Germany: LK Ahrweiler (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 14:40:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ahrweiler", weeks=5);
2023-03-07T14:40:20.328635 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) 89.0 LK Ahrweiler, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 25 50 75 100 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.00 0.05 0.05 0.10 0.10 daily change Germany-LK Ahrweiler new deaths (rolling 7d mean) Germany-LK Ahrweiler new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Ahrweiler cases daily growth factor Germany-LK Ahrweiler cases daily growth factor (rolling mean) Germany-LK Ahrweiler 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 Ahrweiler deaths daily growth factor Germany-LK Ahrweiler deaths daily growth factor (rolling mean) Germany-LK Ahrweiler estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-LK Ahrweiler doubling time cases (rolling mean) 0.0 32.0 64.1 96.1 128.1 daily change Germany-LK Ahrweiler new cases (rolling 7d mean) Germany-LK Ahrweiler new cases 0.000 0.388 0.776
In [4]:
overview(country="Germany", subregion="LK Ahrweiler");
2023-03-07T14:40:39.717433 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 7-day incidence rate (per 100K people) 89.0 LK Ahrweiler, 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 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 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on cases) Germany-LK Ahrweiler cases daily growth factor Germany-LK Ahrweiler cases daily growth factor (rolling mean) Germany-LK Ahrweiler 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 Ahrweiler deaths daily growth factor Germany-LK Ahrweiler deaths daily growth factor (rolling mean) Germany-LK Ahrweiler 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 Ahrweiler doubling time cases (rolling mean) Germany-LK Ahrweiler doubling time deaths (rolling mean) 0.0 128.1 256.3 384.4 daily change Germany-LK Ahrweiler new cases (rolling 7d mean) Germany-LK Ahrweiler new cases 0.000 1.281 2.563 3.844 daily change Germany-LK Ahrweiler new deaths (rolling 7d mean) Germany-LK Ahrweiler new deaths 0.0 26.3 52.7 79.0 105.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ahrweiler", dates="2020-03-15:");
2023-03-07T14:42:53.222727 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 Ahrweiler LK Ahrweiler 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 Ahrweiler Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ahrweiler")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ahrweiler")
print(f'Population of country="Germany", subregion="LK Ahrweiler": {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 Ahrweiler": 128146 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-01 47945 40 145 0
2023-02-28 47905 74 145 0
2023-02-27 47831 138 145 0
2023-02-24 47693 93 145 0
2023-02-23 47600 57 145 0
2023-02-22 47543 85 145 0
2023-02-21 47458 116 145 0
2023-02-17 47342 22 145 0
2023-02-16 47320 50 145 0
2023-02-15 47270 60 145 0
2023-02-14 47210 38 145 0
2023-02-13 47172 53 145 0
2023-02-10 47119 26 145 0
2023-02-09 47093 27 145 0
2023-02-08 47066 33 145 0
2023-02-07 47033 33 145 0
2023-02-06 47000 39 145 0
2023-02-03 46961 29 145 0
2023-02-02 46932 23 145 0
2023-02-01 46909 31 145 0
2023-01-31 46878 28 145 0
2023-01-30 46850 31 145 1
2023-01-27 46819 15 144 0
2023-01-26 46804 21 144 0
2023-01-25 46783 18 144 0
2023-01-24 46765 27 144 0
2023-01-23 46738 17 144 0
2023-01-20 46721 15 144 0
2023-01-19 46706 12 144 0
2023-01-18 46694 23 144 0
2023-01-17 46671 13 144 0
2023-01-16 46658 39 144 0
2023-01-13 46619 9 144 0
2023-01-12 46610 8 144 0
2023-01-11 46602 22 144 0
2023-01-10 46580 27 144 0
2023-01-09 46553 36 144 0
2023-01-06 46517 19 144 0
2023-01-05 46498 24 144 0
2023-01-04 46474 29 144 0
2023-01-03 46445 52 144 0
2023-01-02 46393 52 144 1
2022-12-30 46341 17 143 0
2022-12-29 46324 34 143 0
2022-12-28 46290 34 143 0
2022-12-27 46256 91 143 1
2022-12-23 46165 45 142 0
2022-12-22 46120 44 142 0
2022-12-21 46076 73 142 0
2022-12-20 46003 61 142 0
2022-12-19 45942 74 142 0
2022-12-17 45868 1 142 0
2022-12-16 45867 26 142 1
2022-12-15 45841 34 141 0
2022-12-14 45807 41 141 1
2022-12-13 45766 43 140 1
2022-12-12 45723 78 139 0
2022-12-09 45645 38 139 0
2022-12-08 45607 29 139 0
2022-12-07 45578 33 139 0
2022-12-06 45545 50 139 0
2022-12-05 45495 45 139 0
2022-12-02 45450 41 139 0
2022-12-01 45409 44 139 0
2022-11-30 45365 46 139 0
2022-11-29 45319 49 139 0
2022-11-28 45270 72 139 0
2022-11-25 45198 58 139 1
2022-11-24 45140 50 138 0
2022-11-23 45090 41 138 0
2022-11-22 45049 60 138 0
2022-11-21 44989 79 138 1
2022-11-18 44910 54 137 0
2022-11-17 44856 45 137 1
2022-11-16 44811 52 136 1
2022-11-15 44759 69 135 0
2022-11-14 44690 76 135 0
2022-11-11 44614 42 135 0
2022-11-10 44572 44 135 0
2022-11-09 44528 41 135 0
2022-11-08 44487 58 135 0
2022-11-07 44429 85 135 0
2022-11-04 44344 45 135 0
2022-11-03 44299 69 135 0
2022-11-02 44230 95 135 0
2022-10-31 44135 105 135 0
2022-10-28 44030 58 135 0
2022-10-27 43972 62 135 1
2022-10-26 43910 92 134 0
2022-10-25 43818 173 134 1
2022-10-24 43645 100 133 1
2022-10-21 43545 95 132 0
2022-10-20 43450 118 132 0
2022-10-19 43332 165 132 1
2022-10-18 43167 171 131 0
2022-10-17 42996 284 131 0
2022-10-14 42712 133 131 1
2022-10-13 42579 172 130 0
2022-10-12 42407 171 130 0
2022-10-11 42236 157 130 0
2022-10-10 42079 286 130 0
2022-10-07 41793 135 130 1
2022-10-06 41658 186 129 0
2022-10-05 41472 167 129 0
2022-10-04 41305 291 129 2
2022-09-30 41014 76 127 0
2022-09-29 40938 122 127 1
2022-09-28 40816 131 126 0
2022-09-27 40685 145 126 0
2022-09-26 40540 191 126 0
2022-09-23 40349 66 126 0
2022-09-22 40283 74 126 0
2022-09-21 40209 85 126 0
2022-09-20 40124 112 126 0
2022-09-19 40012 132 126 0
2022-09-16 39880 64 126 0
2022-09-15 39816 56 126 1
2022-09-14 39760 69 125 0
2022-09-13 39691 62 125 0
2022-09-12 39629 128 125 0
2022-09-09 39501 53 125 0
2022-09-08 39448 48 125 0
2022-09-07 39400 62 125 0
2022-09-06 39338 86 125 0
2022-09-05 39252 81 125 0
2022-09-02 39171 33 125 0
2022-09-01 39138 35 125 0
2022-08-31 39103 58 125 0
2022-08-30 39045 55 125 0
2022-08-29 38990 80 125 2
2022-08-26 38910 26 123 0
2022-08-25 38884 34 123 1
2022-08-24 38850 42 122 1
2022-08-23 38808 52 121 0
2022-08-22 38756 68 121 0
2022-08-19 38688 37 121 0
2022-08-18 38651 43 121 0
2022-08-17 38608 62 121 0
2022-08-16 38546 72 121 0
2022-08-15 38474 106 121 0
2022-08-12 38368 45 121 0
2022-08-11 38323 59 121 0
2022-08-10 38264 43 121 1
2022-08-09 38221 67 120 0
2022-08-08 38154 96 120 2
2022-08-05 38058 41 118 0
2022-08-04 38017 55 118 2
2022-08-03 37962 68 116 0
2022-08-02 37894 77 116 1
2022-08-01 37817 121 115 1
2022-07-29 37696 70 114 0
2022-07-28 37626 75 114 0
2022-07-27 37551 98 114 0
2022-07-26 37453 83 114 0
2022-07-25 37370 175 114 1
2022-07-22 37195 93 113 1
2022-07-21 37102 112 112 2
2022-07-20 36990 116 110 0
2022-07-19 36874 138 110 1
2022-07-18 36736 208 109 0
2022-07-15 36528 139 109 0
2022-07-14 36389 147 109 0
2022-07-13 36242 170 109 0
2022-07-12 36072 149 109 1
2022-07-11 35923 227 108 1
2022-07-08 35696 94 107 0
2022-07-07 35602 121 107 1
2022-07-06 35481 165 106 0
2022-07-05 35316 142 106 0
2022-07-04 35174 252 106 0
2022-07-01 34922 119 106 0
2022-06-30 34803 158 106 0
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2022-06-27 34239 234 106 1
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2022-06-22 33677 182 105 0
2022-06-21 33495 174 105 0
2022-06-20 33321 234 105 0
2022-06-17 33087 174 105 0
2022-06-15 32913 138 105 0
2022-06-14 32775 151 105 0
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2022-06-10 32405 114 105 0
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2022-06-07 32040 146 105 0
2022-06-03 31894 55 105 0
2022-06-02 31839 53 105 0
2022-06-01 31786 55 105 0
2022-05-31 31731 57 105 0
2022-05-30 31674 61 105 0
2022-05-27 31613 62 105 0
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2022-05-24 31493 64 105 0
2022-05-23 31429 85 105 1
2022-05-20 31344 52 104 0
2022-05-19 31292 47 104 0
2022-05-18 31245 37 104 0
2022-05-17 31208 82 104 0
2022-05-16 31126 124 104 1
2022-05-13 31002 63 103 1
2022-05-12 30939 60 102 0
2022-05-11 30879 78 102 0
2022-05-10 30801 81 102 0
2022-05-09 30720 165 102 0
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2022-05-03 30295 133 102 0
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2022-04-29 29977 85 102 0
2022-04-28 29892 118 102 0
2022-04-27 29774 144 102 0
2022-04-26 29630 136 102 0
2022-04-25 29494 90 102 0
2022-04-24 29404 51 102 0
2022-04-23 29353 117 102 0
2022-04-22 29236 122 102 0
2022-04-21 29114 152 102 0
2022-04-20 28962 184 102 0
2022-04-19 28778 61 102 0
2022-04-18 28717 32 102 0
2022-04-17 28685 103 102 0
2022-04-16 28582 131 102 0
2022-04-15 28451 88 102 0
2022-04-14 28363 188 102 0
2022-04-13 28175 176 102 1
2022-04-12 27999 226 101 0
2022-04-11 27773 144 101 0
2022-04-10 27629 100 101 0
2022-04-09 27529 181 101 0
2022-04-08 27348 213 101 1
2022-04-07 27135 297 100 0
2022-04-06 26838 271 100 0
2022-04-05 26567 270 100 1
2022-04-04 26297 189 99 3
2022-04-03 26108 3 96 0
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2022-04-01 25939 276 96 1
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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: 0:03:03.098828