Germany: LK Cuxhaven (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 13:07:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Cuxhaven", weeks=5);
2023-03-07T13:07:34.037025 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 150 150 7-day incidence rate (per 100K people) 54.1 LK Cuxhaven, 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 0.00 0.25 0.50 0.75 1.00 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Cuxhaven cases daily growth factor Germany-LK Cuxhaven cases daily growth factor (rolling mean) Germany-LK Cuxhaven 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 Cuxhaven deaths daily growth factor Germany-LK Cuxhaven deaths daily growth factor (rolling mean) Germany-LK Cuxhaven 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 Cuxhaven doubling time cases (rolling mean) 0.00 19.96 39.92 59.88 79.84 daily change Germany-LK Cuxhaven new cases (rolling 7d mean) Germany-LK Cuxhaven new cases 0.000 0.499 0.998 1.497 1.996 daily change Germany-LK Cuxhaven new deaths (rolling 7d mean) Germany-LK Cuxhaven new deaths 0.000 0.306 0.612 0.917
In [4]:
overview(country="Germany", subregion="LK Cuxhaven");
2023-03-07T13:07:57.508559 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) 54.1 LK Cuxhaven, 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 1 2 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Cuxhaven cases daily growth factor Germany-LK Cuxhaven cases daily growth factor (rolling mean) Germany-LK Cuxhaven 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 Cuxhaven deaths daily growth factor Germany-LK Cuxhaven deaths daily growth factor (rolling mean) Germany-LK Cuxhaven 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 cases doubling time [days] Germany-LK Cuxhaven doubling time cases (rolling mean) Germany-LK Cuxhaven doubling time deaths (rolling mean) 0 399 798 daily change Germany-LK Cuxhaven new cases (rolling 7d mean) Germany-LK Cuxhaven new cases 0.000 1.996 3.992 5.988 daily change Germany-LK Cuxhaven new deaths (rolling 7d mean) Germany-LK Cuxhaven new deaths 0.0 103.8 207.6 311.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Cuxhaven", dates="2020-03-15:");
2023-03-07T13:10:16.132628 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 Cuxhaven LK Cuxhaven 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 Cuxhaven Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Cuxhaven")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Cuxhaven")
print(f'Population of country="Germany", subregion="LK Cuxhaven": {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 Cuxhaven": 199603 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 90554 23 325 0
2023-03-03 90531 15 325 0
2023-03-02 90516 10 325 0
2023-03-01 90506 24 325 0
2023-02-28 90482 36 325 0
2023-02-27 90446 41 325 1
2023-02-24 90405 45 324 0
2023-02-23 90360 29 324 0
2023-02-22 90331 35 324 0
2023-02-21 90296 48 324 0
2023-02-20 90248 50 324 0
2023-02-17 90198 22 324 0
2023-02-16 90176 43 324 0
2023-02-15 90133 53 324 2
2023-02-14 90080 57 322 0
2023-02-13 90023 37 322 0
2023-02-10 89986 27 322 0
2023-02-09 89959 34 322 1
2023-02-08 89925 35 321 0
2023-02-07 89890 67 321 0
2023-02-06 89823 46 321 0
2023-02-03 89777 25 321 0
2023-02-02 89752 50 321 0
2023-02-01 89702 44 321 0
2023-01-31 89658 99 321 1
2023-01-30 89559 78 320 0
2023-01-27 89481 34 320 0
2023-01-26 89447 41 320 0
2023-01-25 89406 67 320 0
2023-01-24 89339 90 320 0
2023-01-23 89249 45 320 1
2023-01-20 89204 24 319 0
2023-01-19 89180 25 319 0
2023-01-18 89155 43 319 0
2023-01-17 89112 37 319 0
2023-01-16 89075 68 319 0
2023-01-13 89007 33 319 0
2023-01-12 88974 35 319 0
2023-01-11 88939 64 319 0
2023-01-10 88875 105 319 0
2023-01-09 88770 89 319 0
2023-01-07 88681 1 319 0
2023-01-06 88680 47 319 1
2023-01-05 88633 61 318 0
2023-01-04 88572 111 318 0
2023-01-03 88461 148 318 1
2023-01-02 88313 143 317 2
2022-12-30 88170 70 315 0
2022-12-29 88100 118 315 0
2022-12-28 87982 166 315 0
2022-12-27 87816 280 315 6
2022-12-26 87536 2 309 0
2022-12-25 87534 2 309 0
2022-12-24 87532 1 309 0
2022-12-23 87531 102 309 1
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2022-12-19 86759 267 306 1
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2022-10-20 79516 249 293 1
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2021-01-04 1780 5 71 2
2021-01-03 1775 5 69 1
2021-01-02 1770 14 68 1
2021-01-01 1756 7 67 0
2020-12-31 1749 43 67 7
2020-12-30 1706 27 60 0
2020-12-29 1679 21 60 1
2020-12-28 1658 5 59 1
2020-12-27 1653 10 58 0
2020-12-25 1643 9 58 0
2020-12-24 1634 20 58 0
2020-12-23 1614 38 58 0
2020-12-22 1576 33 58 1
2020-12-21 1543 6 57 0
2020-12-20 1537 3 57 0
2020-12-19 1534 12 57 0
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2020-12-15 1472 17 55 0
2020-12-14 1455 13 55 1
2020-12-13 1442 10 54 0
2020-12-12 1432 2 54 1
2020-12-11 1430 10 53 0
2020-12-10 1420 52 53 2
2020-12-09 1368 24 51 0
2020-12-08 1344 11 51 1
2020-12-07 1333 6 50 0
2020-12-06 1327 2 50 0
2020-12-05 1325 8 50 0
2020-12-04 1317 13 50 0
2020-12-03 1304 40 50 3
2020-12-02 1264 17 47 2
2020-12-01 1247 12 45 1
2020-11-30 1235 1 44 1
2020-11-29 1234 9 43 0
2020-11-28 1225 9 43 0
2020-11-27 1216 1 43 0
2020-11-26 1215 34 43 5
2020-11-25 1181 14 38 1
2020-11-24 1167 12 37 0
2020-11-23 1155 12 37 0
2020-11-22 1143 11 37 0
2020-11-21 1132 6 37 1
2020-11-20 1126 16 36 0
2020-11-19 1110 18 36 0
2020-11-18 1092 23 36 0
2020-11-17 1069 27 36 1
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2020-11-15 1025 10 35 0
2020-11-14 1015 28 35 1
2020-11-13 987 14 34 0
2020-11-12 973 50 34 3
2020-11-11 923 28 31 1
2020-11-10 895 15 30 1
2020-11-09 880 16 29 1
2020-11-08 864 10 28 0
2020-11-07 854 23 28 0
2020-11-06 831 27 28 1
2020-11-05 804 27 27 0
2020-11-04 777 23 27 0
2020-11-03 754 27 27 0
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2020-11-01 704 29 27 0
2020-10-30 675 27 27 0
2020-10-29 648 21 27 0
2020-10-28 627 49 27 1
2020-10-27 578 16 26 0
2020-10-26 562 10 26 0
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2020-10-23 531 26 26 2
2020-10-22 505 15 24 1
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2020-10-15 475 16 23 1
2020-10-14 459 3 22 0
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2020-06-11 343 7 21 0
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2020-06-08 311 8 21 0
2020-06-05 303 12 21 0
2020-06-04 291 19 21 0
2020-06-03 272 15 21 0
2020-06-02 257 13 21 0
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2020-05-26 205 2 21 1
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2020-05-14 176 7 18 1
2020-05-13 169 6 17 0
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2020-04-28 103 3 9 0
2020-04-27 100 3 9 1
2020-04-26 97 1 8 0
2020-04-24 96 8 8 0
2020-04-22 88 5 8 2
2020-04-21 83 1 6 0
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2020-04-08 72 2 3 0
2020-04-07 70 5 3 1
2020-04-06 65 3 2 0
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2020-03-31 55 1 2 0
2020-03-30 54 5 2 0
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2020-03-24 41 2 0 0
2020-03-23 39 13 0 0
2020-03-20 26 1 0 0
2020-03-19 25 6 0 0
2020-03-18 19 5 0 0
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2020-03-03 4 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: 0:03:09.917792