Germany: LK Saarpfalz-Kreis (Saarland)¶

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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 15:20:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Saarpfalz-Kreis", weeks=5);
2023-03-07T15:20:24.009446 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 152.5 LK Saarpfalz-Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 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 Saarpfalz-Kreis new deaths (rolling 7d mean) Germany-LK Saarpfalz-Kreis 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Saarpfalz-Kreis cases daily growth factor Germany-LK Saarpfalz-Kreis cases daily growth factor (rolling mean) Germany-LK Saarpfalz-Kreis 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 Saarpfalz-Kreis deaths daily growth factor Germany-LK Saarpfalz-Kreis deaths daily growth factor (rolling mean) Germany-LK Saarpfalz-Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Saarpfalz-Kreis doubling time cases (rolling mean) 0.0 28.2 56.4 84.6 daily change Germany-LK Saarpfalz-Kreis new cases (rolling 7d mean) Germany-LK Saarpfalz-Kreis new cases 0.00 0.21 0.42 0.63 0.84
In [4]:
overview(country="Germany", subregion="LK Saarpfalz-Kreis");
2023-03-07T15:20:39.203382 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) 152.5 LK Saarpfalz-Kreis, 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 0 200 400 600 800 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Saarpfalz-Kreis cases daily growth factor Germany-LK Saarpfalz-Kreis cases daily growth factor (rolling mean) Germany-LK Saarpfalz-Kreis 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 Saarpfalz-Kreis deaths daily growth factor Germany-LK Saarpfalz-Kreis deaths daily growth factor (rolling mean) Germany-LK Saarpfalz-Kreis 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 Saarpfalz-Kreis doubling time cases (rolling mean) Germany-LK Saarpfalz-Kreis doubling time deaths (rolling mean) 0 282 564 846 1128 daily change Germany-LK Saarpfalz-Kreis new cases (rolling 7d mean) Germany-LK Saarpfalz-Kreis new cases 0.000 1.410 2.819 daily change Germany-LK Saarpfalz-Kreis new deaths (rolling 7d mean) Germany-LK Saarpfalz-Kreis new deaths 0.0 46.6 93.2 139.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Saarpfalz-Kreis", dates="2020-03-15:");
2023-03-07T15:22:56.013022 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 Saarpfalz-Kreis LK Saarpfalz-Kreis 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 Saarpfalz-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Saarpfalz-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Saarpfalz-Kreis")
print(f'Population of country="Germany", subregion="LK Saarpfalz-Kreis": {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 Saarpfalz-Kreis": 140960 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 69784 42 221 0
2023-03-03 69742 15 221 0
2023-03-02 69727 33 221 0
2023-03-01 69694 50 221 0
2023-02-28 69644 75 221 0
2023-02-27 69569 112 221 0
2023-02-24 69457 33 221 0
2023-02-23 69424 64 221 0
2023-02-22 69360 35 221 0
2023-02-21 69325 101 221 0
2023-02-17 69224 36 221 0
2023-02-16 69188 26 221 0
2023-02-15 69162 49 221 0
2023-02-14 69113 70 221 0
2023-02-13 69043 37 221 0
2023-02-10 69006 35 221 0
2023-02-09 68971 77 221 0
2023-02-08 68894 5 221 0
2023-02-07 68889 26 221 0
2023-02-06 68863 27 221 0
2023-02-03 68836 23 221 0
2023-02-02 68813 21 221 0
2023-02-01 68792 22 221 0
2023-01-31 68770 35 221 0
2023-01-30 68735 38 221 0
2023-01-27 68697 15 221 0
2023-01-26 68682 15 221 0
2023-01-25 68667 28 221 0
2023-01-24 68639 22 221 0
2023-01-23 68617 23 221 0
2023-01-20 68594 16 221 0
2023-01-19 68578 18 221 0
2023-01-18 68560 21 221 0
2023-01-17 68539 23 221 0
2023-01-16 68516 25 221 0
2023-01-13 68491 13 221 0
2023-01-12 68478 12 221 0
2023-01-11 68466 21 221 0
2023-01-10 68445 34 221 0
2023-01-09 68411 38 221 0
2023-01-06 68373 35 221 0
2023-01-05 68338 30 221 0
2023-01-04 68308 39 221 0
2023-01-03 68269 87 221 0
2023-01-02 68182 67 221 0
2022-12-30 68115 34 221 0
2022-12-29 68081 42 221 0
2022-12-28 68039 83 221 0
2022-12-27 67956 84 221 1
2022-12-23 67872 45 220 0
2022-12-22 67827 37 220 0
2022-12-21 67790 58 220 0
2022-12-20 67732 84 220 0
2022-12-19 67648 70 220 0
2022-12-16 67578 39 220 0
2022-12-15 67539 42 220 0
2022-12-14 67497 57 220 0
2022-12-13 67440 91 220 0
2022-12-12 67349 59 220 0
2022-12-09 67290 52 220 0
2022-12-08 67238 45 220 0
2022-12-07 67193 44 220 0
2022-12-06 67149 78 220 0
2022-12-05 67071 72 220 0
2022-12-02 66999 44 220 0
2022-12-01 66955 55 220 0
2022-11-30 66900 54 220 0
2022-11-29 66846 63 220 0
2022-11-28 66783 61 220 0
2022-11-25 66722 20 220 0
2022-11-24 66702 57 220 0
2022-11-23 66645 55 220 0
2022-11-22 66590 81 220 0
2022-11-21 66509 94 220 1
2022-11-18 66415 51 219 0
2022-11-17 66364 26 219 0
2022-11-16 66338 56 219 0
2022-11-15 66282 83 219 0
2022-11-14 66199 65 219 0
2022-11-11 66134 48 219 0
2022-11-10 66086 53 219 1
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2022-11-07 65845 82 218 0
2022-11-06 65763 1 218 0
2022-11-04 65762 60 218 0
2022-11-03 65702 89 218 0
2022-11-02 65613 165 218 0
2022-10-31 65448 136 218 0
2022-10-28 65312 95 218 0
2022-10-27 65217 118 218 0
2022-10-26 65099 140 218 1
2022-10-25 64959 257 217 1
2022-10-24 64702 211 216 0
2022-10-21 64491 158 216 0
2022-10-20 64333 213 216 0
2022-10-19 64120 314 216 0
2022-10-18 63806 387 216 0
2022-10-17 63419 369 216 0
2022-10-14 63050 257 216 1
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2022-10-12 62387 355 214 1
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2022-10-10 61531 655 212 2
2022-10-08 60876 1 210 0
2022-10-07 60875 393 210 0
2022-10-06 60482 486 210 1
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2022-10-04 59437 441 208 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: 0:03:03.121465