Germany: LK Aschaffenburg (Bayern)¶

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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 10:33:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Aschaffenburg", weeks=5);
2023-03-07T10:33:41.693103 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 7-day incidence rate (per 100K people) 119.5 LK Aschaffenburg, 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 0.00 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-LK Aschaffenburg cases daily growth factor Germany-LK Aschaffenburg cases daily growth factor (rolling mean) Germany-LK Aschaffenburg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Aschaffenburg deaths daily growth factor Germany-LK Aschaffenburg deaths daily growth factor (rolling mean) Germany-LK Aschaffenburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Aschaffenburg doubling time cases (rolling mean) 0.00 17.50 34.99 52.49 69.99 daily change Germany-LK Aschaffenburg new cases (rolling 7d mean) Germany-LK Aschaffenburg new cases 0.000 0.437 0.875 1.312 1.750 daily change Germany-LK Aschaffenburg new deaths (rolling 7d mean) Germany-LK Aschaffenburg new deaths 0.000 0.312 0.624 0.935
In [4]:
overview(country="Germany", subregion="LK Aschaffenburg");
2023-03-07T10:33:59.426085 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) 119.5 LK Aschaffenburg, 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 R & growth factor (based on cases) Germany-LK Aschaffenburg cases daily growth factor Germany-LK Aschaffenburg cases daily growth factor (rolling mean) Germany-LK Aschaffenburg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Aschaffenburg deaths daily growth factor Germany-LK Aschaffenburg deaths daily growth factor (rolling mean) Germany-LK Aschaffenburg 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 Aschaffenburg doubling time cases (rolling mean) Germany-LK Aschaffenburg doubling time deaths (rolling mean) 0.0 175.0 349.9 524.9 699.9 daily change Germany-LK Aschaffenburg new cases (rolling 7d mean) Germany-LK Aschaffenburg new cases 0.0 3.5 7.0 10.5 daily change Germany-LK Aschaffenburg new deaths (rolling 7d mean) Germany-LK Aschaffenburg new deaths 0.0 89.9 179.8 269.8 359.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Aschaffenburg", dates="2020-03-15:");
2023-03-07T10:36:54.444625 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 Aschaffenburg LK Aschaffenburg 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 Aschaffenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Aschaffenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Aschaffenburg")
print(f'Population of country="Germany", subregion="LK Aschaffenburg": {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 Aschaffenburg": 174965 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 86787 24 376 0
2023-03-03 86763 16 376 0
2023-03-02 86747 36 376 0
2023-03-01 86711 78 376 0
2023-02-28 86633 55 376 0
2023-02-27 86578 73 376 1
2023-02-24 86505 61 375 0
2023-02-23 86444 40 375 0
2023-02-22 86404 49 375 0
2023-02-21 86355 45 375 0
2023-02-20 86310 61 375 0
2023-02-19 86249 1 375 1
2023-02-17 86248 34 374 0
2023-02-16 86214 45 374 0
2023-02-15 86169 45 374 0
2023-02-14 86124 40 374 0
2023-02-13 86084 66 374 0
2023-02-10 86018 40 374 2
2023-02-09 85978 39 372 0
2023-02-08 85939 68 372 0
2023-02-07 85871 56 372 0
2023-02-06 85815 72 372 0
2023-02-03 85743 41 372 1
2023-02-02 85702 32 371 0
2023-02-01 85670 60 371 1
2023-01-31 85610 39 370 0
2023-01-30 85571 43 370 0
2023-01-27 85528 26 370 0
2023-01-26 85502 31 370 0
2023-01-25 85471 46 370 0
2023-01-24 85425 37 370 0
2023-01-23 85388 31 370 0
2023-01-20 85357 23 370 1
2023-01-19 85334 26 369 0
2023-01-18 85308 34 369 0
2023-01-17 85274 28 369 0
2023-01-16 85246 32 369 0
2023-01-13 85214 19 369 1
2023-01-12 85195 42 368 0
2023-01-11 85153 41 368 0
2023-01-10 85112 63 368 0
2023-01-09 85049 67 368 2
2023-01-05 84982 36 366 0
2023-01-04 84946 50 366 0
2023-01-03 84896 43 366 2
2023-01-02 84853 75 364 0
2022-12-30 84778 50 364 1
2022-12-29 84728 68 363 0
2022-12-28 84660 56 363 2
2022-12-27 84604 120 361 0
2022-12-23 84484 63 361 0
2022-12-22 84421 61 361 1
2022-12-21 84360 74 360 2
2022-12-20 84286 75 358 0
2022-12-19 84211 90 358 3
2022-12-16 84121 55 355 0
2022-12-15 84066 55 355 0
2022-12-14 84011 88 355 1
2022-12-13 83923 52 354 0
2022-12-12 83871 120 354 0
2022-12-09 83751 40 354 0
2022-12-08 83711 67 354 1
2022-12-07 83644 75 353 0
2022-12-06 83569 46 353 0
2022-12-05 83523 62 353 2
2022-12-02 83461 26 351 0
2022-12-01 83435 45 351 1
2022-11-30 83390 47 350 0
2022-11-29 83343 37 350 1
2022-11-28 83306 56 349 0
2022-11-25 83250 34 349 0
2022-11-24 83216 42 349 0
2022-11-23 83174 66 349 1
2022-11-22 83108 38 348 0
2022-11-21 83070 76 348 2
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2022-11-17 82957 45 346 0
2022-11-16 82912 61 346 0
2022-11-15 82851 55 346 0
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2022-11-08 82512 64 346 0
2022-11-07 82448 147 346 0
2022-11-04 82301 104 346 0
2022-11-03 82197 96 346 1
2022-11-02 82101 191 345 0
2022-10-31 81910 201 345 2
2022-10-28 81709 105 343 0
2022-10-27 81604 132 343 0
2022-10-26 81472 225 343 0
2022-10-25 81247 196 343 0
2022-10-24 81051 286 343 2
2022-10-22 80765 1 341 0
2022-10-21 80764 174 341 0
2022-10-20 80590 274 341 0
2022-10-19 80316 372 341 1
2022-10-18 79944 253 340 1
2022-10-17 79691 405 339 2
2022-10-16 79286 1 337 0
2022-10-14 79285 227 337 0
2022-10-13 79058 283 337 0
2022-10-12 78775 461 337 0
2022-10-11 78314 328 337 0
2022-10-10 77986 525 337 1
2022-10-07 77461 270 336 2
2022-10-06 77191 554 334 0
2022-10-05 76637 223 334 0
2022-10-04 76414 421 334 2
2022-10-03 75993 1 332 0
2022-10-02 75992 4 332 0
2022-09-30 75988 177 332 0
2022-09-29 75811 210 332 1
2022-09-28 75601 328 331 0
2022-09-27 75273 178 331 0
2022-09-26 75095 204 331 0
2022-09-25 74891 1 331 0
2022-09-23 74890 107 331 0
2022-09-22 74783 148 331 0
2022-09-21 74635 173 331 0
2022-09-20 74462 108 331 0
2022-09-19 74354 170 331 0
2022-09-17 74184 2 331 0
2022-09-16 74182 53 331 2
2022-09-15 74129 112 329 0
2022-09-14 74017 138 329 0
2022-09-13 73879 112 329 0
2022-09-12 73767 146 329 2
2022-09-10 73621 1 327 0
2022-09-09 73620 75 327 0
2022-09-08 73545 87 327 0
2022-09-07 73458 106 327 1
2022-09-06 73352 84 326 1
2022-09-05 73268 136 325 0
2022-09-02 73132 63 325 0
2022-09-01 73069 84 325 0
2022-08-31 72985 125 325 0
2022-08-30 72860 104 325 0
2022-08-29 72756 143 325 1
2022-08-28 72613 1 324 0
2022-08-26 72612 68 324 0
2022-08-25 72544 92 324 0
2022-08-24 72452 122 324 0
2022-08-23 72330 120 324 0
2022-08-22 72210 175 324 0
2022-08-21 72035 1 324 0
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2022-08-17 71739 96 324 2
2022-08-16 71643 247 322 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: 0:03:40.501794