Germany: SK Pirmasens (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 15:11:36 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Pirmasens", weeks=5);
2023-03-07T15:11:46.442609 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) 147.3 SK Pirmasens, 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 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 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-SK Pirmasens cases daily growth factor Germany-SK Pirmasens cases daily growth factor (rolling mean) Germany-SK Pirmasens 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-SK Pirmasens deaths daily growth factor Germany-SK Pirmasens deaths daily growth factor (rolling mean) Germany-SK Pirmasens 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 Pirmasens doubling time cases (rolling mean) 0.00 8.01 16.02 24.03 daily change Germany-SK Pirmasens new cases (rolling 7d mean) Germany-SK Pirmasens new cases 0.000 0.200 0.401 0.601 0.801 daily change Germany-SK Pirmasens new deaths (rolling 7d mean) Germany-SK Pirmasens new deaths 0.000 0.230 0.459 0.689 0.918
In [4]:
overview(country="Germany", subregion="SK Pirmasens");
2023-03-07T15:12:05.297768 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) 147.3 SK Pirmasens, 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 10 20 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-SK Pirmasens cases daily growth factor Germany-SK Pirmasens cases daily growth factor (rolling mean) Germany-SK Pirmasens 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-SK Pirmasens deaths daily growth factor Germany-SK Pirmasens deaths daily growth factor (rolling mean) Germany-SK Pirmasens estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Pirmasens doubling time cases (rolling mean) Germany-SK Pirmasens doubling time deaths (rolling mean) 0.0 80.1 160.2 daily change Germany-SK Pirmasens new cases (rolling 7d mean) Germany-SK Pirmasens new cases 0.00 4.01 8.01 daily change Germany-SK Pirmasens new deaths (rolling 7d mean) Germany-SK Pirmasens new deaths 0.00 11.27 22.53 33.80 45.07 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Pirmasens", dates="2020-03-15:");
2023-03-07T15:14:18.979120 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 Pirmasens SK Pirmasens 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Pirmasens Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Pirmasens")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Pirmasens")
print(f'Population of country="Germany", subregion="SK Pirmasens": {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 Pirmasens": 40054 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 16004 5 120 0
2023-03-03 15999 11 120 0
2023-03-02 15988 11 120 0
2023-03-01 15977 14 120 0
2023-02-28 15963 18 120 0
2023-02-27 15945 26 120 0
2023-02-24 15919 3 120 0
2023-02-23 15916 9 120 0
2023-02-22 15907 11 120 0
2023-02-21 15896 13 120 0
2023-02-20 15883 12 120 0
2023-02-17 15871 11 120 0
2023-02-16 15860 11 120 1
2023-02-15 15849 2 119 0
2023-02-14 15847 8 119 0
2023-02-13 15839 8 119 0
2023-02-10 15831 2 119 0
2023-02-09 15829 5 119 0
2023-02-08 15824 9 119 0
2023-02-07 15815 5 119 0
2023-02-06 15810 11 119 0
2023-02-03 15799 3 119 0
2023-02-02 15796 5 119 0
2023-02-01 15791 19 119 1
2023-01-31 15772 15 118 0
2023-01-30 15757 12 118 1
2023-01-27 15745 3 117 0
2023-01-26 15742 4 117 0
2023-01-25 15738 10 117 0
2023-01-24 15728 13 117 0
2023-01-23 15715 14 117 0
2023-01-20 15701 10 117 0
2023-01-19 15691 6 117 0
2023-01-18 15685 6 117 0
2023-01-17 15679 7 117 0
2023-01-16 15672 12 117 0
2023-01-12 15660 16 117 1
2023-01-11 15644 26 116 1
2023-01-10 15618 4 115 0
2023-01-09 15614 11 115 0
2023-01-06 15603 5 115 0
2023-01-05 15598 9 115 0
2023-01-04 15589 25 115 0
2023-01-03 15564 28 115 1
2023-01-02 15536 37 114 0
2022-12-30 15499 6 114 0
2022-12-29 15493 6 114 0
2022-12-28 15487 5 114 0
2022-12-27 15482 15 114 0
2022-12-23 15467 18 114 0
2022-12-22 15449 7 114 0
2022-12-21 15442 17 114 0
2022-12-20 15425 20 114 0
2022-12-19 15405 39 114 0
2022-12-16 15366 8 114 0
2022-12-15 15358 11 114 0
2022-12-14 15347 17 114 0
2022-12-13 15330 13 114 0
2022-12-12 15317 14 114 0
2022-12-09 15303 21 114 0
2022-12-08 15282 5 114 1
2022-12-07 15277 8 113 0
2022-12-06 15269 7 113 0
2022-12-05 15262 10 113 0
2022-12-02 15252 2 113 0
2022-12-01 15250 1 113 0
2022-11-30 15249 9 113 0
2022-11-29 15240 10 113 0
2022-11-28 15230 26 113 0
2022-11-25 15204 3 113 0
2022-11-24 15201 5 113 0
2022-11-23 15196 5 113 0
2022-11-22 15191 8 113 0
2022-11-21 15183 21 113 0
2022-11-18 15162 7 113 0
2022-11-17 15155 17 113 1
2022-11-16 15138 13 112 0
2022-11-15 15125 9 112 0
2022-11-14 15116 20 112 0
2022-11-11 15096 11 112 0
2022-11-10 15085 6 112 0
2022-11-09 15079 36 112 0
2022-11-08 15043 14 112 0
2022-11-07 15029 39 112 0
2022-11-04 14990 8 112 0
2022-11-03 14982 24 112 0
2022-11-02 14958 33 112 0
2022-10-31 14925 41 112 0
2022-10-28 14884 17 112 0
2022-10-27 14867 19 112 0
2022-10-26 14848 61 112 0
2022-10-25 14787 14 112 0
2022-10-24 14773 121 112 1
2022-10-21 14652 32 111 0
2022-10-20 14620 76 111 1
2022-10-19 14544 76 110 1
2022-10-18 14468 119 109 0
2022-10-17 14349 141 109 0
2022-10-14 14208 52 109 0
2022-10-13 14156 85 109 0
2022-10-12 14071 139 109 1
2022-10-11 13932 93 108 0
2022-10-10 13839 171 108 0
2022-10-07 13668 80 108 0
2022-10-06 13588 107 108 0
2022-10-05 13481 70 108 0
2022-10-04 13411 76 108 0
2022-09-30 13335 14 108 0
2022-09-29 13321 81 108 1
2022-09-28 13240 68 107 0
2022-09-27 13172 89 107 0
2022-09-26 13083 34 107 0
2022-09-23 13049 8 107 0
2022-09-22 13041 33 107 1
2022-09-21 13008 18 106 0
2022-09-20 12990 30 106 0
2022-09-19 12960 28 106 1
2022-09-16 12932 3 105 0
2022-09-15 12929 27 105 0
2022-09-14 12902 33 105 0
2022-09-13 12869 27 105 0
2022-09-12 12842 27 105 0
2022-09-09 12815 1 105 0
2022-09-08 12814 23 105 0
2022-09-07 12791 8 105 0
2022-09-06 12783 3 105 0
2022-09-05 12780 24 105 0
2022-09-02 12756 11 105 0
2022-09-01 12745 18 105 0
2022-08-31 12727 22 105 0
2022-08-30 12705 31 105 0
2022-08-29 12674 25 105 0
2022-08-26 12649 11 105 0
2022-08-25 12638 12 105 0
2022-08-24 12626 32 105 0
2022-08-23 12594 15 105 0
2022-08-22 12579 29 105 0
2022-08-19 12550 11 105 0
2022-08-18 12539 22 105 0
2022-08-17 12517 28 105 0
2022-08-16 12489 23 105 1
2022-08-15 12466 62 104 1
2022-08-12 12404 23 103 0
2022-08-11 12381 12 103 0
2022-08-10 12369 46 103 0
2022-08-09 12323 23 103 0
2022-08-08 12300 53 103 0
2022-08-05 12247 11 103 0
2022-08-04 12236 28 103 0
2022-08-03 12208 39 103 0
2022-08-02 12169 34 103 0
2022-08-01 12135 60 103 1
2022-07-29 12075 20 102 0
2022-07-28 12055 79 102 1
2022-07-27 11976 50 101 0
2022-07-26 11926 42 101 0
2022-07-25 11884 62 101 0
2022-07-22 11822 56 101 0
2022-07-21 11766 83 101 0
2022-07-20 11683 42 101 1
2022-07-19 11641 38 100 0
2022-07-18 11603 52 100 0
2022-07-15 11551 25 100 1
2022-07-14 11526 57 99 1
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2022-07-12 11386 69 98 0
2022-07-11 11317 60 98 0
2022-07-08 11257 18 98 0
2022-07-07 11239 35 98 0
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2022-06-13 10586 35 98 2
2022-06-10 10551 49 96 0
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2022-05-25 10379 5 96 0
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2022-05-02 9738 62 95 0
2022-04-29 9676 44 95 0
2022-04-28 9632 46 95 0
2022-04-27 9586 51 95 0
2022-04-26 9535 61 95 0
2022-04-25 9474 151 95 0
2022-04-22 9323 27 95 0
2022-04-21 9296 75 95 0
2022-04-20 9221 61 95 0
2022-04-19 9160 46 95 0
2022-04-14 9114 86 95 1
2022-04-13 9028 119 94 1
2022-04-12 8909 56 93 0
2022-04-11 8853 75 93 1
2022-04-09 8778 23 92 0
2022-04-08 8755 85 92 0
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2022-04-04 8105 170 92 0
2022-04-02 7935 32 92 0
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2022-03-31 7822 147 92 2
2022-03-30 7675 162 90 1
2022-03-29 7513 172 89 0
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2022-03-17 6713 114 88 3
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2021-03-11 909 15 50 0
2021-03-10 894 16 50 0
2021-03-09 878 8 50 0
2021-03-08 870 1 50 0
2021-03-07 869 5 50 0
2021-03-06 864 4 50 0
2021-03-05 860 2 50 0
2021-03-04 858 4 50 0
2021-03-03 854 7 50 0
2021-03-02 847 5 50 0
2021-03-01 842 1 50 0
2021-02-28 841 1 50 0
2021-02-27 840 4 50 1
2021-02-26 836 5 49 0
2021-02-25 831 2 49 0
2021-02-24 829 5 49 0
2021-02-23 824 4 49 0
2021-02-22 820 1 49 0
2021-02-20 819 3 49 0
2021-02-18 816 1 49 0
2021-02-17 815 2 49 0
2021-02-16 813 2 49 0
2021-02-15 811 6 49 0
2021-02-14 805 2 49 0
2021-02-13 803 2 49 0
2021-02-12 801 3 49 0
2021-02-11 798 2 49 0
2021-02-10 796 4 49 0
2021-02-06 792 3 49 0
2021-02-05 789 3 49 0
2021-02-04 786 7 49 0
2021-02-03 779 7 49 1
2021-02-02 772 2 48 0
2021-02-01 770 5 48 0
2021-01-31 765 2 48 0
2021-01-30 763 1 48 0
2021-01-29 762 4 48 0
2021-01-28 758 4 48 0
2021-01-27 754 11 48 1
2021-01-26 743 1 47 0
2021-01-24 742 3 47 0
2021-01-23 739 3 47 0
2021-01-22 736 9 47 1
2021-01-21 727 5 46 0
2021-01-20 722 19 46 1
2021-01-19 703 5 45 0
2021-01-18 698 8 45 1
2021-01-17 690 5 44 1
2021-01-16 685 4 43 0
2021-01-15 681 7 43 0
2021-01-14 674 9 43 0
2021-01-13 665 9 43 1
2021-01-12 656 13 42 1
2021-01-10 643 8 41 0
2021-01-09 635 10 41 1
2021-01-08 625 2 40 0
2021-01-07 623 16 40 0
2021-01-06 607 14 40 0
2021-01-05 593 10 40 2
2021-01-04 583 3 38 1
2021-01-03 580 2 37 0
2021-01-02 578 20 37 2
2020-12-31 558 2 35 0
2020-12-30 556 27 35 4
2020-12-29 529 14 31 2
2020-12-28 515 16 29 0
2020-12-27 499 27 29 3
2020-12-23 472 38 26 12
2020-12-22 434 7 14 1
2020-12-21 427 5 13 1
2020-12-20 422 18 12 0
2020-12-19 404 7 12 0
2020-12-18 397 11 12 0
2020-12-17 386 13 12 3
2020-12-16 373 15 9 0
2020-12-15 358 15 9 0
2020-12-13 343 8 9 0
2020-12-12 335 5 9 0
2020-12-11 330 15 9 0
2020-12-10 315 6 9 1
2020-12-09 309 24 8 1
2020-12-08 285 2 7 0
2020-12-07 283 4 7 0
2020-12-06 279 9 7 0
2020-12-05 270 7 7 1
2020-12-04 263 6 6 0
2020-12-03 257 15 6 1
2020-12-02 242 13 5 0
2020-12-01 229 2 5 0
2020-11-30 227 4 5 0
2020-11-29 223 7 5 0
2020-11-28 216 4 5 0
2020-11-27 212 11 5 0
2020-11-26 201 6 5 0
2020-11-25 195 7 5 1
2020-11-24 188 6 4 2
2020-11-22 182 4 2 0
2020-11-21 178 4 2 0
2020-11-20 174 3 2 0
2020-11-19 171 7 2 0
2020-11-18 164 6 2 0
2020-11-17 158 2 2 0
2020-11-16 156 3 2 0
2020-11-15 153 2 2 0
2020-11-14 151 4 2 0
2020-11-13 147 6 2 0
2020-11-12 141 2 2 0
2020-11-11 139 5 2 0
2020-11-10 134 3 2 0
2020-11-07 131 3 2 0
2020-11-06 128 5 2 0
2020-11-05 123 6 2 0
2020-11-04 117 8 2 0
2020-11-03 109 4 2 0
2020-11-02 105 1 2 1
2020-11-01 104 3 1 0
2020-10-31 101 2 1 0
2020-10-30 99 8 1 0
2020-10-29 91 9 1 0
2020-10-28 82 5 1 0
2020-10-27 77 4 1 0
2020-10-26 73 1 1 0
2020-10-25 72 2 1 0
2020-10-23 70 3 1 0
2020-10-22 67 3 1 0
2020-10-21 64 1 1 0
2020-10-19 63 1 1 0
2020-10-18 62 1 1 0
2020-10-15 61 2 1 0
2020-10-14 59 1 1 0
2020-10-13 58 1 1 0
2020-10-12 57 3 1 0
2020-10-09 54 1 1 0
2020-10-07 53 2 1 0
2020-09-29 51 2 1 0
2020-09-21 49 1 1 0
2020-09-17 48 2 1 0
2020-09-14 46 2 1 0
2020-09-11 44 1 1 0
2020-09-07 43 1 1 0
2020-08-27 42 1 1 0
2020-08-25 41 1 1 1
2020-08-24 40 1 0 0
2020-08-13 39 2 0 0
2020-08-06 37 2 0 0
2020-07-20 35 1 0 0
2020-07-16 34 1 0 0
2020-07-15 33 2 0 0
2020-05-16 31 1 0 0
2020-04-15 30 3 0 0
2020-04-13 27 1 0 0
2020-04-09 26 2 0 0
2020-04-04 24 4 0 0
2020-04-02 20 1 0 0
2020-04-01 19 4 0 0
2020-03-29 15 3 0 0
2020-03-26 12 1 0 0
2020-03-25 11 4 0 0
2020-03-23 7 2 0 0
2020-03-22 5 2 0 0
2020-03-21 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: 0:03:29.623496