Germany: LK Uckermark (Brandenburg)¶

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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 12:21:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Uckermark", weeks=5);
2023-03-07T12:21:23.963386 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 7-day incidence rate (per 100K people) 41.8 LK Uckermark, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 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 Uckermark new deaths (rolling 7d mean) Germany-LK Uckermark 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 Uckermark cases daily growth factor Germany-LK Uckermark cases daily growth factor (rolling mean) Germany-LK Uckermark 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 Uckermark deaths daily growth factor Germany-LK Uckermark deaths daily growth factor (rolling mean) Germany-LK Uckermark 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 Uckermark doubling time cases (rolling mean) 0.00 5.87 11.73 17.60 23.47 daily change Germany-LK Uckermark new cases (rolling 7d mean) Germany-LK Uckermark new cases 0.000 0.257 0.513 0.770
In [4]:
overview(country="Germany", subregion="LK Uckermark");
2023-03-07T12:21:39.174863 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) 41.8 LK Uckermark, 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 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 Uckermark cases daily growth factor Germany-LK Uckermark cases daily growth factor (rolling mean) Germany-LK Uckermark 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 Uckermark deaths daily growth factor Germany-LK Uckermark deaths daily growth factor (rolling mean) Germany-LK Uckermark 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 Uckermark doubling time cases (rolling mean) Germany-LK Uckermark doubling time deaths (rolling mean) 0.0 234.7 469.3 704.0 daily change Germany-LK Uckermark new cases (rolling 7d mean) Germany-LK Uckermark new cases 0.000 2.347 4.693 7.040 daily change Germany-LK Uckermark new deaths (rolling 7d mean) Germany-LK Uckermark new deaths 0.0 56.6 113.3 169.9 226.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Uckermark", dates="2020-03-15:");
2023-03-07T12:23:57.854108 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 Uckermark LK Uckermark 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 Uckermark Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Uckermark")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Uckermark")
print(f'Population of country="Germany", subregion="LK Uckermark": {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 Uckermark": 117336 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 48273 10 292 0
2023-03-03 48263 7 292 0
2023-03-02 48256 8 292 0
2023-03-01 48248 8 292 0
2023-02-28 48240 16 292 0
2023-02-27 48224 15 292 0
2023-02-24 48209 4 292 0
2023-02-23 48205 15 292 0
2023-02-22 48190 9 292 0
2023-02-21 48181 12 292 0
2023-02-20 48169 19 292 0
2023-02-17 48150 8 292 0
2023-02-16 48142 7 292 0
2023-02-15 48135 20 292 0
2023-02-14 48115 14 292 0
2023-02-13 48101 24 292 0
2023-02-10 48077 4 292 0
2023-02-09 48073 7 292 0
2023-02-08 48066 18 292 0
2023-02-07 48048 24 292 0
2023-02-06 48024 14 292 0
2023-02-03 48010 16 292 0
2023-02-02 47994 3 292 0
2023-02-01 47991 9 292 0
2023-01-31 47982 14 292 0
2023-01-30 47968 10 292 0
2023-01-27 47958 2 292 0
2023-01-26 47956 10 292 0
2023-01-25 47946 8 292 0
2023-01-24 47938 9 292 0
2023-01-23 47929 10 292 0
2023-01-20 47919 4 292 0
2023-01-19 47915 9 292 0
2023-01-18 47906 12 292 0
2023-01-17 47894 21 292 0
2023-01-16 47873 17 292 0
2023-01-13 47856 10 292 0
2023-01-12 47846 12 292 0
2023-01-11 47834 22 292 0
2023-01-10 47812 38 292 0
2023-01-09 47774 42 292 0
2023-01-06 47732 26 292 0
2023-01-05 47706 49 292 0
2023-01-04 47657 33 292 0
2023-01-03 47624 63 292 1
2023-01-02 47561 50 291 0
2022-12-30 47511 49 291 0
2022-12-29 47462 49 291 0
2022-12-28 47413 89 291 0
2022-12-27 47324 73 291 0
2022-12-23 47251 49 291 2
2022-12-22 47202 73 289 0
2022-12-21 47129 104 289 1
2022-12-20 47025 98 288 0
2022-12-19 46927 104 288 0
2022-12-16 46823 41 288 0
2022-12-15 46782 75 288 0
2022-12-14 46707 58 288 0
2022-12-13 46649 114 288 0
2022-12-12 46535 107 288 2
2022-12-09 46428 49 286 0
2022-12-08 46379 71 286 1
2022-12-07 46308 107 285 0
2022-12-06 46201 106 285 0
2022-12-05 46095 76 285 1
2022-12-02 46019 73 284 1
2022-12-01 45946 66 283 0
2022-11-30 45880 91 283 0
2022-11-29 45789 97 283 0
2022-11-28 45692 89 283 0
2022-11-25 45603 29 283 0
2022-11-24 45574 50 283 0
2022-11-23 45524 51 283 0
2022-11-22 45473 92 283 0
2022-11-21 45381 35 283 0
2022-11-18 45346 29 283 0
2022-11-17 45317 14 283 0
2022-11-16 45303 56 283 0
2022-11-15 45247 58 283 1
2022-11-14 45189 51 282 1
2022-11-11 45138 19 281 0
2022-11-10 45119 51 281 1
2022-11-09 45068 59 280 0
2022-11-08 45009 126 280 0
2022-11-07 44883 79 280 0
2022-11-04 44804 64 280 0
2022-11-03 44740 79 280 0
2022-11-02 44661 97 280 0
2022-11-01 44564 129 280 3
2022-10-28 44435 66 277 1
2022-10-27 44369 81 276 0
2022-10-26 44288 116 276 0
2022-10-25 44172 157 276 0
2022-10-24 44015 142 276 0
2022-10-21 43873 102 276 0
2022-10-20 43771 107 276 0
2022-10-19 43664 173 276 0
2022-10-18 43491 189 276 0
2022-10-17 43302 179 276 2
2022-10-14 43123 134 274 0
2022-10-13 42989 106 274 1
2022-10-12 42883 249 273 1
2022-10-11 42634 225 272 0
2022-10-10 42409 209 272 0
2022-10-07 42200 180 272 0
2022-10-06 42020 228 272 1
2022-10-05 41792 226 271 0
2022-10-04 41566 186 271 0
2022-09-30 41380 97 271 0
2022-09-29 41283 98 271 1
2022-09-28 41185 188 270 0
2022-09-27 40997 96 270 0
2022-09-26 40901 90 270 0
2022-09-23 40811 68 270 0
2022-09-22 40743 46 270 0
2022-09-21 40697 84 270 0
2022-09-20 40613 111 270 0
2022-09-19 40502 73 270 0
2022-09-16 40429 73 270 0
2022-09-15 40356 31 270 0
2022-09-14 40325 92 270 0
2022-09-13 40233 92 270 0
2022-09-12 40141 52 270 0
2022-09-09 40089 51 270 0
2022-09-08 40038 66 270 0
2022-09-07 39972 109 270 0
2022-09-06 39863 38 270 0
2022-09-05 39825 36 270 0
2022-09-02 39789 33 270 0
2022-09-01 39756 40 270 0
2022-08-31 39716 39 270 0
2022-08-30 39677 63 270 0
2022-08-29 39614 72 270 1
2022-08-26 39542 39 269 0
2022-08-25 39503 53 269 1
2022-08-24 39450 87 268 0
2022-08-23 39363 91 268 0
2022-08-22 39272 78 268 0
2022-08-19 39194 48 268 0
2022-08-18 39146 61 268 1
2022-08-17 39085 118 267 0
2022-08-16 38967 80 267 1
2022-08-15 38887 99 266 0
2022-08-12 38788 64 266 0
2022-08-11 38724 80 266 0
2022-08-10 38644 104 266 0
2022-08-09 38540 105 266 1
2022-08-08 38435 84 265 0
2022-08-05 38351 72 265 1
2022-08-04 38279 47 264 2
2022-08-03 38232 93 262 3
2022-08-02 38139 104 259 0
2022-08-01 38035 63 259 1
2022-07-29 37972 50 258 1
2022-07-28 37922 51 257 0
2022-07-27 37871 87 257 0
2022-07-26 37784 116 257 0
2022-07-25 37668 124 257 0
2022-07-22 37544 57 257 1
2022-07-21 37487 67 256 1
2022-07-20 37420 122 255 0
2022-07-19 37298 85 255 0
2022-07-18 37213 104 255 0
2022-07-15 37109 33 255 0
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2022-04-28 33970 67 252 0
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2022-04-26 33811 150 252 0
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2022-04-22 33551 56 252 0
2022-04-21 33495 137 252 0
2022-04-20 33358 214 252 0
2022-04-19 33144 180 252 1
2022-04-14 32964 50 251 0
2022-04-13 32914 218 251 0
2022-04-12 32696 105 251 0
2022-04-11 32591 161 251 0
2022-04-08 32430 96 251 0
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2022-04-04 31814 182 251 1
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2021-03-22 2895 3 144 0
2021-03-21 2892 1 144 0
2021-03-20 2891 4 144 0
2021-03-19 2887 4 144 0
2021-03-18 2883 12 144 0
2021-03-17 2871 9 144 0
2021-03-16 2862 17 144 0
2021-03-15 2845 1 144 0
2021-03-14 2844 8 144 0
2021-03-13 2836 4 144 0
2021-03-12 2832 10 144 0
2021-03-11 2822 6 144 0
2021-03-10 2816 9 144 0
2021-03-09 2807 5 144 2
2021-03-08 2802 7 142 0
2021-03-06 2795 1 142 0
2021-03-05 2794 11 142 0
2021-03-04 2783 6 142 0
2021-03-03 2777 12 142 0
2021-03-02 2765 12 142 0
2021-03-01 2753 3 142 0
2021-02-28 2750 7 142 0
2021-02-27 2743 5 142 0
2021-02-26 2738 6 142 0
2021-02-25 2732 15 142 1
2021-02-24 2717 9 141 0
2021-02-23 2708 8 141 0
2021-02-21 2700 9 141 0
2021-02-20 2691 5 141 0
2021-02-19 2686 1 141 0
2021-02-18 2685 5 141 0
2021-02-17 2680 16 141 0
2021-02-16 2664 9 141 0
2021-02-15 2655 3 141 1
2021-02-14 2652 9 140 0
2021-02-12 2643 14 140 1
2021-02-11 2629 24 139 0
2021-02-10 2605 18 139 0
2021-02-09 2587 14 139 1
2021-02-08 2573 20 138 0
2021-02-06 2553 14 138 0
2021-02-05 2539 8 138 1
2021-02-04 2531 11 137 0
2021-02-03 2520 33 137 2
2021-02-02 2487 8 135 1
2021-02-01 2479 3 134 0
2021-01-30 2476 28 134 1
2021-01-29 2448 27 133 4
2021-01-28 2421 37 129 3
2021-01-27 2384 37 126 3
2021-01-26 2347 41 123 6
2021-01-25 2306 43 117 5
2021-01-23 2263 32 112 4
2021-01-22 2231 33 108 2
2021-01-21 2198 47 106 5
2021-01-20 2151 87 101 4
2021-01-19 2064 39 97 2
2021-01-18 2025 17 95 1
2021-01-17 2008 16 94 0
2021-01-16 1992 49 94 4
2021-01-15 1943 55 90 1
2021-01-14 1888 50 89 3
2021-01-13 1838 51 86 4
2021-01-12 1787 42 82 7
2021-01-11 1745 77 75 5
2021-01-10 1668 24 70 4
2021-01-09 1644 32 66 3
2021-01-08 1612 65 63 3
2021-01-07 1547 40 60 0
2021-01-06 1507 68 60 2
2021-01-05 1439 63 58 3
2021-01-04 1376 57 55 3
2021-01-03 1319 31 52 3
2021-01-02 1288 22 49 0
2020-12-30 1266 57 49 0
2020-12-29 1209 16 49 2
2020-12-28 1193 43 47 1
2020-12-27 1150 26 46 2
2020-12-26 1124 43 44 1
2020-12-23 1081 30 43 0
2020-12-22 1051 41 43 2
2020-12-21 1010 45 41 0
2020-12-20 965 29 41 0
2020-12-19 936 36 41 1
2020-12-18 900 34 40 8
2020-12-17 866 9 32 0
2020-12-16 857 18 32 2
2020-12-15 839 20 30 2
2020-12-14 819 38 28 7
2020-12-11 781 16 21 0
2020-12-10 765 22 21 0
2020-12-09 743 26 21 0
2020-12-08 717 19 21 0
2020-12-07 698 19 21 0
2020-12-04 679 34 21 0
2020-12-03 645 18 21 1
2020-12-02 627 27 20 0
2020-12-01 600 20 20 1
2020-11-30 580 30 19 2
2020-11-29 550 4 17 0
2020-11-27 546 20 17 1
2020-11-26 526 22 16 2
2020-11-25 504 18 14 0
2020-11-24 486 26 14 0
2020-11-23 460 35 14 2
2020-11-22 425 13 12 0
2020-11-21 412 25 12 0
2020-11-20 387 9 12 0
2020-11-19 378 21 12 0
2020-11-18 357 5 12 1
2020-11-17 352 5 11 0
2020-11-16 347 17 11 0
2020-11-15 330 9 11 0
2020-11-13 321 11 11 0
2020-11-12 310 16 11 0
2020-11-11 294 11 11 0
2020-11-09 283 3 11 0
2020-11-08 280 3 11 0
2020-11-07 277 8 11 0
2020-11-06 269 11 11 0
2020-11-05 258 12 11 1
2020-11-04 246 27 10 0
2020-11-03 219 17 10 0
2020-11-02 202 1 10 0
2020-11-01 201 2 10 0
2020-10-31 199 11 10 0
2020-10-30 188 21 10 0
2020-10-29 167 19 10 1
2020-10-28 148 12 9 0
2020-10-27 136 16 9 0
2020-10-26 120 11 9 1
2020-10-25 109 2 8 0
2020-10-24 107 5 8 0
2020-10-23 102 4 8 1
2020-10-22 98 12 7 0
2020-10-21 86 6 7 1
2020-10-20 80 1 6 1
2020-10-19 79 5 5 2
2020-10-17 74 1 3 0
2020-10-16 73 5 3 1
2020-10-14 68 3 2 0
2020-10-12 65 1 2 0
2020-10-09 64 4 2 0
2020-10-07 60 2 2 0
2020-10-06 58 1 2 0
2020-10-05 57 1 2 0
2020-10-03 56 2 2 0
2020-10-02 54 2 2 0
2020-10-01 52 1 2 0
2020-09-26 51 1 2 0
2020-09-19 50 1 2 0
2020-09-08 49 1 2 0
2020-09-04 48 1 2 0
2020-08-30 47 1 2 0
2020-08-12 46 1 2 0
2020-08-04 45 1 2 0
2020-06-30 44 1 2 0
2020-06-18 43 1 2 0
2020-06-17 42 3 2 0
2020-06-16 39 2 2 0
2020-06-13 37 1 2 0
2020-05-27 36 1 2 0
2020-04-30 35 1 2 0
2020-04-21 34 2 2 0
2020-04-20 32 1 2 0
2020-04-18 31 1 2 0
2020-04-17 30 2 2 0
2020-04-12 28 1 2 0
2020-04-11 27 1 2 0
2020-04-10 26 3 2 0
2020-04-07 23 1 2 0
2020-04-06 22 1 2 0
2020-04-02 21 1 2 0
2020-04-01 20 2 2 0
2020-03-30 18 8 2 1
2020-03-28 10 1 1 0
2020-03-27 9 1 1 0
2020-03-26 8 3 1 1
2020-03-24 5 1 0 0
2020-03-18 4 3 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: 0:03:26.098867