Germany: LK Kusel (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 14:47:36 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Kusel", weeks=5);
2023-03-07T14:47:50.466902 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) 87.2 LK Kusel, 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 Kusel new deaths (rolling 7d mean) Germany-LK Kusel 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 Kusel cases daily growth factor Germany-LK Kusel cases daily growth factor (rolling mean) Germany-LK Kusel 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 Kusel deaths daily growth factor Germany-LK Kusel deaths daily growth factor (rolling mean) Germany-LK Kusel 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-LK Kusel doubling time cases (rolling mean) 0.00 13.99 27.98 41.97 daily change Germany-LK Kusel new cases (rolling 7d mean) Germany-LK Kusel new cases 0.000 0.330 0.659 0.989
In [4]:
overview(country="Germany", subregion="LK Kusel");
2023-03-07T14:48:08.388917 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 7-day incidence rate (per 100K people) 87.2 LK Kusel, 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 0 250 500 750 1000 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 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Kusel cases daily growth factor Germany-LK Kusel cases daily growth factor (rolling mean) Germany-LK Kusel 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 Kusel deaths daily growth factor Germany-LK Kusel deaths daily growth factor (rolling mean) Germany-LK Kusel 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 Kusel doubling time cases (rolling mean) Germany-LK Kusel doubling time deaths (rolling mean) 0.0 174.9 349.7 524.6 699.5 daily change Germany-LK Kusel new cases (rolling 7d mean) Germany-LK Kusel new cases 0.000 1.399 2.798 4.197 daily change Germany-LK Kusel new deaths (rolling 7d mean) Germany-LK Kusel new deaths 0.0 22.8 45.7 68.5 91.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Kusel", dates="2020-03-15:");
2023-03-07T14:50:35.276294 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 Kusel LK Kusel 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 Kusel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Kusel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Kusel")
print(f'Population of country="Germany", subregion="LK Kusel": {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 Kusel": 69949 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 29776 19 141 0
2023-03-03 29757 12 141 0
2023-03-01 29745 16 141 0
2023-02-28 29729 14 141 0
2023-02-27 29715 54 141 0
2023-02-24 29661 18 141 0
2023-02-23 29643 22 141 0
2023-02-22 29621 15 141 0
2023-02-21 29606 37 141 0
2023-02-17 29569 15 141 0
2023-02-16 29554 19 141 0
2023-02-15 29535 28 141 0
2023-02-14 29507 16 141 0
2023-02-13 29491 28 141 0
2023-02-10 29463 11 141 0
2023-02-09 29452 9 141 0
2023-02-08 29443 17 141 0
2023-02-07 29426 23 141 0
2023-02-06 29403 27 141 0
2023-02-03 29376 7 141 0
2023-02-02 29369 15 141 0
2023-02-01 29354 9 141 0
2023-01-31 29345 14 141 0
2023-01-30 29331 24 141 0
2023-01-27 29307 5 141 0
2023-01-26 29302 10 141 0
2023-01-25 29292 5 141 0
2023-01-24 29287 8 141 0
2023-01-23 29279 9 141 0
2023-01-20 29270 1 141 0
2023-01-19 29269 6 141 0
2023-01-18 29263 2 141 0
2023-01-17 29261 18 141 0
2023-01-16 29243 4 141 0
2023-01-13 29239 7 141 0
2023-01-12 29232 10 141 0
2023-01-11 29222 7 141 0
2023-01-10 29215 14 141 0
2023-01-09 29201 17 141 0
2023-01-06 29184 8 141 0
2023-01-05 29176 18 141 0
2023-01-04 29158 18 141 0
2023-01-03 29140 61 141 0
2022-12-30 29079 13 141 0
2022-12-29 29066 19 141 0
2022-12-28 29047 12 141 0
2022-12-27 29035 30 141 0
2022-12-23 29005 32 141 0
2022-12-22 28973 14 141 0
2022-12-21 28959 27 141 0
2022-12-20 28932 35 141 0
2022-12-19 28897 34 141 1
2022-12-16 28863 11 140 0
2022-12-15 28852 13 140 0
2022-12-14 28839 24 140 0
2022-12-13 28815 27 140 0
2022-12-12 28788 24 140 1
2022-12-09 28764 19 139 0
2022-12-08 28745 17 139 0
2022-12-07 28728 13 139 0
2022-12-06 28715 18 139 0
2022-12-05 28697 27 139 0
2022-12-02 28670 13 139 0
2022-12-01 28657 18 139 0
2022-11-30 28639 12 139 0
2022-11-29 28627 18 139 0
2022-11-28 28609 29 139 0
2022-11-25 28580 12 139 0
2022-11-24 28568 18 139 0
2022-11-23 28550 28 139 0
2022-11-22 28522 36 139 0
2022-11-21 28486 31 139 0
2022-11-18 28455 29 139 0
2022-11-17 28426 18 139 0
2022-11-16 28408 23 139 0
2022-11-15 28385 28 139 0
2022-11-14 28357 48 139 1
2022-11-11 28309 14 138 1
2022-11-10 28295 23 137 0
2022-11-09 28272 13 137 0
2022-11-08 28259 30 137 0
2022-11-07 28229 59 137 0
2022-11-04 28170 13 137 0
2022-11-03 28157 37 137 1
2022-11-02 28120 54 136 0
2022-10-31 28066 47 136 0
2022-10-28 28019 21 136 0
2022-10-27 27998 60 136 1
2022-10-26 27938 68 135 1
2022-10-25 27870 101 134 0
2022-10-24 27769 98 134 0
2022-10-21 27671 29 134 1
2022-10-20 27642 104 133 1
2022-10-19 27538 109 132 0
2022-10-18 27429 143 132 0
2022-10-17 27286 158 132 1
2022-10-14 27128 108 131 0
2022-10-13 27020 149 131 2
2022-10-12 26871 207 129 1
2022-10-11 26664 257 128 0
2022-10-10 26407 190 128 1
2022-10-07 26217 129 127 0
2022-10-06 26088 329 127 1
2022-10-05 25759 191 126 2
2022-10-04 25568 137 124 0
2022-09-30 25431 75 124 0
2022-09-29 25356 95 124 0
2022-09-28 25261 116 124 1
2022-09-27 25145 133 123 0
2022-09-26 25012 175 123 0
2022-09-23 24837 54 123 0
2022-09-22 24783 102 123 0
2022-09-21 24681 95 123 0
2022-09-20 24586 100 123 0
2022-09-19 24486 97 123 1
2022-09-16 24389 43 122 0
2022-09-15 24346 56 122 0
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2022-09-13 24227 77 122 1
2022-09-12 24150 93 121 0
2022-09-09 24057 38 121 0
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2022-09-06 23903 25 121 0
2022-09-05 23878 11 121 0
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2022-09-01 23849 21 121 0
2022-08-31 23828 41 121 0
2022-08-30 23787 28 121 0
2022-08-29 23759 56 121 0
2022-08-26 23703 22 121 0
2022-08-25 23681 25 121 0
2022-08-24 23656 27 121 0
2022-08-23 23629 42 121 1
2022-08-22 23587 67 120 0
2022-08-19 23520 20 120 0
2022-08-18 23500 26 120 0
2022-08-17 23474 47 120 0
2022-08-16 23427 68 120 0
2022-08-15 23359 83 120 1
2022-08-12 23276 17 119 0
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2022-08-10 23221 35 119 1
2022-08-09 23186 52 118 0
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2022-08-05 23048 21 117 0
2022-08-04 23027 67 117 0
2022-08-03 22960 65 117 0
2022-08-02 22895 75 117 1
2022-08-01 22820 118 116 0
2022-07-29 22702 36 116 0
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2022-04-21 16964 127 108 0
2022-04-20 16837 172 108 0
2022-04-19 16665 351 108 0
2022-04-14 16314 168 108 1
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2021-04-06 1761 2 64 0
2021-04-05 1759 7 64 0
2021-04-04 1752 4 64 0
2021-04-03 1748 5 64 1
2021-04-02 1743 10 63 0
2021-04-01 1733 10 63 0
2021-03-31 1723 10 63 1
2021-03-30 1713 7 62 0
2021-03-29 1706 3 62 0
2021-03-28 1703 5 62 0
2021-03-27 1698 7 62 0
2021-03-26 1691 7 62 0
2021-03-25 1684 6 62 0
2021-03-24 1678 9 62 1
2021-03-23 1669 7 61 0
2021-03-21 1662 2 61 0
2021-03-20 1660 19 61 0
2021-03-19 1641 2 61 0
2021-03-18 1639 5 61 1
2021-03-17 1634 7 60 0
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2021-03-15 1622 3 59 0
2021-03-14 1619 6 59 0
2021-03-13 1613 5 59 0
2021-03-12 1608 1 59 0
2021-03-10 1607 4 59 0
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2021-03-03 1594 1 59 0
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2021-02-17 1572 6 59 0
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2021-02-13 1561 1 59 0
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2021-02-10 1558 7 59 0
2021-02-09 1551 3 59 0
2021-02-08 1548 1 59 0
2021-02-07 1547 1 59 0
2021-02-06 1546 6 59 0
2021-02-04 1540 14 59 0
2021-02-03 1526 4 59 0
2021-02-02 1522 5 59 0
2021-02-01 1517 5 59 0
2021-01-31 1512 3 59 0
2021-01-30 1509 7 59 0
2021-01-29 1502 1 59 0
2021-01-28 1501 4 59 0
2021-01-27 1497 6 59 0
2021-01-26 1491 5 59 0
2021-01-25 1486 3 59 1
2021-01-24 1483 4 58 0
2021-01-23 1479 9 58 1
2021-01-22 1470 1 57 0
2021-01-21 1469 12 57 0
2021-01-20 1457 9 57 0
2021-01-19 1448 8 57 0
2021-01-18 1440 3 57 0
2021-01-17 1437 2 57 0
2021-01-16 1435 6 57 1
2021-01-15 1429 4 56 1
2021-01-14 1425 6 55 0
2021-01-13 1419 15 55 2
2021-01-12 1404 7 53 0
2021-01-11 1397 8 53 0
2021-01-10 1389 1 53 0
2021-01-09 1388 16 53 0
2021-01-08 1372 6 53 0
2021-01-07 1366 29 53 1
2021-01-06 1337 25 52 2
2021-01-05 1312 9 50 1
2021-01-04 1303 8 49 1
2021-01-02 1295 9 48 0
2020-12-31 1286 17 48 1
2020-12-30 1269 13 47 0
2020-12-29 1256 6 47 0
2020-12-28 1250 7 47 0
2020-12-26 1243 1 47 0
2020-12-24 1242 5 47 0
2020-12-23 1237 27 47 0
2020-12-22 1210 5 47 0
2020-12-21 1205 3 47 0
2020-12-20 1202 3 47 0
2020-12-19 1199 8 47 0
2020-12-18 1191 11 47 0
2020-12-17 1180 17 47 1
2020-12-16 1163 19 46 2
2020-12-15 1144 2 44 0
2020-12-14 1142 6 44 0
2020-12-13 1136 4 44 0
2020-12-12 1132 11 44 0
2020-12-11 1121 12 44 0
2020-12-10 1109 92 44 0
2020-12-09 1017 16 44 0
2020-12-08 1001 15 44 3
2020-12-07 986 3 41 0
2020-12-06 983 6 41 0
2020-12-05 977 17 41 1
2020-12-04 960 20 40 0
2020-12-03 940 14 40 0
2020-12-02 926 23 40 0
2020-12-01 903 8 40 0
2020-11-30 895 4 40 0
2020-11-29 891 7 40 0
2020-11-28 884 13 40 0
2020-11-27 871 18 40 1
2020-11-26 853 36 39 0
2020-11-25 817 17 39 0
2020-11-24 800 16 39 1
2020-11-23 784 10 38 2
2020-11-22 774 13 36 0
2020-11-21 761 23 36 1
2020-11-20 738 17 35 0
2020-11-19 721 30 35 3
2020-11-18 691 16 32 0
2020-11-17 675 11 32 1
2020-11-16 664 14 31 3
2020-11-15 650 27 28 5
2020-11-14 623 23 23 4
2020-11-13 600 29 19 2
2020-11-12 571 25 17 0
2020-11-11 546 37 17 2
2020-11-10 509 18 15 2
2020-11-09 491 6 13 0
2020-11-08 485 45 13 1
2020-11-07 440 29 12 0
2020-11-06 411 33 12 2
2020-11-05 378 41 10 2
2020-11-04 337 22 8 0
2020-11-03 315 28 8 3
2020-11-02 287 6 5 0
2020-11-01 281 8 5 0
2020-10-31 273 31 5 0
2020-10-30 242 9 5 1
2020-10-29 233 15 4 0
2020-10-28 218 19 4 1
2020-10-27 199 6 3 1
2020-10-26 193 3 2 0
2020-10-25 190 6 2 1
2020-10-24 184 3 1 0
2020-10-23 181 4 1 0
2020-10-22 177 8 1 0
2020-10-21 169 11 1 0
2020-10-20 158 8 1 0
2020-10-18 150 3 1 0
2020-10-16 147 3 1 0
2020-10-14 144 1 1 0
2020-10-13 143 1 1 0
2020-10-10 142 1 1 0
2020-10-09 141 1 1 0
2020-10-08 140 1 1 0
2020-10-06 139 2 1 0
2020-10-05 137 1 1 0
2020-09-28 136 1 1 0
2020-09-26 135 1 1 0
2020-09-24 134 1 1 0
2020-09-19 133 1 1 0
2020-09-18 132 1 1 0
2020-09-17 131 1 1 0
2020-09-11 130 2 1 0
2020-09-09 128 1 1 0
2020-08-29 127 1 1 0
2020-08-27 126 1 1 0
2020-08-26 125 2 1 0
2020-08-25 123 1 1 0
2020-08-21 122 1 1 0
2020-08-20 121 2 1 0
2020-08-19 119 1 1 0
2020-08-18 118 2 1 0
2020-08-16 116 1 1 0
2020-08-08 115 1 1 0
2020-08-05 114 6 1 0
2020-08-04 108 3 1 0
2020-08-01 105 3 1 0
2020-07-29 102 1 1 0
2020-07-28 101 2 1 0
2020-07-22 99 1 1 0
2020-07-16 98 1 1 0
2020-07-14 97 1 1 0
2020-06-30 96 4 1 0
2020-06-29 92 1 1 0
2020-05-18 91 1 1 0
2020-05-11 90 1 1 0
2020-05-06 89 1 1 0
2020-05-04 88 1 1 0
2020-04-23 87 1 1 0
2020-04-22 86 1 1 0
2020-04-20 85 1 1 1
2020-04-16 84 2 0 0
2020-04-15 82 1 0 0
2020-04-13 81 1 0 0
2020-04-12 80 3 0 0
2020-04-11 77 1 0 0
2020-04-10 76 4 0 0
2020-04-09 72 3 0 0
2020-04-08 69 2 0 0
2020-04-07 67 3 0 0
2020-04-04 64 4 0 0
2020-04-03 60 3 0 0
2020-04-02 57 4 0 0
2020-04-01 53 4 0 0
2020-03-31 49 3 0 0
2020-03-30 46 1 0 0
2020-03-29 45 1 0 0
2020-03-28 44 8 0 0
2020-03-27 36 3 0 0
2020-03-26 33 1 0 0
2020-03-25 32 1 0 0
2020-03-24 31 5 0 0
2020-03-23 26 2 0 0
2020-03-22 24 3 0 0
2020-03-21 21 2 0 0
2020-03-20 19 1 0 0
2020-03-18 18 3 0 0
2020-03-17 15 1 0 0
2020-03-15 14 1 0 0
2020-03-13 13 12 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: 0:04:38.503282