Germany: LK Ilm-Kreis (Thüringen)¶

  • Homepage of project: https://oscovida.github.io
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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 16:01:44 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ilm-Kreis", weeks=5);
2023-03-07T16:01:58.369902 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 7-day incidence rate (per 100K people) 50.3 LK Ilm-Kreis, 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 0.0 0.2 0.4 0.6 0.8 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-LK Ilm-Kreis cases daily growth factor Germany-LK Ilm-Kreis cases daily growth factor (rolling mean) Germany-LK Ilm-Kreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Ilm-Kreis deaths daily growth factor Germany-LK Ilm-Kreis deaths daily growth factor (rolling mean) Germany-LK Ilm-Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Ilm-Kreis doubling time cases (rolling mean) 0.00 10.54 21.07 daily change Germany-LK Ilm-Kreis new cases (rolling 7d mean) Germany-LK Ilm-Kreis new cases 0.000 0.211 0.421 0.632 0.843 daily change Germany-LK Ilm-Kreis new deaths (rolling 7d mean) Germany-LK Ilm-Kreis new deaths 0.000 0.207 0.413 0.620 0.826
In [4]:
overview(country="Germany", subregion="LK Ilm-Kreis");
2023-03-07T16:02:16.617172 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) 50.3 LK Ilm-Kreis, 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 8 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 Ilm-Kreis cases daily growth factor Germany-LK Ilm-Kreis cases daily growth factor (rolling mean) Germany-LK Ilm-Kreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Ilm-Kreis deaths daily growth factor Germany-LK Ilm-Kreis deaths daily growth factor (rolling mean) Germany-LK Ilm-Kreis 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 cases doubling time [days] Germany-LK Ilm-Kreis doubling time cases (rolling mean) Germany-LK Ilm-Kreis doubling time deaths (rolling mean) 0.0 210.7 421.5 632.2 daily change Germany-LK Ilm-Kreis new cases (rolling 7d mean) Germany-LK Ilm-Kreis new cases 0.00 2.11 4.21 6.32 8.43 daily change Germany-LK Ilm-Kreis new deaths (rolling 7d mean) Germany-LK Ilm-Kreis new deaths 0.0 115.5 231.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ilm-Kreis", dates="2020-03-15:");
2023-03-07T16:04:31.185385 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 Ilm-Kreis LK Ilm-Kreis 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 Ilm-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ilm-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ilm-Kreis")
print(f'Population of country="Germany", subregion="LK Ilm-Kreis": {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 Ilm-Kreis": 105367 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 48563 2 460 0
2023-03-05 48561 1 460 0
2023-03-03 48560 6 460 0
2023-03-02 48554 5 460 0
2023-03-01 48549 10 460 0
2023-02-28 48539 29 460 0
2023-02-27 48510 26 460 0
2023-02-24 48484 11 460 0
2023-02-23 48473 4 460 0
2023-02-22 48469 10 460 0
2023-02-21 48459 23 460 0
2023-02-20 48436 5 460 0
2023-02-19 48431 3 460 0
2023-02-18 48428 6 460 0
2023-02-17 48422 8 460 0
2023-02-16 48414 11 460 0
2023-02-15 48403 10 460 0
2023-02-14 48393 12 460 0
2023-02-13 48381 17 460 1
2023-02-10 48364 7 459 1
2023-02-09 48357 10 458 0
2023-02-08 48347 3 458 0
2023-02-07 48344 9 458 1
2023-02-06 48335 5 457 0
2023-02-05 48330 6 457 0
2023-02-04 48324 3 457 0
2023-02-03 48321 6 457 0
2023-02-02 48315 2 457 0
2023-02-01 48313 8 457 1
2023-01-31 48305 6 456 0
2023-01-30 48299 4 456 0
2023-01-29 48295 2 456 0
2023-01-28 48293 1 456 0
2023-01-27 48292 2 456 0
2023-01-26 48290 7 456 0
2023-01-25 48283 4 456 1
2023-01-24 48279 6 455 0
2023-01-23 48273 2 455 0
2023-01-22 48271 7 455 0
2023-01-20 48264 3 455 0
2023-01-18 48261 12 455 0
2023-01-17 48249 15 455 0
2023-01-16 48234 2 455 0
2023-01-15 48232 2 455 0
2023-01-14 48230 2 455 0
2023-01-13 48228 2 455 0
2023-01-12 48226 7 455 0
2023-01-11 48219 27 455 0
2023-01-10 48192 22 455 2
2023-01-08 48170 5 453 0
2023-01-07 48165 5 453 0
2023-01-06 48160 7 453 0
2023-01-05 48153 12 453 0
2023-01-04 48141 19 453 0
2023-01-03 48122 17 453 0
2023-01-02 48105 15 453 0
2022-12-31 48090 17 453 1
2022-12-30 48073 32 452 0
2022-12-29 48041 12 452 0
2022-12-28 48029 25 452 0
2022-12-27 48004 6 452 1
2022-12-25 47998 11 451 0
2022-12-24 47987 40 451 0
2022-12-23 47947 12 451 0
2022-12-22 47935 19 451 0
2022-12-21 47916 33 451 2
2022-12-20 47883 30 449 1
2022-12-19 47853 1 448 0
2022-12-18 47852 3 448 1
2022-12-17 47849 31 447 0
2022-12-16 47818 17 447 0
2022-12-15 47801 35 447 1
2022-12-14 47766 35 446 0
2022-12-13 47731 37 446 0
2022-12-11 47694 28 446 0
2022-12-09 47666 21 446 0
2022-12-08 47645 42 446 0
2022-12-07 47603 27 446 0
2022-12-06 47576 35 446 0
2022-12-05 47541 9 446 0
2022-12-04 47532 21 446 0
2022-12-02 47511 22 446 0
2022-12-01 47489 35 446 0
2022-11-30 47454 17 446 0
2022-11-29 47437 31 446 0
2022-11-28 47406 5 446 0
2022-11-27 47401 5 446 0
2022-11-26 47396 13 446 0
2022-11-25 47383 33 446 0
2022-11-24 47350 24 446 0
2022-11-23 47326 37 446 0
2022-11-22 47289 30 446 0
2022-11-21 47259 3 446 0
2022-11-20 47256 11 446 0
2022-11-19 47245 20 446 0
2022-11-18 47225 16 446 0
2022-11-17 47209 32 446 0
2022-11-16 47177 36 446 0
2022-11-15 47141 41 446 0
2022-11-14 47100 8 446 0
2022-11-13 47092 35 446 0
2022-11-11 47057 39 446 0
2022-11-10 47018 37 446 0
2022-11-09 46981 43 446 0
2022-11-08 46938 34 446 0
2022-11-07 46904 12 446 0
2022-11-06 46892 25 446 0
2022-11-05 46867 24 446 0
2022-11-04 46843 41 446 0
2022-11-03 46802 36 446 0
2022-11-02 46766 85 446 0
2022-11-01 46681 16 446 0
2022-10-31 46665 12 446 0
2022-10-30 46653 4 446 0
2022-10-29 46649 51 446 0
2022-10-28 46598 58 446 0
2022-10-27 46540 51 446 0
2022-10-26 46489 93 446 1
2022-10-25 46396 115 445 0
2022-10-24 46281 15 445 0
2022-10-23 46266 64 445 0
2022-10-22 46202 40 445 0
2022-10-21 46162 82 445 0
2022-10-20 46080 89 445 0
2022-10-19 45991 177 445 1
2022-10-18 45814 143 444 0
2022-10-17 45671 48 444 0
2022-10-16 45623 89 444 0
2022-10-15 45534 73 444 0
2022-10-14 45461 85 444 0
2022-10-13 45376 125 444 1
2022-10-12 45251 400 443 0
2022-10-11 44851 108 443 0
2022-10-10 44743 55 443 1
2022-10-09 44688 8 442 0
2022-10-08 44680 66 442 0
2022-10-07 44614 85 442 0
2022-10-06 44529 284 442 0
2022-10-05 44245 149 442 1
2022-10-04 44096 34 441 0
2022-10-03 44062 15 441 0
2022-10-02 44047 25 441 0
2022-10-01 44022 49 441 0
2022-09-30 43973 56 441 1
2022-09-29 43917 132 440 0
2022-09-28 43785 86 440 1
2022-09-27 43699 132 439 0
2022-09-26 43567 36 439 0
2022-09-25 43531 21 439 0
2022-09-24 43510 29 439 0
2022-09-23 43481 60 439 0
2022-09-22 43421 84 439 1
2022-09-21 43337 24 438 0
2022-09-20 43313 35 438 0
2022-09-19 43278 18 438 0
2022-09-18 43260 12 438 0
2022-09-17 43248 19 438 0
2022-09-16 43229 79 438 1
2022-09-15 43150 88 437 0
2022-09-14 43062 96 437 0
2022-09-13 42966 112 437 0
2022-09-12 42854 32 437 0
2022-09-10 42822 31 437 0
2022-09-09 42791 90 437 0
2022-09-08 42701 53 437 0
2022-09-07 42648 53 437 0
2022-09-06 42595 92 437 1
2022-09-05 42503 45 436 1
2022-09-03 42458 30 435 0
2022-09-02 42428 38 435 0
2022-09-01 42390 31 435 0
2022-08-31 42359 92 435 0
2022-08-29 42267 31 435 0
2022-08-28 42236 31 435 0
2022-08-26 42205 36 435 0
2022-08-25 42169 76 435 0
2022-08-24 42093 37 435 0
2022-08-23 42056 41 435 0
2022-08-22 42015 27 435 0
2022-08-21 41988 10 435 0
2022-08-20 41978 28 435 0
2022-08-19 41950 21 435 0
2022-08-18 41929 61 435 0
2022-08-17 41868 73 435 0
2022-08-16 41795 48 435 0
2022-08-15 41747 37 435 0
2022-08-14 41710 12 435 1
2022-08-13 41698 28 434 0
2022-08-12 41670 42 434 0
2022-08-11 41628 44 434 0
2022-08-10 41584 80 434 0
2022-08-09 41504 57 434 0
2022-08-08 41447 27 434 0
2022-08-07 41420 5 434 0
2022-08-06 41415 20 434 0
2022-08-05 41395 27 434 0
2022-08-04 41368 65 434 0
2022-08-03 41303 88 434 0
2022-08-02 41215 64 434 0
2022-08-01 41151 62 434 1
2022-07-29 41089 57 433 0
2022-07-28 41032 59 433 0
2022-07-27 40973 75 433 1
2022-07-26 40898 76 432 0
2022-07-25 40822 39 432 1
2022-07-24 40783 30 431 0
2022-07-23 40753 51 431 0
2022-07-22 40702 55 431 0
2022-07-21 40647 105 431 0
2022-07-20 40542 99 431 0
2022-07-19 40443 99 431 0
2022-07-18 40344 33 431 0
2022-07-17 40311 11 431 0
2022-07-16 40300 30 431 0
2022-07-15 40270 96 431 0
2022-07-14 40174 128 431 0
2022-07-13 40046 80 431 0
2022-07-12 39966 51 431 0
2022-07-11 39915 59 431 0
2022-07-10 39856 9 431 0
2022-07-09 39847 24 431 0
2022-07-08 39823 54 431 1
2022-07-07 39769 59 430 0
2022-07-06 39710 65 430 0
2022-07-05 39645 95 430 0
2022-07-04 39550 24 430 0
2022-07-03 39526 42 430 0
2022-07-01 39484 73 430 0
2022-06-30 39411 46 430 1
2022-06-29 39365 96 429 0
2022-06-28 39269 70 429 0
2022-06-27 39199 61 429 0
2022-06-26 39138 8 429 0
2022-06-25 39130 18 429 0
2022-06-24 39112 80 429 0
2022-06-23 39032 30 429 0
2022-06-22 39002 71 429 1
2022-06-21 38931 56 428 0
2022-06-20 38875 7 428 0
2022-06-19 38868 38 428 1
2022-06-17 38830 69 427 0
2022-06-16 38761 42 427 0
2022-06-15 38719 41 427 0
2022-06-14 38678 37 427 0
2022-06-13 38641 23 427 0
2022-06-12 38618 13 427 1
2022-06-11 38605 10 426 0
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2021-01-04 2188 13 91 2
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2021-01-02 2162 44 88 3
2021-01-01 2118 57 85 7
2020-12-31 2061 65 78 7
2020-12-30 1996 69 71 3
2020-12-29 1927 52 68 3
2020-12-28 1875 28 65 1
2020-12-27 1847 12 64 0
2020-12-26 1835 27 64 0
2020-12-25 1808 46 64 2
2020-12-24 1762 33 62 1
2020-12-23 1729 71 61 2
2020-12-22 1658 58 59 3
2020-12-21 1600 27 56 0
2020-12-20 1573 65 56 9
2020-12-19 1508 39 47 1
2020-12-18 1469 41 46 1
2020-12-17 1428 36 45 3
2020-12-16 1392 49 42 3
2020-12-15 1343 54 39 2
2020-12-14 1289 34 37 2
2020-12-13 1255 41 35 0
2020-12-12 1214 32 35 0
2020-12-11 1182 72 35 4
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2020-12-09 1073 31 30 4
2020-12-08 1042 32 26 1
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2020-12-05 983 27 22 0
2020-12-04 956 31 22 3
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2020-12-02 904 25 18 3
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2020-11-28 833 16 13 0
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2020-11-25 780 20 13 1
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2020-11-03 331 10 8 0
2020-11-02 321 6 8 1
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2020-10-31 310 1 7 0
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2020-10-29 300 12 7 0
2020-10-28 288 6 7 0
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2020-10-26 278 3 7 0
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2020-10-23 271 12 7 0
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2020-10-11 228 2 7 0
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2020-09-28 216 1 7 0
2020-09-25 215 1 7 0
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2020-09-23 213 1 7 0
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2020-09-18 207 10 7 0
2020-09-17 197 1 7 0
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2020-09-15 187 7 7 0
2020-09-14 180 7 7 0
2020-09-11 173 2 7 0
2020-09-09 171 2 7 0
2020-09-08 169 1 7 0
2020-09-06 168 1 7 1
2020-09-05 167 2 6 0
2020-09-04 165 3 6 0
2020-09-03 162 3 6 0
2020-09-02 159 1 6 0
2020-08-21 158 1 6 0
2020-08-19 157 1 6 0
2020-08-18 156 2 6 0
2020-08-12 154 1 6 0
2020-08-10 153 1 6 0
2020-08-07 152 2 6 0
2020-08-05 150 1 6 0
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2020-07-29 146 2 6 0
2020-07-20 144 1 6 0
2020-07-17 143 1 6 0
2020-07-16 142 2 6 0
2020-07-15 140 3 6 0
2020-07-08 137 1 6 0
2020-05-28 136 1 6 0
2020-05-27 135 2 6 0
2020-05-26 133 3 6 1
2020-05-20 130 1 5 0
2020-05-09 129 2 5 0
2020-05-06 127 2 5 0
2020-05-05 125 2 5 0
2020-05-02 123 1 5 0
2020-05-01 122 1 5 0
2020-04-24 121 1 5 0
2020-04-22 120 1 5 0
2020-04-21 119 6 5 0
2020-04-20 113 1 5 0
2020-04-18 112 2 5 0
2020-04-17 110 3 5 0
2020-04-16 107 3 5 0
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2020-04-13 103 1 5 0
2020-04-09 102 2 5 1
2020-04-08 100 2 4 1
2020-04-07 98 1 3 0
2020-04-06 97 4 3 1
2020-04-04 93 13 2 0
2020-04-03 80 6 2 0
2020-04-02 74 9 2 0
2020-03-31 65 7 2 1
2020-03-30 58 3 1 0
2020-03-28 55 4 1 0
2020-03-27 51 12 1 0
2020-03-26 39 9 1 0
2020-03-25 30 9 1 0
2020-03-24 21 8 1 0
2020-03-23 13 2 1 0
2020-03-22 11 1 1 1
2020-03-21 10 2 0 0
2020-03-20 8 4 0 0
2020-03-19 4 1 0 0
2020-03-18 3 1 0 0
2020-03-17 2 1 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: 0:02:55.468398