Germany: LK Dahme-Spreewald (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 12:13:05 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Dahme-Spreewald", weeks=5);
2023-03-07T12:13:19.899231 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 80 100 100 120 120 7-day incidence rate (per 100K people) 56.3 LK Dahme-Spreewald, 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 30 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 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 Dahme-Spreewald cases daily growth factor Germany-LK Dahme-Spreewald cases daily growth factor (rolling mean) Germany-LK Dahme-Spreewald 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-LK Dahme-Spreewald deaths daily growth factor Germany-LK Dahme-Spreewald deaths daily growth factor (rolling mean) Germany-LK Dahme-Spreewald 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 Dahme-Spreewald doubling time cases (rolling mean) 0.00 17.58 35.17 52.75 70.33 daily change Germany-LK Dahme-Spreewald new cases (rolling 7d mean) Germany-LK Dahme-Spreewald new cases 0.000 0.352 0.703 daily change Germany-LK Dahme-Spreewald new deaths (rolling 7d mean) Germany-LK Dahme-Spreewald new deaths 0.000 0.304 0.608 0.912
In [4]:
overview(country="Germany", subregion="LK Dahme-Spreewald");
2023-03-07T12:13:36.687269 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 2000 2000 7-day incidence rate (per 100K people) 56.3 LK Dahme-Spreewald, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Dahme-Spreewald cases daily growth factor Germany-LK Dahme-Spreewald cases daily growth factor (rolling mean) Germany-LK Dahme-Spreewald 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-LK Dahme-Spreewald deaths daily growth factor Germany-LK Dahme-Spreewald deaths daily growth factor (rolling mean) Germany-LK Dahme-Spreewald estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Dahme-Spreewald doubling time cases (rolling mean) Germany-LK Dahme-Spreewald doubling time deaths (rolling mean) 0.0 175.8 351.7 527.5 703.3 daily change Germany-LK Dahme-Spreewald new cases (rolling 7d mean) Germany-LK Dahme-Spreewald new cases 0.00 3.52 7.03 10.55 daily change Germany-LK Dahme-Spreewald new deaths (rolling 7d mean) Germany-LK Dahme-Spreewald new deaths 0.0 118.5 236.9 355.4 473.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Dahme-Spreewald", dates="2020-03-15:");
2023-03-07T12:16:28.416496 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 Dahme-Spreewald LK Dahme-Spreewald 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 Dahme-Spreewald Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Dahme-Spreewald")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Dahme-Spreewald")
print(f'Population of country="Germany", subregion="LK Dahme-Spreewald": {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 Dahme-Spreewald": 175834 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 80109 11 444 0
2023-03-03 80098 5 444 0
2023-03-02 80093 9 444 0
2023-03-01 80084 37 444 0
2023-02-28 80047 37 444 0
2023-02-27 80010 49 444 0
2023-02-24 79961 21 444 0
2023-02-23 79940 74 444 0
2023-02-21 79866 28 444 0
2023-02-20 79838 32 444 0
2023-02-17 79806 12 444 0
2023-02-16 79794 28 444 0
2023-02-15 79766 24 444 0
2023-02-14 79742 44 444 0
2023-02-13 79698 34 444 0
2023-02-10 79664 21 444 0
2023-02-09 79643 29 444 0
2023-02-08 79614 24 444 0
2023-02-07 79590 47 444 0
2023-02-06 79543 44 444 0
2023-02-03 79499 14 444 0
2023-02-02 79485 21 444 0
2023-02-01 79464 27 444 1
2023-01-31 79437 37 443 0
2023-01-30 79400 39 443 1
2023-01-27 79361 13 442 0
2023-01-26 79348 30 442 0
2023-01-25 79318 30 442 0
2023-01-24 79288 28 442 0
2023-01-23 79260 30 442 0
2023-01-20 79230 9 442 0
2023-01-19 79221 30 442 0
2023-01-18 79191 13 442 0
2023-01-17 79178 25 442 0
2023-01-16 79153 34 442 1
2023-01-13 79119 19 441 0
2023-01-12 79100 22 441 0
2023-01-11 79078 44 441 1
2023-01-10 79034 32 440 0
2023-01-09 79002 79 440 0
2023-01-07 78923 1 440 0
2023-01-06 78922 40 440 1
2023-01-05 78882 42 439 0
2023-01-04 78840 73 439 0
2023-01-03 78767 76 439 0
2023-01-02 78691 63 439 0
2022-12-30 78628 66 439 0
2022-12-29 78562 87 439 0
2022-12-28 78475 114 439 0
2022-12-27 78361 163 439 0
2022-12-23 78198 143 439 1
2022-12-22 78055 211 438 0
2022-12-21 77844 77 438 0
2022-12-20 77767 130 438 0
2022-12-19 77637 198 438 0
2022-12-16 77439 109 438 2
2022-12-15 77330 83 436 0
2022-12-14 77247 94 436 0
2022-12-13 77153 148 436 0
2022-12-12 77005 107 436 1
2022-12-09 76898 119 435 1
2022-12-08 76779 110 434 0
2022-12-07 76669 103 434 0
2022-12-06 76566 146 434 1
2022-12-05 76420 188 433 0
2022-12-02 76232 98 433 0
2022-12-01 76134 97 433 0
2022-11-30 76037 140 433 0
2022-11-29 75897 118 433 0
2022-11-28 75779 156 433 0
2022-11-25 75623 55 433 0
2022-11-24 75568 88 433 0
2022-11-23 75480 102 433 0
2022-11-22 75378 79 433 0
2022-11-21 75299 114 433 0
2022-11-18 75185 57 433 0
2022-11-17 75128 70 433 0
2022-11-16 75058 111 433 0
2022-11-15 74947 91 433 0
2022-11-14 74856 128 433 0
2022-11-11 74728 69 433 0
2022-11-10 74659 190 433 0
2022-11-09 74469 197 433 0
2022-11-08 74272 131 433 0
2022-11-07 74141 224 433 0
2022-11-04 73917 233 433 0
2022-11-03 73684 186 433 0
2022-11-02 73498 183 433 0
2022-11-01 73315 164 433 0
2022-10-28 73151 205 433 2
2022-10-27 72946 213 431 0
2022-10-26 72733 225 431 2
2022-10-25 72508 187 429 0
2022-10-24 72321 325 429 2
2022-10-21 71996 121 427 0
2022-10-20 71875 225 427 0
2022-10-19 71650 252 427 1
2022-10-18 71398 210 426 0
2022-10-17 71188 428 426 0
2022-10-15 70760 1 426 0
2022-10-14 70759 256 426 0
2022-10-13 70503 167 426 0
2022-10-12 70336 154 426 2
2022-10-11 70182 250 424 0
2022-10-10 69932 190 424 0
2022-10-07 69742 325 424 0
2022-10-06 69417 288 424 0
2022-10-05 69129 285 424 0
2022-10-04 68844 152 424 1
2022-09-30 68692 242 423 1
2022-09-29 68450 196 422 0
2022-09-28 68254 257 422 0
2022-09-27 67997 148 422 0
2022-09-26 67849 192 422 0
2022-09-23 67657 124 422 0
2022-09-22 67533 133 422 0
2022-09-21 67400 119 422 0
2022-09-20 67281 125 422 0
2022-09-19 67156 174 422 0
2022-09-16 66982 119 422 0
2022-09-15 66863 148 422 0
2022-09-14 66715 118 422 0
2022-09-13 66597 89 422 2
2022-09-12 66508 182 420 0
2022-09-09 66326 91 420 0
2022-09-08 66235 100 420 0
2022-09-07 66135 120 420 0
2022-09-06 66015 122 420 0
2022-09-05 65893 150 420 0
2022-09-02 65743 79 420 0
2022-09-01 65664 72 420 0
2022-08-31 65592 97 420 0
2022-08-30 65495 181 420 0
2022-08-29 65314 73 420 0
2022-08-26 65241 67 420 0
2022-08-25 65174 97 420 0
2022-08-24 65077 288 420 1
2022-08-23 64789 89 419 0
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2022-08-19 64583 62 418 1
2022-08-18 64521 61 417 0
2022-08-17 64460 115 417 0
2022-08-16 64345 104 417 0
2022-08-15 64241 221 417 1
2022-08-13 64020 1 416 0
2022-08-12 64019 123 416 0
2022-08-11 63896 79 416 0
2022-08-10 63817 139 416 1
2022-08-09 63678 145 415 1
2022-08-08 63533 181 414 0
2022-08-05 63352 72 414 0
2022-08-04 63280 126 414 0
2022-08-03 63154 138 414 2
2022-08-02 63016 149 412 0
2022-08-01 62867 239 412 1
2022-07-29 62628 115 411 0
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2022-07-25 61999 343 410 0
2022-07-22 61656 154 410 2
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2022-07-06 59072 265 406 2
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2022-06-10 56052 89 403 0
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2022-05-09 54311 218 400 0
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2022-05-03 53781 261 400 1
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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: 0:03:36.742957