Germany: SK Gera (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 16:16:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Gera", weeks=5);
2023-03-07T16:16:32.418042 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 20 20 30 30 40 40 7-day incidence rate (per 100K people) 36.1 SK Gera, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 2.5 5.0 7.5 10.0 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 R & growth factor (based on cases) Germany-SK Gera cases daily growth factor Germany-SK Gera cases daily growth factor (rolling mean) Germany-SK Gera estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Gera deaths daily growth factor Germany-SK Gera deaths daily growth factor (rolling mean) Germany-SK Gera estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Gera doubling time cases (rolling mean) 0.00 2.28 4.57 6.85 9.14 daily change Germany-SK Gera new cases (rolling 7d mean) Germany-SK Gera new cases 0.000 0.228 0.457 0.685 0.914 daily change Germany-SK Gera new deaths (rolling 7d mean) Germany-SK Gera new deaths 0.000 0.220 0.440 0.659 0.879
In [4]:
overview(country="Germany", subregion="SK Gera");
2023-03-07T16:16:45.776591 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 7-day incidence rate (per 100K people) 36.1 SK Gera, 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 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 R & growth factor (based on cases) Germany-SK Gera cases daily growth factor Germany-SK Gera cases daily growth factor (rolling mean) Germany-SK Gera estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Gera deaths daily growth factor Germany-SK Gera deaths daily growth factor (rolling mean) Germany-SK Gera 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 15000 20000 cases doubling time [days] Germany-SK Gera doubling time cases (rolling mean) Germany-SK Gera doubling time deaths (rolling mean) 0.0 182.7 365.5 daily change Germany-SK Gera new cases (rolling 7d mean) Germany-SK Gera new cases 0.000 1.827 3.655 5.482 daily change Germany-SK Gera new deaths (rolling 7d mean) Germany-SK Gera new deaths 0.0 93.8 187.5 281.3 375.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Gera", dates="2020-03-15:");
2023-03-07T16:19:09.550328 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: SK Gera SK Gera 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 SK Gera Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Gera")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Gera")
print(f'Population of country="Germany", subregion="SK Gera": {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="SK Gera": 91368 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 36829 9 411 0
2023-03-03 36820 2 411 0
2023-03-02 36818 8 411 0
2023-03-01 36810 8 411 0
2023-02-28 36802 6 411 0
2023-02-27 36796 3 411 0
2023-02-24 36793 5 411 0
2023-02-23 36788 5 411 0
2023-02-22 36783 7 411 0
2023-02-21 36776 7 411 0
2023-02-20 36769 10 411 1
2023-02-17 36759 5 410 0
2023-02-16 36754 6 410 0
2023-02-15 36748 8 410 1
2023-02-14 36740 7 409 0
2023-02-13 36733 7 409 0
2023-02-10 36726 5 409 0
2023-02-09 36721 1 409 0
2023-02-08 36720 8 409 0
2023-02-07 36712 4 409 0
2023-02-06 36708 5 409 0
2023-02-03 36703 6 409 0
2023-02-02 36697 5 409 0
2023-02-01 36692 5 409 0
2023-01-31 36687 4 409 0
2023-01-30 36683 3 409 0
2023-01-27 36680 2 409 0
2023-01-26 36678 3 409 0
2023-01-25 36675 1 409 0
2023-01-24 36674 4 409 0
2023-01-23 36670 3 409 0
2023-01-20 36667 15 409 0
2023-01-19 36652 8 409 0
2023-01-18 36644 2 409 0
2023-01-17 36642 5 409 0
2023-01-16 36637 6 409 0
2023-01-13 36631 4 409 0
2023-01-12 36627 7 409 0
2023-01-11 36620 8 409 0
2023-01-10 36612 9 409 0
2023-01-09 36603 16 409 0
2023-01-06 36587 8 409 0
2023-01-05 36579 12 409 0
2023-01-04 36567 9 409 0
2023-01-03 36558 18 409 0
2023-01-02 36540 18 409 1
2022-12-30 36522 8 408 1
2022-12-29 36514 21 407 1
2022-12-28 36493 15 406 0
2022-12-27 36478 66 406 7
2022-12-23 36412 12 399 0
2022-12-22 36400 32 399 0
2022-12-21 36368 53 399 1
2022-12-20 36315 23 398 0
2022-12-19 36292 29 398 2
2022-12-16 36263 28 396 1
2022-12-15 36235 31 395 0
2022-12-14 36204 58 395 1
2022-12-13 36146 18 394 0
2022-12-12 36128 50 394 0
2022-12-09 36078 39 394 0
2022-12-08 36039 42 394 0
2022-12-07 35997 18 394 0
2022-12-06 35979 16 394 1
2022-12-05 35963 70 393 3
2022-12-02 35893 27 390 1
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2022-11-30 35831 51 388 0
2022-11-29 35780 31 388 0
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2021-03-26 3889 78 145 4
2021-03-25 3811 46 141 0
2021-03-24 3765 82 141 3
2021-03-23 3683 28 138 2
2021-03-22 3655 40 136 2
2021-03-20 3615 47 134 0
2021-03-19 3568 99 134 1
2021-03-18 3469 45 133 3
2021-03-17 3424 38 130 2
2021-03-16 3386 56 128 2
2021-03-14 3330 4 126 0
2021-03-13 3326 33 126 2
2021-03-12 3293 37 124 0
2021-03-11 3256 16 124 0
2021-03-10 3240 28 124 0
2021-03-09 3212 12 124 0
2021-03-08 3200 24 124 1
2021-03-06 3176 13 123 0
2021-03-05 3163 10 123 1
2021-03-04 3153 13 122 0
2021-03-03 3140 30 122 0
2021-03-02 3110 17 122 1
2021-03-01 3093 1 121 0
2021-02-28 3092 5 121 0
2021-02-27 3087 10 121 0
2021-02-26 3077 21 121 0
2021-02-25 3056 7 121 0
2021-02-24 3049 25 121 0
2021-02-23 3024 15 121 1
2021-02-22 3009 10 120 0
2021-02-20 2999 8 120 0
2021-02-19 2991 28 120 1
2021-02-18 2963 11 119 0
2021-02-17 2952 20 119 0
2021-02-16 2932 17 119 0
2021-02-15 2915 7 119 0
2021-02-13 2908 12 119 1
2021-02-12 2896 11 118 0
2021-02-11 2885 23 118 1
2021-02-10 2862 17 117 0
2021-02-09 2845 6 117 0
2021-02-08 2839 5 117 0
2021-02-06 2834 9 117 0
2021-02-05 2825 17 117 0
2021-02-04 2808 14 117 0
2021-02-03 2794 22 117 0
2021-02-02 2772 7 117 0
2021-02-01 2765 9 117 1
2021-01-30 2756 6 116 0
2021-01-29 2750 21 116 0
2021-01-28 2729 15 116 2
2021-01-27 2714 16 114 1
2021-01-26 2698 13 113 2
2021-01-25 2685 19 111 1
2021-01-23 2666 8 110 1
2021-01-22 2658 26 109 0
2021-01-21 2632 5 109 0
2021-01-20 2627 40 109 0
2021-01-19 2587 16 109 0
2021-01-18 2571 23 109 3
2021-01-16 2548 20 106 0
2021-01-15 2528 23 106 1
2021-01-14 2505 12 105 0
2021-01-13 2493 39 105 1
2021-01-12 2454 27 104 0
2021-01-11 2427 30 104 0
2021-01-09 2397 31 104 0
2021-01-08 2366 70 104 4
2021-01-07 2296 29 100 0
2021-01-06 2267 47 100 2
2021-01-05 2220 30 98 2
2021-01-04 2190 15 96 2
2021-01-02 2175 30 94 1
2020-12-31 2145 37 93 1
2020-12-30 2108 62 92 0
2020-12-29 2046 19 92 1
2020-12-28 2027 37 91 1
2020-12-26 1990 12 90 0
2020-12-25 1978 27 90 0
2020-12-24 1951 77 90 1
2020-12-23 1874 73 89 3
2020-12-22 1801 37 86 2
2020-12-21 1764 73 84 3
2020-12-19 1691 53 81 5
2020-12-18 1638 69 76 1
2020-12-17 1569 81 75 2
2020-12-16 1488 64 73 2
2020-12-15 1424 21 71 1
2020-12-14 1403 112 70 6
2020-12-12 1291 81 64 3
2020-12-11 1210 46 61 6
2020-12-10 1164 50 55 3
2020-12-09 1114 28 52 0
2020-12-08 1086 10 52 2
2020-12-07 1076 76 50 5
2020-12-05 1000 24 45 1
2020-12-04 976 21 44 1
2020-12-03 955 48 43 6
2020-12-02 907 21 37 3
2020-12-01 886 14 34 0
2020-11-30 872 21 34 0
2020-11-28 851 15 34 1
2020-11-27 836 42 33 2
2020-11-26 794 25 31 0
2020-11-25 769 24 31 0
2020-11-24 745 15 31 0
2020-11-23 730 13 31 0
2020-11-22 717 11 31 0
2020-11-21 706 14 31 1
2020-11-20 692 49 30 3
2020-11-19 643 9 27 0
2020-11-18 634 13 27 0
2020-11-17 621 7 27 0
2020-11-16 614 4 27 0
2020-11-14 610 7 27 0
2020-11-13 603 12 27 1
2020-11-12 591 10 26 0
2020-11-11 581 15 26 0
2020-11-10 566 4 26 0
2020-11-09 562 15 26 0
2020-11-07 547 8 26 0
2020-11-06 539 15 26 1
2020-11-05 524 13 25 0
2020-11-04 511 23 25 1
2020-11-03 488 10 24 0
2020-11-02 478 26 24 0
2020-10-31 452 4 24 0
2020-10-30 448 20 24 0
2020-10-29 428 16 24 1
2020-10-28 412 12 23 1
2020-10-27 400 4 22 0
2020-10-26 396 3 22 0
2020-10-24 393 3 22 0
2020-10-23 390 12 22 0
2020-10-22 378 6 22 0
2020-10-21 372 11 22 0
2020-10-20 361 1 22 0
2020-10-19 360 9 22 0
2020-10-17 351 9 22 0
2020-10-16 342 3 22 0
2020-10-15 339 5 22 0
2020-10-14 334 9 22 0
2020-10-13 325 1 22 0
2020-10-12 324 12 22 0
2020-10-09 312 9 22 0
2020-10-08 303 2 22 0
2020-10-07 301 8 22 0
2020-10-05 293 1 22 0
2020-10-04 292 1 22 0
2020-10-03 291 1 22 0
2020-10-02 290 3 22 0
2020-09-30 287 3 22 0
2020-09-25 284 2 22 0
2020-09-23 282 3 22 0
2020-09-21 279 1 22 0
2020-09-18 278 1 22 0
2020-09-13 277 3 22 0
2020-09-11 274 1 22 0
2020-09-04 273 2 22 0
2020-08-27 271 1 22 0
2020-08-12 270 3 22 0
2020-08-11 267 1 22 0
2020-08-08 266 1 22 0
2020-08-07 265 2 22 0
2020-08-03 263 1 22 0
2020-08-01 262 1 22 0
2020-07-28 261 1 22 0
2020-07-22 260 1 22 0
2020-07-17 259 1 22 0
2020-07-06 258 2 22 0
2020-07-01 256 1 22 0
2020-06-30 255 1 22 0
2020-06-26 254 1 22 0
2020-06-16 253 1 22 0
2020-06-08 252 1 22 0
2020-06-07 251 1 22 0
2020-06-05 250 4 22 0
2020-06-04 246 2 22 0
2020-06-03 244 5 22 0
2020-05-30 239 5 22 2
2020-05-29 234 3 20 0
2020-05-28 231 3 20 0
2020-05-27 228 1 20 0
2020-05-26 227 2 20 0
2020-05-25 225 9 20 1
2020-05-23 216 3 19 1
2020-05-22 213 7 18 1
2020-05-20 206 3 17 1
2020-05-19 203 1 16 0
2020-05-18 202 7 16 1
2020-05-16 195 1 15 0
2020-05-15 194 5 15 0
2020-05-14 189 4 15 1
2020-05-12 185 3 14 0
2020-05-11 182 2 14 0
2020-05-09 180 2 14 0
2020-05-08 178 3 14 0
2020-05-07 175 6 14 1
2020-05-06 169 6 13 0
2020-05-04 163 1 13 0
2020-05-02 162 4 13 0
2020-05-01 158 1 13 0
2020-04-30 157 7 13 1
2020-04-29 150 3 12 0
2020-04-28 147 8 12 0
2020-04-27 139 3 12 0
2020-04-26 136 3 12 0
2020-04-25 133 1 12 0
2020-04-24 132 9 12 0
2020-04-23 123 9 12 0
2020-04-22 114 6 12 0
2020-04-21 108 15 12 1
2020-04-20 93 3 11 1
2020-04-19 90 3 10 0
2020-04-18 87 8 10 0
2020-04-17 79 1 10 1
2020-04-16 78 5 9 0
2020-04-15 73 8 9 1
2020-04-14 65 11 8 4
2020-04-12 54 5 4 3
2020-04-11 49 2 1 0
2020-04-09 47 9 1 1
2020-04-08 38 2 0 0
2020-04-07 36 2 0 0
2020-04-06 34 1 0 0
2020-04-03 33 2 0 0
2020-04-02 31 8 0 0
2020-04-01 23 4 0 0
2020-03-28 19 5 0 0
2020-03-27 14 6 0 0
2020-03-26 8 1 0 0
2020-03-24 7 1 0 0
2020-03-23 6 2 0 0
2020-03-18 4 2 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: 0:04:13.321775