Germany: SK Berlin Treptow-Köpenick (Berlin)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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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 12:06:49 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Treptow-Köpenick", weeks=5);
2023-03-07T12:07:07.885678 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 48.3 SK Berlin Treptow-Köpenick, 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 0.0 0.1 0.2 0.3 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 Berlin Treptow-Köpenick cases daily growth factor Germany-SK Berlin Treptow-Köpenick cases daily growth factor (rolling mean) Germany-SK Berlin Treptow-Köpenick 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 R & growth factor (based on deaths) Germany-SK Berlin Treptow-Köpenick deaths daily growth factor Germany-SK Berlin Treptow-Köpenick deaths daily growth factor (rolling mean) Germany-SK Berlin Treptow-Köpenick 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-SK Berlin Treptow-Köpenick doubling time cases (rolling mean) 0.00 13.66 27.32 40.98 54.64 daily change Germany-SK Berlin Treptow-Köpenick new cases (rolling 7d mean) Germany-SK Berlin Treptow-Köpenick new cases 0.000 0.273 0.546 0.820 daily change Germany-SK Berlin Treptow-Köpenick new deaths (rolling 7d mean) Germany-SK Berlin Treptow-Köpenick new deaths 0.000 0.325 0.650 0.975
In [4]:
overview(country="Germany", subregion="SK Berlin Treptow-Köpenick");
2023-03-07T12:07:27.872784 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 0 500 500 1000 1000 1500 1500 7-day incidence rate (per 100K people) 48.3 SK Berlin Treptow-Köpenick, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 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-SK Berlin Treptow-Köpenick cases daily growth factor Germany-SK Berlin Treptow-Köpenick cases daily growth factor (rolling mean) Germany-SK Berlin Treptow-Köpenick 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 R & growth factor (based on deaths) Germany-SK Berlin Treptow-Köpenick deaths daily growth factor Germany-SK Berlin Treptow-Köpenick deaths daily growth factor (rolling mean) Germany-SK Berlin Treptow-Köpenick 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-SK Berlin Treptow-Köpenick doubling time cases (rolling mean) Germany-SK Berlin Treptow-Köpenick doubling time deaths (rolling mean) 0 273 546 820 1093 daily change Germany-SK Berlin Treptow-Köpenick new cases (rolling 7d mean) Germany-SK Berlin Treptow-Köpenick new cases 0.00 2.73 5.46 8.20 10.93 daily change Germany-SK Berlin Treptow-Köpenick new deaths (rolling 7d mean) Germany-SK Berlin Treptow-Köpenick new deaths 0.0 106.3 212.6 319.0 425.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Treptow-Köpenick", dates="2020-03-15:");
2023-03-07T12:09:36.525269 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 Berlin Treptow-Köpenick SK Berlin Treptow-Köpenick 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 Berlin Treptow-Köpenick Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Treptow-Köpenick")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Treptow-Köpenick")
print(f'Population of country="Germany", subregion="SK Berlin Treptow-Köpenick": {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 Berlin Treptow-Köpenick": 273188 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 101900 18 414 0
2023-03-03 101882 9 414 0
2023-03-02 101873 31 414 0
2023-03-01 101842 21 414 0
2023-02-28 101821 53 414 0
2023-02-27 101768 56 414 0
2023-02-24 101712 43 414 0
2023-02-23 101669 29 414 0
2023-02-22 101640 32 414 0
2023-02-21 101608 41 414 0
2023-02-20 101567 41 414 0
2023-02-17 101526 26 414 0
2023-02-16 101500 27 414 0
2023-02-15 101473 51 414 0
2023-02-14 101422 27 414 0
2023-02-13 101395 68 414 0
2023-02-10 101327 43 414 0
2023-02-09 101284 37 414 0
2023-02-08 101247 40 414 1
2023-02-07 101207 19 413 0
2023-02-06 101188 40 413 0
2023-02-03 101148 56 413 0
2023-02-02 101092 29 413 0
2023-02-01 101063 23 413 0
2023-01-31 101040 43 413 0
2023-01-30 100997 42 413 0
2023-01-27 100955 28 413 1
2023-01-26 100927 34 412 0
2023-01-25 100893 39 412 0
2023-01-24 100854 19 412 1
2023-01-23 100835 44 411 1
2023-01-20 100791 42 410 0
2023-01-19 100749 35 410 0
2023-01-18 100714 26 410 0
2023-01-17 100688 34 410 0
2023-01-16 100654 61 410 0
2023-01-13 100593 33 410 0
2023-01-12 100560 43 410 2
2023-01-11 100517 49 408 0
2023-01-10 100468 57 408 0
2023-01-09 100411 78 408 0
2023-01-06 100333 65 408 0
2023-01-05 100268 60 408 0
2023-01-04 100208 84 408 0
2023-01-03 100124 54 408 0
2023-01-02 100070 121 408 1
2022-12-30 99949 88 407 2
2022-12-29 99861 109 405 2
2022-12-28 99752 135 403 2
2022-12-27 99617 154 401 0
2022-12-23 99463 94 401 0
2022-12-22 99369 92 401 0
2022-12-21 99277 139 401 2
2022-12-20 99138 137 399 2
2022-12-19 99001 207 397 0
2022-12-16 98794 106 397 0
2022-12-15 98688 176 397 1
2022-12-14 98512 121 396 0
2022-12-13 98391 169 396 0
2022-12-12 98222 166 396 0
2022-12-10 98056 1 396 0
2022-12-09 98055 63 396 0
2022-12-08 97992 115 396 0
2022-12-07 97877 149 396 3
2022-12-06 97728 152 393 0
2022-12-05 97576 214 393 0
2022-12-03 97362 1 393 0
2022-12-02 97361 132 393 1
2022-12-01 97229 107 392 1
2022-11-30 97122 197 391 0
2022-11-29 96925 145 391 1
2022-11-28 96780 183 390 0
2022-11-25 96597 82 390 0
2022-11-24 96515 93 390 0
2022-11-23 96422 95 390 1
2022-11-22 96327 89 389 1
2022-11-21 96238 141 388 0
2022-11-18 96097 86 388 1
2022-11-17 96011 67 387 0
2022-11-16 95944 107 387 1
2022-11-15 95837 116 386 0
2022-11-14 95721 169 386 1
2022-11-11 95552 58 385 0
2022-11-10 95494 85 385 0
2022-11-09 95409 136 385 1
2022-11-08 95273 135 384 0
2022-11-07 95138 170 384 0
2022-11-04 94968 75 384 0
2022-11-03 94893 135 384 0
2022-11-02 94758 125 384 0
2022-11-01 94633 181 384 3
2022-10-31 94452 209 381 1
2022-10-28 94243 151 380 0
2022-10-27 94092 157 380 0
2022-10-26 93935 239 380 1
2022-10-25 93696 279 379 1
2022-10-24 93417 289 378 1
2022-10-21 93128 199 377 1
2022-10-20 92929 219 376 0
2022-10-19 92710 335 376 0
2022-10-18 92375 290 376 1
2022-10-17 92085 318 375 1
2022-10-14 91767 260 374 0
2022-10-13 91507 221 374 1
2022-10-12 91286 266 373 1
2022-10-11 91020 314 372 0
2022-10-10 90706 321 372 0
2022-10-07 90385 289 372 0
2022-10-06 90096 216 372 0
2022-10-05 89880 234 372 0
2022-10-04 89646 290 372 0
2022-10-03 89356 3 372 0
2022-09-30 89353 165 372 1
2022-09-29 89188 141 371 0
2022-09-28 89047 234 371 0
2022-09-27 88813 185 371 2
2022-09-26 88628 253 369 2
2022-09-23 88375 158 367 1
2022-09-22 88217 94 366 0
2022-09-21 88123 169 366 1
2022-09-20 87954 103 365 0
2022-09-19 87851 203 365 0
2022-09-16 87648 95 365 0
2022-09-15 87553 102 365 0
2022-09-14 87451 163 365 0
2022-09-13 87288 124 365 0
2022-09-12 87164 208 365 0
2022-09-09 86956 64 365 1
2022-09-08 86892 110 364 0
2022-09-07 86782 127 364 0
2022-09-06 86655 95 364 0
2022-09-05 86560 176 364 0
2022-09-02 86384 101 364 0
2022-09-01 86283 90 364 0
2022-08-31 86193 107 364 0
2022-08-30 86086 186 364 1
2022-08-29 85900 86 363 0
2022-08-26 85814 114 363 0
2022-08-25 85700 144 363 0
2022-08-24 85556 402 363 0
2022-08-23 85154 40 363 0
2022-08-22 85114 75 363 0
2022-08-19 85039 43 363 0
2022-08-18 84996 82 363 0
2022-08-17 84914 102 363 0
2022-08-16 84812 132 363 1
2022-08-15 84680 248 362 1
2022-08-12 84432 123 361 0
2022-08-11 84309 125 361 0
2022-08-10 84184 152 361 0
2022-08-09 84032 180 361 0
2022-08-08 83852 216 361 0
2022-08-05 83636 96 361 0
2022-08-04 83540 168 361 2
2022-08-03 83372 155 359 0
2022-08-02 83217 228 359 0
2022-08-01 82989 271 359 2
2022-07-29 82718 159 357 0
2022-07-28 82559 140 357 0
2022-07-27 82419 224 357 1
2022-07-26 82195 267 356 1
2022-07-25 81928 357 355 0
2022-07-24 81571 1 355 0
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2022-06-28 76551 425 343 0
2022-06-27 76126 270 343 0
2022-06-26 75856 7 343 0
2022-06-25 75849 10 343 0
2022-06-24 75839 265 343 0
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2022-06-11 73520 1 342 0
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2022-05-30 72687 102 342 0
2022-05-29 72585 1 342 0
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2022-05-26 72493 1 342 0
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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: 0:03:04.297856