Germany: LK Gifhorn (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 Gifhorn", weeks=5);
2023-03-07T13:07:34.027141 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) 47.2 LK Gifhorn, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Gifhorn cases daily growth factor Germany-LK Gifhorn cases daily growth factor (rolling mean) Germany-LK Gifhorn estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Gifhorn deaths daily growth factor Germany-LK Gifhorn deaths daily growth factor (rolling mean) Germany-LK Gifhorn 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 Gifhorn doubling time cases (rolling mean) 0.00 17.79 35.58 53.38 daily change Germany-LK Gifhorn new cases (rolling 7d mean) Germany-LK Gifhorn new cases 0.000 0.356 0.712 daily change Germany-LK Gifhorn new deaths (rolling 7d mean) Germany-LK Gifhorn new deaths 0.000 0.242 0.484 0.726 0.969
In [4]:
overview(country="Germany", subregion="LK Gifhorn");
2023-03-07T13:07:57.257123 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) 47.2 LK Gifhorn, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Gifhorn cases daily growth factor Germany-LK Gifhorn cases daily growth factor (rolling mean) Germany-LK Gifhorn estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Gifhorn deaths daily growth factor Germany-LK Gifhorn deaths daily growth factor (rolling mean) Germany-LK Gifhorn 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 Gifhorn doubling time cases (rolling mean) Germany-LK Gifhorn doubling time deaths (rolling mean) 0 356 712 1068 daily change Germany-LK Gifhorn new cases (rolling 7d mean) Germany-LK Gifhorn new cases 0.00 3.56 7.12 10.68 daily change Germany-LK Gifhorn new deaths (rolling 7d mean) Germany-LK Gifhorn new deaths 0.0 134.5 269.0 403.5 538.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Gifhorn", dates="2020-03-15:");
2023-03-07T13:10:13.728321 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 Gifhorn LK Gifhorn 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 Gifhorn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Gifhorn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Gifhorn")
print(f'Population of country="Germany", subregion="LK Gifhorn": {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 Gifhorn": 177919 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 82942 10 534 0
2023-03-03 82932 5 534 0
2023-03-02 82927 3 534 0
2023-03-01 82924 29 534 0
2023-02-28 82895 37 534 0
2023-02-27 82858 37 534 0
2023-02-24 82821 26 534 0
2023-02-23 82795 25 534 0
2023-02-22 82770 41 534 0
2023-02-21 82729 38 534 0
2023-02-20 82691 53 534 0
2023-02-17 82638 26 534 0
2023-02-16 82612 52 534 0
2023-02-15 82560 40 534 0
2023-02-14 82520 49 534 1
2023-02-13 82471 45 533 1
2023-02-10 82426 16 532 1
2023-02-09 82410 28 531 0
2023-02-08 82382 26 531 0
2023-02-07 82356 27 531 0
2023-02-06 82329 37 531 1
2023-02-03 82292 16 530 0
2023-02-02 82276 37 530 0
2023-02-01 82239 34 530 0
2023-01-31 82205 69 530 1
2023-01-30 82136 45 529 0
2023-01-27 82091 30 529 0
2023-01-26 82061 45 529 1
2023-01-25 82016 44 528 0
2023-01-24 81972 50 528 1
2023-01-23 81922 49 527 0
2023-01-20 81873 16 527 0
2023-01-19 81857 33 527 0
2023-01-18 81824 51 527 1
2023-01-17 81773 46 526 0
2023-01-16 81727 60 526 1
2023-01-13 81667 45 525 1
2023-01-12 81622 32 524 2
2023-01-11 81590 55 522 0
2023-01-10 81535 59 522 3
2023-01-09 81476 71 519 0
2023-01-06 81405 46 519 1
2023-01-05 81359 57 518 1
2023-01-04 81302 194 517 2
2023-01-03 81108 115 515 1
2023-01-02 80993 174 514 4
2022-12-29 80819 94 510 0
2022-12-28 80725 100 510 2
2022-12-27 80625 152 508 2
2022-12-23 80473 124 506 1
2022-12-22 80349 159 505 5
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2022-12-09 78829 185 476 2
2022-12-08 78644 1 474 0
2022-12-07 78643 111 474 2
2022-12-06 78532 135 472 1
2022-12-05 78397 154 471 2
2022-12-02 78243 94 469 0
2022-12-01 78149 114 469 1
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2022-11-25 77522 97 459 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: 0:03:02.359723