Germany: SK Koblenz (Rheinland-Pfalz)¶

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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 15:04:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Koblenz", weeks=5);
2023-03-07T15:04:17.974527 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 200 200 300 300 7-day incidence rate (per 100K people) 134.6 SK Koblenz, 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-SK Koblenz new deaths (rolling 7d mean) Germany-SK Koblenz new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-SK Koblenz cases daily growth factor Germany-SK Koblenz cases daily growth factor (rolling mean) Germany-SK Koblenz 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-SK Koblenz deaths daily growth factor Germany-SK Koblenz deaths daily growth factor (rolling mean) Germany-SK Koblenz estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-SK Koblenz doubling time cases (rolling mean) 0.00 22.73 45.46 68.18 daily change Germany-SK Koblenz new cases (rolling 7d mean) Germany-SK Koblenz new cases 0.000 0.318 0.635 0.953
In [4]:
overview(country="Germany", subregion="SK Koblenz");
2023-03-07T15:04:32.734274 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 134.6 SK Koblenz, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-SK Koblenz cases daily growth factor Germany-SK Koblenz cases daily growth factor (rolling mean) Germany-SK Koblenz 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-SK Koblenz deaths daily growth factor Germany-SK Koblenz deaths daily growth factor (rolling mean) Germany-SK Koblenz 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-SK Koblenz doubling time cases (rolling mean) Germany-SK Koblenz doubling time deaths (rolling mean) 0.0 113.6 227.3 340.9 454.6 daily change Germany-SK Koblenz new cases (rolling 7d mean) Germany-SK Koblenz new cases 0.000 2.273 4.546 6.818 daily change Germany-SK Koblenz new deaths (rolling 7d mean) Germany-SK Koblenz new deaths 0.0 30.4 60.9 91.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Koblenz", dates="2020-03-15:");
2023-03-07T15:07:05.623878 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: SK Koblenz SK Koblenz 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 SK Koblenz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Koblenz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Koblenz")
print(f'Population of country="Germany", subregion="SK Koblenz": {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="SK Koblenz": 113638 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 51138 5 195 0
2023-03-04 51133 17 195 0
2023-03-03 51116 18 195 0
2023-03-02 51098 31 195 0
2023-03-01 51067 28 195 0
2023-02-28 51039 54 195 0
2023-02-27 50985 64 195 0
2023-02-26 50921 18 195 0
2023-02-25 50903 26 195 0
2023-02-24 50877 57 195 0
2023-02-23 50820 71 195 0
2023-02-22 50749 70 195 0
2023-02-21 50679 61 195 0
2023-02-20 50618 43 195 0
2023-02-19 50575 11 195 0
2023-02-18 50564 14 195 0
2023-02-17 50550 24 195 0
2023-02-16 50526 20 195 0
2023-02-15 50506 39 195 0
2023-02-14 50467 37 195 0
2023-02-13 50430 38 195 0
2023-02-12 50392 10 195 0
2023-02-11 50382 13 195 0
2023-02-10 50369 15 195 0
2023-02-09 50354 23 195 0
2023-02-08 50331 18 195 0
2023-02-07 50313 21 195 0
2023-02-06 50292 39 195 0
2023-02-05 50253 8 195 0
2023-02-04 50245 10 195 0
2023-02-03 50235 17 195 0
2023-02-02 50218 13 195 0
2023-02-01 50205 11 195 0
2023-01-31 50194 16 195 0
2023-01-30 50178 28 195 0
2023-01-29 50150 5 195 0
2023-01-28 50145 5 195 0
2023-01-27 50140 15 195 0
2023-01-26 50125 14 195 0
2023-01-25 50111 15 195 0
2023-01-24 50096 13 195 0
2023-01-23 50083 18 195 0
2023-01-22 50065 3 195 0
2023-01-21 50062 7 195 0
2023-01-20 50055 8 195 0
2023-01-19 50047 9 195 0
2023-01-18 50038 5 195 0
2023-01-17 50033 11 195 0
2023-01-16 50022 24 195 0
2023-01-15 49998 4 195 0
2023-01-14 49994 7 195 0
2023-01-13 49987 9 195 0
2023-01-12 49978 15 195 0
2023-01-11 49963 11 195 0
2023-01-10 49952 21 195 0
2023-01-09 49931 31 195 1
2023-01-08 49900 9 194 0
2023-01-07 49891 12 194 0
2023-01-06 49879 18 194 0
2023-01-05 49861 18 194 0
2023-01-04 49843 30 194 0
2023-01-03 49813 33 194 1
2023-01-02 49780 45 193 0
2023-01-01 49735 8 193 0
2022-12-31 49727 21 193 0
2022-12-30 49706 20 193 0
2022-12-29 49686 36 193 0
2022-12-28 49650 59 193 0
2022-12-27 49591 54 193 0
2022-12-26 49537 26 193 0
2022-12-25 49511 16 193 0
2022-12-24 49495 36 193 0
2022-12-23 49459 50 193 1
2022-12-22 49409 58 192 0
2022-12-21 49351 54 192 0
2022-12-20 49297 51 192 0
2022-12-19 49246 68 192 0
2022-12-18 49178 16 192 0
2022-12-17 49162 21 192 0
2022-12-16 49141 38 192 0
2022-12-15 49103 49 192 0
2022-12-14 49054 46 192 0
2022-12-13 49008 60 192 0
2022-12-12 48948 54 192 0
2022-12-11 48894 12 192 0
2022-12-10 48882 20 192 0
2022-12-09 48862 44 192 0
2022-12-08 48818 43 192 0
2022-12-07 48775 45 192 0
2022-12-06 48730 47 192 0
2022-12-05 48683 53 192 0
2022-12-04 48630 13 192 0
2022-12-03 48617 19 192 0
2022-12-02 48598 26 192 0
2022-12-01 48572 27 192 1
2022-11-30 48545 36 191 0
2022-11-29 48509 39 191 0
2022-11-28 48470 28 191 0
2022-11-27 48442 12 191 0
2022-11-26 48430 14 191 0
2022-11-25 48416 28 191 0
2022-11-24 48388 32 191 0
2022-11-23 48356 36 191 0
2022-11-22 48320 56 191 0
2022-11-21 48264 74 191 0
2022-11-20 48190 18 191 0
2022-11-19 48172 17 191 0
2022-11-18 48155 39 191 0
2022-11-17 48116 36 191 0
2022-11-16 48080 33 191 0
2022-11-15 48047 38 191 0
2022-11-14 48009 60 191 0
2022-11-13 47949 23 191 0
2022-11-12 47926 25 191 0
2022-11-11 47901 44 191 0
2022-11-10 47857 36 191 0
2022-11-09 47821 61 191 0
2022-11-08 47760 37 191 0
2022-11-07 47723 57 191 0
2022-11-06 47666 24 191 0
2022-11-05 47642 10 191 0
2022-11-04 47632 50 191 0
2022-11-03 47582 64 191 0
2022-11-02 47518 72 191 0
2022-11-01 47446 41 191 0
2022-10-31 47405 56 191 0
2022-10-30 47349 21 191 1
2022-10-29 47328 38 190 0
2022-10-28 47290 68 190 0
2022-10-27 47222 72 190 1
2022-10-26 47150 55 189 0
2022-10-25 47095 83 189 0
2022-10-24 47012 142 189 0
2022-10-23 46870 43 189 0
2022-10-22 46827 62 189 0
2022-10-21 46765 67 189 0
2022-10-20 46698 125 189 0
2022-10-19 46573 133 189 0
2022-10-18 46440 104 189 0
2022-10-17 46336 215 189 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: 0:03:08.652968