Germany: SK Neustadt a.d.Weinstraße (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 15:04:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Neustadt a.d.Weinstraße", weeks=5);
2023-03-07T15:04:21.803594 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 7-day incidence rate (per 100K people) 110.3 SK Neustadt a.d.Weinstraße, 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 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 Neustadt a.d.Weinstraße new deaths (rolling 7d mean) Germany-SK Neustadt a.d.Weinstraße new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-SK Neustadt a.d.Weinstraße cases daily growth factor Germany-SK Neustadt a.d.Weinstraße cases daily growth factor (rolling mean) Germany-SK Neustadt a.d.Weinstraße 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 Neustadt a.d.Weinstraße deaths daily growth factor Germany-SK Neustadt a.d.Weinstraße deaths daily growth factor (rolling mean) Germany-SK Neustadt a.d.Weinstraße 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-SK Neustadt a.d.Weinstraße doubling time cases (rolling mean) 0.0 10.7 21.4 daily change Germany-SK Neustadt a.d.Weinstraße new cases (rolling 7d mean) Germany-SK Neustadt a.d.Weinstraße new cases 0.000 0.233 0.466 0.699 0.932
In [4]:
overview(country="Germany", subregion="SK Neustadt a.d.Weinstraße");
2023-03-07T15:04:38.255108 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) 110.3 SK Neustadt a.d.Weinstraße, 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 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-SK Neustadt a.d.Weinstraße cases daily growth factor Germany-SK Neustadt a.d.Weinstraße cases daily growth factor (rolling mean) Germany-SK Neustadt a.d.Weinstraße 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 Neustadt a.d.Weinstraße deaths daily growth factor Germany-SK Neustadt a.d.Weinstraße deaths daily growth factor (rolling mean) Germany-SK Neustadt a.d.Weinstraße estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 20000 cases doubling time [days] Germany-SK Neustadt a.d.Weinstraße doubling time cases (rolling mean) Germany-SK Neustadt a.d.Weinstraße doubling time deaths (rolling mean) 0 107 214 321 daily change Germany-SK Neustadt a.d.Weinstraße new cases (rolling 7d mean) Germany-SK Neustadt a.d.Weinstraße new cases 0.00 1.07 2.14 3.21 daily change Germany-SK Neustadt a.d.Weinstraße new deaths (rolling 7d mean) Germany-SK Neustadt a.d.Weinstraße new deaths 0.0 22.5 45.0 67.5 90.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Neustadt a.d.Weinstraße", dates="2020-03-15:");
2023-03-07T15:07:11.091348 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 Neustadt a.d.Weinstraße SK Neustadt a.d.Weinstraße 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 Neustadt a.d.Weinstraße Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Neustadt a.d.Weinstraße")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Neustadt a.d.Weinstraße")
print(f'Population of country="Germany", subregion="SK Neustadt a.d.Weinstraße": {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 Neustadt a.d.Weinstraße": 53491 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 22967 2 87 0
2023-03-03 22965 3 87 0
2023-03-02 22962 20 87 0
2023-03-01 22942 11 87 0
2023-02-28 22931 23 87 0
2023-02-27 22908 17 87 0
2023-02-24 22891 10 87 0
2023-02-23 22881 9 87 0
2023-02-22 22872 12 87 0
2023-02-21 22860 9 87 0
2023-02-20 22851 22 87 0
2023-02-17 22829 31 87 0
2023-02-16 22798 25 87 0
2023-02-15 22773 21 87 0
2023-02-14 22752 8 87 0
2023-02-13 22744 11 87 0
2023-02-10 22733 5 87 0
2023-02-09 22728 10 87 0
2023-02-08 22718 4 87 0
2023-02-07 22714 10 87 0
2023-02-06 22704 17 87 0
2023-02-03 22687 10 87 0
2023-02-02 22677 6 87 0
2023-02-01 22671 4 87 0
2023-01-31 22667 16 87 0
2023-01-30 22651 13 87 0
2023-01-29 22638 1 87 0
2023-01-27 22637 4 87 0
2023-01-26 22633 17 87 0
2023-01-25 22616 9 87 0
2023-01-24 22607 7 87 0
2023-01-23 22600 14 87 0
2023-01-20 22586 4 87 0
2023-01-19 22582 14 87 0
2023-01-18 22568 7 87 0
2023-01-17 22561 6 87 0
2023-01-16 22555 13 87 0
2023-01-15 22542 1 87 0
2023-01-13 22541 9 87 0
2023-01-12 22532 21 87 0
2023-01-11 22511 9 87 0
2023-01-10 22502 11 87 0
2023-01-09 22491 27 87 0
2023-01-08 22464 1 87 0
2023-01-07 22463 1 87 0
2023-01-06 22462 19 87 1
2023-01-05 22443 67 86 0
2023-01-04 22376 22 86 0
2023-01-03 22354 67 86 1
2023-01-02 22287 73 85 3
2022-12-31 22214 1 82 0
2022-12-28 22213 2 82 0
2022-12-26 22211 1 82 0
2022-12-23 22210 8 82 0
2022-12-22 22202 27 82 0
2022-12-21 22175 27 82 0
2022-12-20 22148 19 82 0
2022-12-19 22129 38 82 2
2022-12-18 22091 5 80 0
2022-12-17 22086 2 80 0
2022-12-16 22084 15 80 1
2022-12-15 22069 27 79 1
2022-12-14 22042 21 78 0
2022-12-13 22021 31 78 0
2022-12-12 21990 40 78 0
2022-12-09 21950 9 78 0
2022-12-08 21941 9 78 0
2022-12-07 21932 8 78 0
2022-12-06 21924 24 78 1
2022-12-05 21900 20 77 0
2022-12-02 21880 16 77 0
2022-12-01 21864 10 77 0
2022-11-30 21854 6 77 0
2022-11-29 21848 21 77 0
2022-11-28 21827 35 77 0
2022-11-25 21792 13 77 0
2022-11-24 21779 15 77 0
2022-11-23 21764 23 77 0
2022-11-22 21741 24 77 0
2022-11-21 21717 19 77 0
2022-11-18 21698 1 77 0
2022-11-17 21697 22 77 0
2022-11-16 21675 34 77 0
2022-11-15 21641 12 77 0
2022-11-14 21629 29 77 0
2022-11-11 21600 16 77 0
2022-11-10 21584 17 77 0
2022-11-09 21567 25 77 0
2022-11-08 21542 35 77 1
2022-11-07 21507 47 76 1
2022-11-04 21460 37 75 0
2022-11-03 21423 62 75 0
2022-11-02 21361 56 75 0
2022-10-28 21305 15 75 0
2022-10-27 21290 32 75 0
2022-10-26 21258 30 75 0
2022-10-25 21228 60 75 0
2022-10-24 21168 68 75 1
2022-10-21 21100 62 74 0
2022-10-20 21038 99 74 1
2022-10-19 20939 101 73 0
2022-10-18 20838 63 73 0
2022-10-17 20775 132 73 0
2022-10-14 20643 69 73 0
2022-10-13 20574 120 73 1
2022-10-12 20454 116 72 1
2022-10-11 20338 134 71 1
2022-10-10 20204 166 70 0
2022-10-07 20038 18 70 0
2022-10-06 20020 216 70 1
2022-10-05 19804 27 69 0
2022-10-04 19777 157 69 0
2022-09-30 19620 36 69 0
2022-09-29 19584 65 69 0
2022-09-28 19519 84 69 0
2022-09-27 19435 78 69 0
2022-09-26 19357 111 69 0
2022-09-23 19246 23 69 0
2022-09-22 19223 42 69 0
2022-09-21 19181 39 69 0
2022-09-20 19142 55 69 0
2022-09-19 19087 74 69 0
2022-09-16 19013 6 69 0
2022-09-15 19007 30 69 0
2022-09-14 18977 30 69 0
2022-09-13 18947 30 69 0
2022-09-12 18917 44 69 0
2022-09-09 18873 30 69 0
2022-09-08 18843 21 69 0
2022-09-07 18822 18 69 0
2022-09-06 18804 26 69 0
2022-09-05 18778 33 69 0
2022-09-02 18745 6 69 0
2022-09-01 18739 20 69 0
2022-08-31 18719 17 69 0
2022-08-30 18702 26 69 0
2022-08-29 18676 37 69 0
2022-08-26 18639 8 69 0
2022-08-25 18631 8 69 0
2022-08-24 18623 33 69 0
2022-08-23 18590 20 69 0
2022-08-22 18570 29 69 0
2022-08-19 18541 19 69 0
2022-08-18 18522 20 69 0
2022-08-17 18502 59 69 0
2022-08-16 18443 42 69 0
2022-08-15 18401 55 69 0
2022-08-12 18346 38 69 0
2022-08-11 18308 29 69 0
2022-08-10 18279 56 69 0
2022-08-09 18223 49 69 0
2022-08-08 18174 64 69 0
2022-08-05 18110 21 69 0
2022-08-04 18089 40 69 0
2022-08-03 18049 41 69 0
2022-08-02 18008 50 69 1
2022-08-01 17958 76 68 0
2022-07-29 17882 37 68 1
2022-07-28 17845 32 67 0
2022-07-27 17813 97 67 0
2022-07-26 17716 83 67 0
2022-07-25 17633 141 67 0
2022-07-22 17492 76 67 0
2022-07-21 17416 90 67 0
2022-07-20 17326 122 67 0
2022-07-19 17204 139 67 1
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2021-01-13 954 15 22 3
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2021-01-04 861 3 14 0
2021-01-03 858 4 14 0
2021-01-02 854 4 14 0
2021-01-01 850 5 14 0
2020-12-31 845 11 14 0
2020-12-30 834 7 14 0
2020-12-29 827 26 14 2
2020-12-28 801 6 12 0
2020-12-27 795 1 12 0
2020-12-26 794 5 12 0
2020-12-25 789 10 12 0
2020-12-24 779 11 12 0
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2020-12-18 720 18 11 0
2020-12-17 702 14 11 0
2020-12-16 688 6 11 0
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2020-12-07 591 12 7 1
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2020-12-05 574 12 6 0
2020-12-04 562 14 6 1
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2020-10-19 176 1 2 0
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2020-10-16 173 1 2 0
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2020-10-10 163 1 2 0
2020-10-09 162 1 2 0
2020-10-08 161 1 2 0
2020-10-07 160 1 2 0
2020-10-02 159 1 2 0
2020-10-01 158 2 2 0
2020-09-30 156 2 2 0
2020-09-28 154 3 2 0
2020-09-27 151 1 2 0
2020-09-26 150 2 2 0
2020-09-22 148 1 2 0
2020-09-21 147 2 2 0
2020-09-18 145 1 2 0
2020-09-17 144 2 2 0
2020-09-15 142 1 2 0
2020-09-14 141 1 2 0
2020-09-10 140 1 2 0
2020-08-31 139 1 2 0
2020-08-26 138 1 2 0
2020-08-21 137 1 2 0
2020-08-20 136 3 2 0
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2020-08-16 132 3 2 0
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2020-08-07 123 1 2 0
2020-08-05 122 1 2 0
2020-08-01 121 1 2 0
2020-07-31 120 2 2 0
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2020-07-21 113 1 2 0
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2020-05-13 103 1 2 0
2020-04-30 102 1 2 0
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2020-04-05 81 2 1 0
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2020-04-03 75 5 1 0
2020-04-02 70 3 1 0
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2020-03-31 62 2 1 0
2020-03-30 60 5 1 0
2020-03-28 55 3 1 0
2020-03-27 52 6 1 0
2020-03-26 46 4 1 0
2020-03-25 42 3 1 0
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2020-03-23 35 11 1 0
2020-03-21 24 6 1 0
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2020-03-18 10 4 1 1
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2020-03-13 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: 0:03:21.717172