Germany: LK Börde (Sachsen-Anhalt)¶

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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 15:36:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Börde", weeks=5);
2023-03-07T15:36:55.253227 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 53.5 LK Börde, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 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 Börde new deaths (rolling 7d mean) Germany-LK Börde 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 R & growth factor (based on cases) Germany-LK Börde cases daily growth factor Germany-LK Börde cases daily growth factor (rolling mean) Germany-LK Börde 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 Börde deaths daily growth factor Germany-LK Börde deaths daily growth factor (rolling mean) Germany-LK Börde estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Börde doubling time cases (rolling mean) 0.00 17.01 34.02 daily change Germany-LK Börde new cases (rolling 7d mean) Germany-LK Börde new cases 0.000 0.309 0.618 0.927
In [4]:
overview(country="Germany", subregion="LK Börde");
2023-03-07T15:37:10.586422 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) 53.5 LK Börde, 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 1 2 3 4 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 R & growth factor (based on cases) Germany-LK Börde cases daily growth factor Germany-LK Börde cases daily growth factor (rolling mean) Germany-LK Börde 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 Börde deaths daily growth factor Germany-LK Börde deaths daily growth factor (rolling mean) Germany-LK Börde estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Börde doubling time cases (rolling mean) Germany-LK Börde doubling time deaths (rolling mean) 0 425 851 1276 1701 daily change Germany-LK Börde new cases (rolling 7d mean) Germany-LK Börde new cases 0.000 1.701 3.402 5.103 6.804 daily change Germany-LK Börde new deaths (rolling 7d mean) Germany-LK Börde new deaths 0.0 46.7 93.5 140.2 186.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Börde", dates="2020-03-15:");
2023-03-07T15:39:31.386293 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 Börde LK Börde 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Börde Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Börde")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Börde")
print(f'Population of country="Germany", subregion="LK Börde": {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 Börde": 170106 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 76049 10 236 0
2023-03-03 76039 7 236 0
2023-03-02 76032 18 236 0
2023-03-01 76014 29 236 0
2023-02-28 75985 27 236 0
2023-02-27 75958 23 236 0
2023-02-24 75935 18 236 0
2023-02-23 75917 25 236 0
2023-02-22 75892 30 236 0
2023-02-21 75862 39 236 0
2023-02-20 75823 38 236 0
2023-02-17 75785 9 236 0
2023-02-16 75776 17 236 0
2023-02-15 75759 32 236 0
2023-02-14 75727 36 236 0
2023-02-13 75691 24 236 0
2023-02-10 75667 17 236 0
2023-02-09 75650 7 236 0
2023-02-08 75643 20 236 0
2023-02-07 75623 35 236 0
2023-02-06 75588 43 236 0
2023-02-03 75545 15 236 0
2023-02-02 75530 35 236 0
2023-02-01 75495 29 236 0
2023-01-31 75466 38 236 0
2023-01-30 75428 37 236 0
2023-01-27 75391 1 236 0
2023-01-26 75390 19 236 0
2023-01-25 75371 23 236 0
2023-01-24 75348 38 236 0
2023-01-23 75310 28 236 0
2023-01-20 75282 10 236 0
2023-01-19 75272 10 236 0
2023-01-18 75262 15 236 0
2023-01-17 75247 23 236 0
2023-01-16 75224 27 236 0
2023-01-13 75197 9 236 0
2023-01-12 75188 14 236 0
2023-01-11 75174 27 236 0
2023-01-10 75147 65 236 0
2023-01-09 75082 46 236 0
2023-01-05 75036 28 236 0
2023-01-04 75008 36 236 0
2023-01-03 74972 72 236 0
2023-01-02 74900 256 236 0
2022-12-23 74644 44 236 0
2022-12-22 74600 50 236 0
2022-12-21 74550 40 236 0
2022-12-20 74510 132 236 0
2022-12-19 74378 81 236 0
2022-12-16 74297 46 236 0
2022-12-15 74251 56 236 0
2022-12-14 74195 62 236 0
2022-12-13 74133 166 236 0
2022-12-12 73967 90 236 0
2022-12-09 73877 67 236 0
2022-12-08 73810 64 236 0
2022-12-07 73746 48 236 0
2022-12-06 73698 128 236 0
2022-12-05 73570 102 236 0
2022-12-02 73468 75 236 0
2022-12-01 73393 50 236 0
2022-11-30 73343 75 236 0
2022-11-29 73268 140 236 0
2022-11-28 73128 100 236 1
2022-11-25 73028 43 235 0
2022-11-24 72985 69 235 0
2022-11-23 72916 80 235 0
2022-11-22 72836 118 235 1
2022-11-21 72718 53 234 0
2022-11-18 72665 59 234 0
2022-11-17 72606 52 234 0
2022-11-16 72554 56 234 0
2022-11-15 72498 104 234 0
2022-11-14 72394 61 234 0
2022-11-11 72333 52 234 0
2022-11-10 72281 58 234 0
2022-11-09 72223 65 234 0
2022-11-08 72158 157 234 0
2022-11-07 72001 85 234 0
2022-11-05 71916 1 234 0
2022-11-04 71915 76 234 0
2022-11-03 71839 87 234 0
2022-11-02 71752 190 234 0
2022-11-01 71562 131 234 1
2022-10-28 71431 84 233 0
2022-10-27 71347 89 233 0
2022-10-26 71258 114 233 1
2022-10-25 71144 197 232 1
2022-10-24 70947 193 231 1
2022-10-21 70754 141 230 0
2022-10-20 70613 158 230 0
2022-10-19 70455 188 230 1
2022-10-18 70267 417 229 1
2022-10-17 69850 222 228 1
2022-10-14 69628 183 227 1
2022-10-13 69445 235 226 0
2022-10-12 69210 247 226 1
2022-10-11 68963 373 225 1
2022-10-10 68590 279 224 0
2022-10-07 68311 248 224 0
2022-10-06 68063 465 224 0
2022-10-05 67598 106 224 1
2022-10-04 67492 194 223 1
2022-09-30 67298 103 222 0
2022-09-29 67195 297 222 0
2022-09-28 66898 84 222 0
2022-09-27 66814 132 222 0
2022-09-26 66682 99 222 0
2022-09-23 66583 83 222 0
2022-09-22 66500 66 222 0
2022-09-21 66434 128 222 0
2022-09-20 66306 105 222 0
2022-09-19 66201 61 222 0
2022-09-16 66140 50 222 0
2022-09-15 66090 85 222 0
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2022-09-13 65920 92 222 2
2022-09-12 65828 68 220 0
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2022-09-07 65624 80 220 0
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2022-09-05 65488 44 220 0
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2022-08-08 63978 92 219 1
2022-08-05 63886 63 218 0
2022-08-04 63823 119 218 0
2022-08-03 63704 71 218 0
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2022-08-01 63493 155 218 1
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2022-07-25 62827 186 217 2
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2021-03-26 4369 22 122 1
2021-03-25 4347 55 121 0
2021-03-24 4292 31 121 0
2021-03-23 4261 76 121 0
2021-03-22 4185 16 121 0
2021-03-21 4169 1 121 0
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2021-03-19 4158 37 121 2
2021-03-18 4121 87 119 1
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2021-03-13 3946 39 116 0
2021-03-12 3907 26 116 0
2021-03-11 3881 45 116 0
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2021-03-05 3722 14 113 0
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2021-03-03 3678 37 113 2
2021-03-02 3641 29 111 1
2021-03-01 3612 4 110 0
2021-02-27 3608 53 110 2
2021-02-26 3555 16 108 0
2021-02-25 3539 31 108 1
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2021-02-22 3466 51 105 0
2021-02-19 3415 8 105 0
2021-02-18 3407 34 105 0
2021-02-17 3373 23 105 2
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2021-02-12 3278 13 102 0
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2021-02-08 3191 4 100 0
2021-02-07 3187 17 100 0
2021-02-06 3170 6 100 1
2021-02-05 3164 23 99 0
2021-02-04 3141 19 99 1
2021-02-03 3122 27 98 0
2021-02-02 3095 20 98 1
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2021-01-31 3068 7 96 0
2021-01-30 3061 36 96 2
2021-01-29 3025 27 94 0
2021-01-28 2998 36 94 3
2021-01-27 2962 51 91 8
2021-01-26 2911 32 83 2
2021-01-25 2879 11 81 1
2021-01-24 2868 13 80 0
2021-01-23 2855 53 80 3
2021-01-22 2802 35 77 2
2021-01-21 2767 61 75 5
2021-01-20 2706 51 70 2
2021-01-19 2655 64 68 3
2021-01-18 2591 14 65 0
2021-01-17 2577 21 65 0
2021-01-16 2556 5 65 0
2021-01-15 2551 44 65 1
2021-01-14 2507 32 64 1
2021-01-13 2475 66 63 1
2021-01-12 2409 88 62 4
2021-01-11 2321 7 58 0
2021-01-09 2314 8 58 0
2021-01-08 2306 53 58 2
2021-01-07 2253 20 56 0
2021-01-06 2233 60 56 2
2021-01-05 2173 107 54 3
2021-01-04 2066 32 51 2
2021-01-03 2034 18 49 1
2021-01-02 2016 28 48 0
2021-01-01 1988 19 48 0
2020-12-31 1969 36 48 1
2020-12-30 1933 72 47 1
2020-12-29 1861 9 46 4
2020-12-28 1852 19 42 1
2020-12-27 1833 4 41 0
2020-12-26 1829 8 41 0
2020-12-25 1821 91 41 0
2020-12-24 1730 1 41 0
2020-12-23 1729 36 41 1
2020-12-22 1693 51 40 0
2020-12-21 1642 15 40 0
2020-12-20 1627 22 40 0
2020-12-19 1605 78 40 3
2020-12-18 1527 48 37 0
2020-12-17 1479 32 37 0
2020-12-16 1447 34 37 2
2020-12-15 1413 43 35 1
2020-12-14 1370 98 34 2
2020-12-12 1272 4 32 0
2020-12-11 1268 43 32 2
2020-12-10 1225 70 30 0
2020-12-09 1155 35 30 0
2020-12-08 1120 11 30 0
2020-12-07 1109 53 30 1
2020-12-04 1056 64 29 1
2020-12-03 992 22 28 0
2020-12-02 970 44 28 2
2020-12-01 926 16 26 0
2020-11-30 910 2 26 1
2020-11-29 908 3 25 0
2020-11-28 905 21 25 1
2020-11-27 884 63 24 4
2020-11-26 821 6 20 0
2020-11-25 815 4 20 1
2020-11-24 811 58 19 0
2020-11-23 753 3 19 0
2020-11-22 750 11 19 0
2020-11-21 739 31 19 0
2020-11-20 708 29 19 3
2020-11-19 679 12 16 0
2020-11-18 667 18 16 0
2020-11-17 649 28 16 1
2020-11-16 621 14 15 0
2020-11-14 607 57 15 0
2020-11-13 550 29 15 0
2020-11-12 521 1 15 0
2020-11-11 520 12 15 0
2020-11-10 508 5 15 0
2020-11-09 503 16 15 0
2020-11-08 487 8 15 0
2020-11-07 479 26 15 0
2020-11-06 453 8 15 0
2020-11-05 445 4 15 0
2020-11-04 441 40 15 0
2020-11-03 401 9 15 0
2020-11-02 392 16 15 1
2020-10-30 376 31 14 0
2020-10-29 345 4 14 0
2020-10-28 341 36 14 0
2020-10-27 305 6 14 0
2020-10-26 299 5 14 0
2020-10-25 294 26 14 0
2020-10-23 268 16 14 1
2020-10-21 252 23 13 0
2020-10-19 229 5 13 0
2020-10-16 224 3 13 0
2020-10-15 221 3 13 0
2020-10-14 218 3 13 0
2020-10-13 215 1 13 0
2020-10-11 214 2 13 0
2020-10-09 212 2 13 0
2020-10-08 210 1 13 0
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2020-09-30 203 1 13 0
2020-09-23 202 1 13 0
2020-09-18 201 1 13 0
2020-09-15 200 2 13 0
2020-09-14 198 1 13 0
2020-09-04 197 2 13 0
2020-09-01 195 2 13 0
2020-08-30 193 1 13 0
2020-08-28 192 2 13 0
2020-08-20 190 1 13 0
2020-08-19 189 2 13 0
2020-08-18 187 1 13 0
2020-08-14 186 2 13 0
2020-08-11 184 2 13 0
2020-08-10 182 1 13 0
2020-08-05 181 1 13 0
2020-08-01 180 1 13 0
2020-07-31 179 3 13 0
2020-07-30 176 1 13 0
2020-07-27 175 1 13 0
2020-07-21 174 3 13 0
2020-07-20 171 1 13 0
2020-07-18 170 2 13 0
2020-07-17 168 2 13 0
2020-07-16 166 1 13 0
2020-07-14 165 2 13 0
2020-07-08 163 1 13 0
2020-06-30 162 1 13 0
2020-06-26 161 1 13 0
2020-06-17 160 1 13 0
2020-06-04 159 2 13 0
2020-06-01 157 1 13 0
2020-05-27 156 1 13 0
2020-05-20 155 1 13 0
2020-05-13 154 1 13 0
2020-05-07 153 3 13 1
2020-05-06 150 2 12 0
2020-05-05 148 9 12 1
2020-05-03 139 1 11 0
2020-05-02 138 1 11 1
2020-05-01 137 1 10 0
2020-04-30 136 1 10 0
2020-04-28 135 5 10 0
2020-04-27 130 2 10 0
2020-04-25 128 1 10 1
2020-04-24 127 10 9 2
2020-04-23 117 2 7 1
2020-04-22 115 4 6 0
2020-04-21 111 11 6 0
2020-04-19 100 1 6 0
2020-04-18 99 1 6 0
2020-04-17 98 3 6 1
2020-04-16 95 3 5 1
2020-04-14 92 5 4 0
2020-04-13 87 2 4 0
2020-04-12 85 2 4 1
2020-04-10 83 4 3 1
2020-04-09 79 5 2 0
2020-04-08 74 1 2 0
2020-04-07 73 7 2 1
2020-04-05 66 2 1 0
2020-04-04 64 1 1 0
2020-04-03 63 5 1 0
2020-04-02 58 2 1 0
2020-04-01 56 3 1 0
2020-03-31 53 2 1 0
2020-03-30 51 6 1 0
2020-03-27 45 3 1 0
2020-03-26 42 2 1 0
2020-03-25 40 1 1 0
2020-03-24 39 3 1 1
2020-03-23 36 5 0 0
2020-03-22 31 6 0 0
2020-03-20 25 4 0 0
2020-03-19 21 3 0 0
2020-03-18 18 5 0 0
2020-03-17 13 4 0 0
2020-03-16 9 8 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: 0:03:16.102641