Germany: LK Eichsfeld (Thüringen)¶

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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 16:01:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Eichsfeld", weeks=5);
2023-03-07T16:01:57.744163 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 83.6 LK Eichsfeld, 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 0.00 0.25 0.50 0.75 1.00 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 Eichsfeld cases daily growth factor Germany-LK Eichsfeld cases daily growth factor (rolling mean) Germany-LK Eichsfeld estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Eichsfeld deaths daily growth factor Germany-LK Eichsfeld deaths daily growth factor (rolling mean) Germany-LK Eichsfeld 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-LK Eichsfeld doubling time cases (rolling mean) 0.00 9.93 19.86 daily change Germany-LK Eichsfeld new cases (rolling 7d mean) Germany-LK Eichsfeld new cases 0.000 0.248 0.497 0.745 0.993 daily change Germany-LK Eichsfeld new deaths (rolling 7d mean) Germany-LK Eichsfeld new deaths 0.000 0.208 0.416 0.624 0.833
In [4]:
overview(country="Germany", subregion="LK Eichsfeld");
2023-03-07T16:02:18.847894 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) 83.6 LK Eichsfeld, 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 Eichsfeld cases daily growth factor Germany-LK Eichsfeld cases daily growth factor (rolling mean) Germany-LK Eichsfeld estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Eichsfeld deaths daily growth factor Germany-LK Eichsfeld deaths daily growth factor (rolling mean) Germany-LK Eichsfeld 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 Eichsfeld doubling time cases (rolling mean) Germany-LK Eichsfeld doubling time deaths (rolling mean) 0.0 198.6 397.3 595.9 daily change Germany-LK Eichsfeld new cases (rolling 7d mean) Germany-LK Eichsfeld new cases 0.000 1.986 3.973 5.959 7.946 daily change Germany-LK Eichsfeld new deaths (rolling 7d mean) Germany-LK Eichsfeld new deaths 0.0 80.1 160.2 240.3 320.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Eichsfeld", dates="2020-03-15:");
2023-03-07T16:04:27.878450 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 Eichsfeld LK Eichsfeld 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 Eichsfeld Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Eichsfeld")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Eichsfeld")
print(f'Population of country="Germany", subregion="LK Eichsfeld": {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 Eichsfeld": 99324 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 47186 16 386 0
2023-03-03 47170 17 386 0
2023-03-02 47153 12 386 0
2023-03-01 47141 13 386 0
2023-02-28 47128 25 386 0
2023-02-27 47103 24 386 0
2023-02-24 47079 17 386 0
2023-02-23 47062 16 386 0
2023-02-22 47046 22 386 0
2023-02-21 47024 24 386 0
2023-02-20 47000 22 386 0
2023-02-17 46978 17 386 0
2023-02-16 46961 9 386 1
2023-02-15 46952 15 385 0
2023-02-14 46937 13 385 0
2023-02-13 46924 13 385 1
2023-02-10 46911 8 384 0
2023-02-09 46903 14 384 0
2023-02-08 46889 15 384 0
2023-02-07 46874 20 384 0
2023-02-06 46854 13 384 0
2023-02-03 46841 7 384 0
2023-02-02 46834 15 384 0
2023-02-01 46819 13 384 0
2023-01-31 46806 18 384 0
2023-01-30 46788 15 384 0
2023-01-27 46773 9 384 0
2023-01-26 46764 7 384 0
2023-01-25 46757 8 384 0
2023-01-24 46749 13 384 0
2023-01-23 46736 21 384 0
2023-01-20 46715 1 384 0
2023-01-19 46714 10 384 0
2023-01-18 46704 19 384 0
2023-01-17 46685 5 384 0
2023-01-16 46680 20 384 0
2023-01-13 46660 7 384 1
2023-01-12 46653 18 383 0
2023-01-11 46635 14 383 0
2023-01-10 46621 17 383 0
2023-01-09 46604 36 383 0
2023-01-06 46568 12 383 0
2023-01-05 46556 30 383 0
2023-01-04 46526 19 383 0
2023-01-03 46507 31 383 1
2023-01-02 46476 32 382 0
2022-12-30 46444 18 382 0
2022-12-29 46426 31 382 1
2022-12-28 46395 47 381 0
2022-12-23 46348 26 381 0
2022-12-22 46322 31 381 1
2022-12-21 46291 36 380 0
2022-12-20 46255 31 380 1
2022-12-19 46224 36 379 0
2022-12-16 46188 8 379 0
2022-12-15 46180 22 379 0
2022-12-14 46158 28 379 0
2022-12-13 46130 50 379 1
2022-12-12 46080 28 378 1
2022-12-09 46052 17 377 0
2022-12-08 46035 27 377 1
2022-12-07 46008 39 376 0
2022-12-06 45969 36 376 1
2022-12-05 45933 34 375 1
2022-12-02 45899 14 374 1
2022-12-01 45885 20 373 0
2022-11-30 45865 19 373 0
2022-11-29 45846 27 373 0
2022-11-28 45819 32 373 0
2022-11-25 45787 21 373 0
2022-11-24 45766 22 373 0
2022-11-23 45744 27 373 0
2022-11-22 45717 21 373 0
2022-11-21 45696 34 373 0
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2022-10-25 45088 91 371 0
2022-10-24 44997 85 371 1
2022-10-21 44912 62 370 0
2022-10-20 44850 67 370 2
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2022-10-18 44647 104 367 0
2022-10-17 44543 131 367 1
2022-10-14 44412 60 366 0
2022-10-13 44352 102 366 0
2022-10-12 44250 135 366 0
2022-10-11 44115 195 366 0
2022-10-10 43920 126 366 1
2022-10-07 43794 123 365 1
2022-10-06 43671 160 364 0
2022-10-05 43511 116 364 1
2022-10-04 43395 110 363 0
2022-09-30 43285 57 363 0
2022-09-29 43228 75 363 0
2022-09-28 43153 90 363 2
2022-09-27 43063 99 361 0
2022-09-26 42964 80 361 0
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2022-08-01 41260 92 359 1
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2021-01-29 3462 34 167 2
2021-01-28 3428 55 165 2
2021-01-27 3373 42 163 3
2021-01-26 3331 67 160 4
2021-01-25 3264 11 156 3
2021-01-24 3253 4 153 0
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2021-01-22 3227 47 153 0
2021-01-21 3180 35 153 0
2021-01-20 3145 63 153 6
2021-01-19 3082 33 147 3
2021-01-18 3049 19 144 4
2021-01-17 3030 9 140 0
2021-01-16 3021 40 140 0
2021-01-15 2981 55 140 6
2021-01-14 2926 43 134 3
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2021-01-12 2833 58 126 0
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2021-01-08 2683 77 116 1
2021-01-07 2606 80 115 4
2021-01-06 2526 106 111 3
2021-01-05 2420 85 108 2
2021-01-04 2335 8 106 2
2021-01-03 2327 2 104 0
2021-01-02 2325 21 104 1
2021-01-01 2304 37 103 4
2020-12-31 2267 39 99 0
2020-12-30 2228 68 99 2
2020-12-29 2160 101 97 2
2020-12-28 2059 7 95 1
2020-12-27 2052 4 94 0
2020-12-26 2048 8 94 0
2020-12-25 2040 56 94 2
2020-12-24 1984 104 92 3
2020-12-23 1880 121 89 8
2020-12-22 1759 54 81 1
2020-12-21 1705 11 80 0
2020-12-20 1694 11 80 0
2020-12-19 1683 90 80 6
2020-12-18 1593 98 74 6
2020-12-17 1495 76 68 1
2020-12-16 1419 42 67 1
2020-12-15 1377 48 66 1
2020-12-14 1329 37 65 6
2020-12-13 1292 37 59 0
2020-12-12 1255 37 59 1
2020-12-11 1218 68 58 7
2020-12-10 1150 11 51 0
2020-12-09 1139 18 51 0
2020-12-08 1121 20 51 3
2020-12-07 1101 7 48 0
2020-12-06 1094 53 48 9
2020-12-05 1041 13 39 1
2020-12-04 1028 27 38 2
2020-12-03 1001 20 36 0
2020-12-02 981 37 36 1
2020-12-01 944 1 35 0
2020-11-30 943 44 35 5
2020-11-29 899 29 30 0
2020-11-28 870 19 30 0
2020-11-27 851 36 30 2
2020-11-26 815 11 28 0
2020-11-25 804 18 28 1
2020-11-24 786 16 27 0
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2020-11-21 745 15 26 1
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2020-11-19 701 7 22 0
2020-11-18 694 16 22 0
2020-11-17 678 20 22 2
2020-11-16 658 8 20 0
2020-11-15 650 7 20 0
2020-11-14 643 5 20 0
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2020-11-11 603 16 20 0
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2020-11-06 521 38 20 0
2020-11-05 483 17 20 0
2020-11-04 466 34 20 1
2020-11-03 432 16 19 1
2020-11-02 416 7 18 0
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2020-10-31 401 20 18 0
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2020-10-28 358 14 16 0
2020-10-27 344 5 16 0
2020-10-26 339 3 16 1
2020-10-25 336 5 15 0
2020-10-24 331 5 15 0
2020-10-23 326 17 15 0
2020-10-22 309 9 15 0
2020-10-21 300 6 15 0
2020-10-20 294 7 15 0
2020-10-19 287 3 15 0
2020-10-18 284 7 15 0
2020-10-17 277 6 15 0
2020-10-16 271 12 15 0
2020-10-15 259 10 15 1
2020-10-14 249 14 14 1
2020-10-13 235 19 13 1
2020-10-12 216 1 12 0
2020-10-11 215 1 12 0
2020-10-10 214 15 12 0
2020-10-09 199 14 12 0
2020-10-08 185 4 12 0
2020-10-07 181 3 12 0
2020-09-25 178 1 12 0
2020-09-24 177 2 12 0
2020-09-22 175 1 12 0
2020-09-21 174 1 12 0
2020-09-18 173 2 12 0
2020-09-02 171 1 12 0
2020-08-29 170 2 12 0
2020-08-27 168 1 12 0
2020-08-26 167 1 12 0
2020-08-22 166 2 12 0
2020-08-20 164 1 12 0
2020-08-08 163 1 12 0
2020-07-07 162 1 12 0
2020-07-02 161 2 12 0
2020-06-11 159 1 12 0
2020-06-09 158 1 12 0
2020-06-05 157 1 12 0
2020-05-30 156 2 12 1
2020-05-29 154 1 11 0
2020-05-27 153 1 11 0
2020-05-26 152 1 11 0
2020-05-21 151 6 11 0
2020-05-20 145 2 11 0
2020-05-19 143 4 11 0
2020-05-16 139 1 11 0
2020-05-12 138 2 11 0
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2020-05-07 135 1 11 0
2020-05-04 134 1 11 0
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2020-04-25 114 1 7 0
2020-04-23 113 2 7 0
2020-04-22 111 4 7 0
2020-04-18 107 2 7 0
2020-04-17 105 2 7 0
2020-04-16 103 5 7 0
2020-04-15 98 3 7 0
2020-04-14 95 3 7 0
2020-04-13 92 3 7 0
2020-04-10 89 4 7 2
2020-04-09 85 6 5 0
2020-04-08 79 4 5 0
2020-04-07 75 4 5 3
2020-04-06 71 1 2 0
2020-04-04 70 4 2 0
2020-04-03 66 2 2 0
2020-04-02 64 3 2 0
2020-04-01 61 4 2 0
2020-03-31 57 5 2 1
2020-03-30 52 1 1 0
2020-03-29 51 2 1 0
2020-03-28 49 5 1 0
2020-03-27 44 4 1 0
2020-03-26 40 3 1 0
2020-03-25 37 7 1 0
2020-03-24 30 10 1 0
2020-03-23 20 1 1 0
2020-03-22 19 1 1 0
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2020-03-20 8 4 0 0
2020-03-19 4 1 0 0
2020-03-15 3 1 0 0
2020-03-14 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: 0:02:56.435485