12-30 27906 28 88 0
2022-12-29 27878 43 88 0
2022-12-28 27835 37 88 0
2022-12-27 27798 104 88 0
2022-12-23 27694 13 88 0
2022-12-22 27681 57 88 0
2022-12-21 27624 62 88 2
2022-12-20 27562 47 86 1
2022-12-19 27515 115 85 2
2022-12-16 27400 22 83 0
2022-12-15 27378 57 83 0
2022-12-14 27321 36 83 1
2022-12-13 27285 70 82 1
2022-12-12 27215 85 81 0
2022-12-09 27130 36 81 1
2022-12-08 27094 14 80 0
2022-12-07 27080 53 80 0
2022-12-06 27027 60 80 0
2022-12-05 26967 74 80 0
2022-12-02 26893 16 80 0
2022-12-01 26877 38 80 0
2022-11-30 26839 45 80 0
2022-11-29 26794 63 80 0
2022-11-28 26731 114 80 0
2022-11-25 26617 2 80 0
2022-11-24 26615 39 80 0
2022-11-23 26576 23 80 0
2022-11-22 26553 45 80 0
2022-11-21 26508 87 80 0
2022-11-18 26421 15 80 0
2022-11-17 26406 33 80 0
2022-11-16 26373 49 80 0
2022-11-15 26324 32 80 0
2022-11-14 26292 64 80 0
2022-11-11 26228 11 80 0
2022-11-10 26217 34 80 0
2022-11-09 26183 49 80 0
2022-11-08 26134 20 80 0
2022-11-07 26114 77 80 1
2022-11-04 26037 27 79 0
2022-11-03 26010 48 79 0
2022-11-02 25962 49 79 0
2022-11-01 25913 108 79 1
2022-10-29 25805 1 78 0
2022-10-28 25804 36 78 0
2022-10-27 25768 90 78 1
2022-10-26 25678 53 77 1
2022-10-25 25625 27 76 0
2022-10-24 25598 140 76 0
2022-10-21 25458 40 76 0
2022-10-20 25418 85 76 0
2022-10-19 25333 99 76 1
2022-10-18 25234 41 75 0
2022-10-17 25193 131 75 0
2022-10-15 25062 1 75 0
2022-10-14 25061 31 75 0
2022-10-13 25030 67 75 0
2022-10-12 24963 82 75 0
2022-10-11 24881 81 75 0
2022-10-10 24800 174 75 0
2022-10-08 24626 1 75 0
2022-10-07 24625 46 75 0
2022-10-06 24579 79 75 0
2022-10-05 24500 83 75 0
2022-10-04 24417 188 75 0
2022-09-30 24229 22 75 0
2022-09-29 24207 62 75 0
2022-09-28 24145 26 75 0
2022-09-27 24119 57 75 0
2022-09-26 24062 48 75 0
2022-09-24 24014 1 75 0
2022-09-23 24013 26 75 0
2022-09-22 23987 23 75 0
2022-09-21 23964 27 75 0
2022-09-20 23937 32 75 0
2022-09-19 23905 43 75 0
2022-09-16 23862 18 75 0
2022-09-15 23844 21 75 0
2022-09-14 23823 40 75 0
2022-09-13 23783 23 75 0
2022-09-12 23760 49 75 1
2022-09-09 23711 10 74 0
2022-09-08 23701 24 74 0
2022-09-07 23677 19 74 0
2022-09-06 23658 32 74 0
2022-09-05 23626 63 74 0
2022-09-02 23563 6 74 0
2022-09-01 23557 17 74 0
2022-08-31 23540 33 74 0
2022-08-30 23507 21 74 0
2022-08-29 23486 60 74 1
2022-08-26 23426 7 73 0
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2022-08-18 23253 24 73 0
2022-08-17 23229 34 73 0
2022-08-16 23195 34 73 0
2022-08-15 23161 56 73 1
2022-08-12 23105 22 72 0
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2022-08-10 23056 35 72 0
2022-08-09 23021 47 72 0
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2022-08-03 22807 65 72 2
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2022-08-01 22684 177 70 0
2022-07-29 22507 36 70 0
2022-07-28 22471 125 70 0
2022-07-27 22346 169 70 1
2022-07-26 22177 83 69 0
2022-07-25 22094 440 69 0
2022-07-22 21654 149 69 0
2022-07-21 21505 231 69 0
2022-07-20 21274 288 69 2
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2022-07-10 19136 1 65 0
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2022-05-17 16391 50 62 0
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2022-05-15 16233 2 62 0
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2022-05-12 16193 59 62 0
2022-05-11 16134 118 62 0
2022-05-10 16016 86 62 0
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2022-05-06 15806 97 62 0
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2022-04-25 14534 493 61 1
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2022-04-21 13736 301 60 1
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2021-03-09 852 4 31 0
2021-03-08 848 1 31 0
2021-03-06 847 3 31 0
2021-03-05 844 1 31 0
2021-03-04 843 2 31 1
2021-03-03 841 2 30 0
2021-03-01 839 3 30 0
2021-02-26 836 2 30 0
2021-02-25 834 10 30 0
2021-02-24 824 3 30 0
2021-02-22 821 4 30 0
2021-02-20 817 1 30 0
2021-02-19 816 7 30 0
2021-02-18 809 4 30 0
2021-02-17 805 5 30 0
2021-02-16 800 2 30 0
2021-02-12 798 5 30 0
2021-02-11 793 3 30 0
2021-02-10 790 2 30 0
2021-02-09 788 3 30 0
2021-02-08 785 2 30 0
2021-02-06 783 2 30 0
2021-02-05 781 6 30 0
2021-02-04 775 4 30 0
2021-02-03 771 11 30 0
2021-02-02 760 7 30 0
2021-02-01 753 1 30 0
2021-01-31 752 3 30 1
2021-01-30 749 2 29 0
2021-01-29 747 9 29 1
2021-01-28 738 12 28 0
2021-01-27 726 16 28 0
2021-01-26 710 9 28 3
2021-01-25 701 10 25 0
2021-01-24 691 5 25 0
2021-01-23 686 4 25 0
2021-01-22 682 6 25 0
2021-01-21 676 10 25 0
2021-01-20 666 16 25 1
2021-01-19 650 5 24 0
2021-01-18 645 3 24 0
2021-01-16 642 5 24 0
2021-01-15 637 17 24 1
2021-01-14 620 12 23 0
2021-01-13 608 34 23 4
2021-01-12 574 12 19 3
2021-01-11 562 2 16 0
2021-01-10 560 5 16 0
2021-01-09 555 21 16 0
2021-01-08 534 11 16 0
2021-01-07 523 16 16 1
2021-01-06 507 25 15 0
2021-01-05 482 9 15 1
2021-01-04 473 10 14 2
2021-01-03 463 15 12 3
2020-12-31 448 7 9 0
2020-12-30 441 13 9 0
2020-12-29 428 7 9 0
2020-12-28 421 3 9 0
2020-12-27 418 2 9 0
2020-12-25 416 6 9 1
2020-12-23 410 9 8 0
2020-12-22 401 5 8 0
2020-12-21 396 6 8 0
2020-12-20 390 1 8 0
2020-12-19 389 4 8 0
2020-12-18 385 2 8 0
2020-12-17 383 4 8 0
2020-12-16 379 4 8 0
2020-12-15 375 3 8 0
2020-12-13 372 3 8 0
2020-12-12 369 4 8 0
2020-12-11 365 2 8 1
2020-12-10 363 5 7 0
2020-12-09 358 15 7 0
2020-12-08 343 3 7 1
2020-12-06 340 3 6 0
2020-12-05 337 11 6 0
2020-12-04 326 6 6 0
2020-12-03 320 7 6 0
2020-12-02 313 5 6 0
2020-12-01 308 5 6 0
2020-11-30 303 1 6 0
2020-11-28 302 12 6 0
2020-11-27 290 4 6 0
2020-11-26 286 9 6 1
2020-11-25 277 16 5 0
2020-11-24 261 6 5 1
2020-11-23 255 2 4 0
2020-11-22 253 4 4 0
2020-11-21 249 6 4 0
2020-11-20 243 2 4 0
2020-11-19 241 14 4 0
2020-11-17 227 1 4 0
2020-11-15 226 1 4 0
2020-11-14 225 5 4 0
2020-11-13 220 7 4 0
2020-11-12 213 15 4 0
2020-11-11 198 6 4 0
2020-11-10 192 3 4 2
2020-11-09 189 7 2 0
2020-11-08 182 7 2 0
2020-11-07 175 4 2 0
2020-11-06 171 7 2 0
2020-11-05 164 12 2 0
2020-11-04 152 7 2 0
2020-11-03 145 5 2 0
2020-11-02 140 3 2 1
2020-11-01 137 1 1 0
2020-10-31 136 13 1 0
2020-10-30 123 10 1 0
2020-10-29 113 8 1 0
2020-10-28 105 2 1 0
2020-10-27 103 6 1 0
2020-10-26 97 1 1 0
2020-10-25 96 2 1 0
2020-10-24 94 1 1 0
2020-10-23 93 3 1 0
2020-10-22 90 2 1 0
2020-10-21 88 1 1 0
2020-10-20 87 1 1 0
2020-10-19 86 1 1 0
2020-10-13 85 1 1 0
2020-10-08 84 2 1 0
2020-10-06 82 1 1 0
2020-10-04 81 1 1 0
2020-10-03 80 3 1 0
2020-10-01 77 6 1 0
2020-09-30 71 2 1 0
2020-09-29 69 1 1 0
2020-09-26 68 3 1 0
2020-09-25 65 1 1 0
2020-09-24 64 5 1 1
2020-09-23 59 1 0 0
2020-09-21 58 2 0 0
2020-09-17 56 1 0 0
2020-09-14 55 2 0 0
2020-09-10 53 2 0 0
2020-09-09 51 1 0 0
2020-08-29 50 1 0 0
2020-08-28 49 1 0 0
2020-08-27 48 1 0 0
2020-08-19 47 3 0 0
2020-08-14 44 1 0 0
2020-08-12 43 1 0 0
2020-07-07 42 1 0 0
2020-06-03 41 1 0 0
2020-05-29 40 1 0 0
2020-05-28 39 1 0 0
2020-05-22 38 1 0 0
2020-05-21 37 2 0 0
2020-05-20 35 7 0 0
2020-05-17 28 1 0 0
2020-05-06 27 1 0 0
2020-05-02 26 1 0 0
2020-04-30 25 1 0 0
2020-04-19 24 2 0 0
2020-04-16 22 1 0 0
2020-04-13 21 1 0 0
2020-04-07 20 4 0 0
2020-04-02 16 1 0 0
2020-03-30 15 1 0 0
2020-03-28 14 2 0 0
2020-03-27 12 4 0 0
2020-03-26 8 1 0 0
2020-03-25 7 2 0 0
2020-03-23 5 1 0 0
2020-03-18 4 1 0 0
2020-03-14 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: 0:02:39.600932