Germany: LK Odenwaldkreis (Hessen)¶

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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 12:36:25 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Odenwaldkreis", weeks=5);
2023-03-07T12:36:41.630847 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 125 125 150 150 175 175 200 200 225 225 7-day incidence rate (per 100K people) 130.0 LK Odenwaldkreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 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 Odenwaldkreis cases daily growth factor Germany-LK Odenwaldkreis cases daily growth factor (rolling mean) Germany-LK Odenwaldkreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 2.5 2.5 5.0 5.0 7.5 7.5 10.0 10.0 R & growth factor (based on deaths) Germany-LK Odenwaldkreis deaths daily growth factor Germany-LK Odenwaldkreis deaths daily growth factor (rolling mean) Germany-LK Odenwaldkreis 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 Odenwaldkreis doubling time cases (rolling mean) 0.00 19.39 38.78 58.17 daily change Germany-LK Odenwaldkreis new cases (rolling 7d mean) Germany-LK Odenwaldkreis new cases 0.000 0.485 0.970 1.454 1.939 daily change Germany-LK Odenwaldkreis new deaths (rolling 7d mean) Germany-LK Odenwaldkreis new deaths 0.000 0.325 0.650 0.975
In [4]:
overview(country="Germany", subregion="LK Odenwaldkreis");
2023-03-07T12:36:57.005315 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) 130.0 LK Odenwaldkreis, 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 0.0 2.5 5.0 7.5 10.0 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 Odenwaldkreis cases daily growth factor Germany-LK Odenwaldkreis cases daily growth factor (rolling mean) Germany-LK Odenwaldkreis 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 2.5 2.5 5.0 5.0 7.5 7.5 10.0 10.0 R & growth factor (based on deaths) Germany-LK Odenwaldkreis deaths daily growth factor Germany-LK Odenwaldkreis deaths daily growth factor (rolling mean) Germany-LK Odenwaldkreis 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 cases doubling time [days] Germany-LK Odenwaldkreis doubling time cases (rolling mean) Germany-LK Odenwaldkreis doubling time deaths (rolling mean) 0.0 97.0 193.9 290.9 387.8 daily change Germany-LK Odenwaldkreis new cases (rolling 7d mean) Germany-LK Odenwaldkreis new cases 0.00 2.42 4.85 7.27 9.70 daily change Germany-LK Odenwaldkreis new deaths (rolling 7d mean) Germany-LK Odenwaldkreis new deaths 0.0 55.3 110.6 165.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Odenwaldkreis", dates="2020-03-15:");
2023-03-07T12:39:28.482100 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 Odenwaldkreis LK Odenwaldkreis 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 Odenwaldkreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Odenwaldkreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Odenwaldkreis")
print(f'Population of country="Germany", subregion="LK Odenwaldkreis": {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 Odenwaldkreis": 96953 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 47407 15 258 0
2023-03-03 47392 8 258 0
2023-03-02 47384 13 258 0
2023-03-01 47371 38 258 0
2023-02-28 47333 52 258 0
2023-02-27 47281 63 258 0
2023-02-24 47218 19 258 0
2023-02-23 47199 42 258 0
2023-02-22 47157 47 258 0
2023-02-21 47110 39 258 0
2023-02-20 47071 34 258 0
2023-02-17 47037 21 258 2
2023-02-16 47016 33 256 0
2023-02-15 46983 34 256 1
2023-02-14 46949 46 255 0
2023-02-13 46903 29 255 0
2023-02-10 46874 21 255 0
2023-02-09 46853 13 255 0
2023-02-08 46840 31 255 0
2023-02-07 46809 56 255 0
2023-02-06 46753 31 255 0
2023-02-03 46722 19 255 0
2023-02-02 46703 26 255 0
2023-02-01 46677 33 255 0
2023-01-31 46644 45 255 0
2023-01-30 46599 48 255 1
2023-01-27 46551 21 254 0
2023-01-26 46530 14 254 0
2023-01-25 46516 13 254 0
2023-01-24 46503 55 254 0
2023-01-23 46448 29 254 1
2023-01-20 46419 15 253 0
2023-01-19 46404 19 253 0
2023-01-18 46385 20 253 0
2023-01-17 46365 23 253 0
2023-01-16 46342 19 253 0
2023-01-13 46323 17 253 0
2023-01-12 46306 16 253 0
2023-01-11 46290 23 253 0
2023-01-10 46267 49 253 0
2023-01-09 46218 25 253 0
2023-01-06 46193 27 253 0
2023-01-05 46166 40 253 0
2023-01-04 46126 27 253 0
2023-01-03 46099 52 253 0
2023-01-02 46047 33 253 0
2022-12-30 46014 16 253 0
2022-12-29 45998 37 253 0
2022-12-28 45961 65 253 1
2022-12-27 45896 77 252 0
2022-12-23 45819 40 252 0
2022-12-22 45779 54 252 0
2022-12-21 45725 59 252 0
2022-12-20 45666 88 252 0
2022-12-19 45578 54 252 0
2022-12-16 45524 56 252 0
2022-12-15 45468 56 252 0
2022-12-14 45412 35 252 0
2022-12-13 45377 60 252 0
2022-12-12 45317 38 252 0
2022-12-09 45279 30 252 0
2022-12-08 45249 30 252 0
2022-12-07 45219 44 252 0
2022-12-06 45175 32 252 1
2022-12-05 45143 49 251 0
2022-12-02 45094 20 251 0
2022-12-01 45074 24 251 0
2022-11-30 45050 30 251 0
2022-11-29 45020 31 251 0
2022-11-28 44989 35 251 0
2022-11-25 44954 24 251 0
2022-11-24 44930 22 251 0
2022-11-23 44908 31 251 0
2022-11-22 44877 46 251 1
2022-11-21 44831 51 250 0
2022-11-18 44780 33 250 0
2022-11-17 44747 30 250 0
2022-11-16 44717 38 250 0
2022-11-15 44679 48 250 0
2022-11-14 44631 46 250 0
2022-11-11 44585 48 250 1
2022-11-10 44537 42 249 0
2022-11-09 44495 45 249 0
2022-11-08 44450 77 249 0
2022-11-07 44373 83 249 1
2022-11-04 44290 35 248 0
2022-11-03 44255 28 248 0
2022-11-02 44227 50 248 0
2022-11-01 44177 148 248 0
2022-10-31 44029 90 248 0
2022-10-28 43939 59 248 0
2022-10-27 43880 92 248 0
2022-10-26 43788 84 248 0
2022-10-25 43704 157 248 1
2022-10-24 43547 175 247 1
2022-10-21 43372 76 246 0
2022-10-20 43296 112 246 1
2022-10-19 43184 175 245 0
2022-10-18 43009 226 245 0
2022-10-17 42783 176 245 0
2022-10-14 42607 105 245 0
2022-10-13 42502 149 245 0
2022-10-12 42353 241 245 1
2022-10-11 42112 232 244 0
2022-10-10 41880 384 244 1
2022-10-07 41496 118 243 2
2022-10-06 41378 173 241 0
2022-10-05 41205 230 241 0
2022-10-04 40975 297 241 1
2022-09-30 40678 94 240 0
2022-09-29 40584 110 240 1
2022-09-28 40474 160 239 0
2022-09-27 40314 128 239 0
2022-09-26 40186 181 239 0
2022-09-23 40005 56 239 0
2022-09-22 39949 61 239 0
2022-09-21 39888 98 239 0
2022-09-20 39790 123 239 0
2022-09-19 39667 155 239 0
2022-09-16 39512 35 239 0
2022-09-15 39477 67 239 0
2022-09-14 39410 73 239 0
2022-09-13 39337 72 239 0
2022-09-12 39265 86 239 0
2022-09-09 39179 47 239 0
2022-09-08 39132 56 239 0
2022-09-07 39076 44 239 0
2022-09-06 39032 68 239 0
2022-09-05 38964 57 239 0
2022-09-02 38907 31 239 0
2022-09-01 38876 35 239 0
2022-08-31 38841 44 239 0
2022-08-30 38797 58 239 0
2022-08-29 38739 75 239 1
2022-08-26 38664 22 238 0
2022-08-25 38642 37 238 0
2022-08-24 38605 48 238 0
2022-08-23 38557 76 238 0
2022-08-22 38481 58 238 0
2022-08-19 38423 43 238 0
2022-08-18 38380 40 238 0
2022-08-17 38340 118 238 0
2022-08-16 38222 58 238 0
2022-08-15 38164 89 238 0
2022-08-12 38075 47 238 0
2022-08-11 38028 80 238 1
2022-08-10 37948 72 237 0
2022-08-09 37876 160 237 0
2022-08-08 37716 156 237 0
2022-08-05 37560 173 237 0
2022-08-04 37387 127 237 1
2022-08-03 37260 173 236 0
2022-08-02 37087 138 236 0
2022-08-01 36949 184 236 1
2022-07-29 36765 202 235 0
2022-07-28 36563 230 235 0
2022-07-27 36333 175 235 0
2022-07-26 36158 220 235 1
2022-07-22 35938 128 234 1
2022-07-21 35810 159 233 0
2022-07-20 35651 258 233 0
2022-07-19 35393 257 233 0
2022-07-18 35136 271 233 0
2022-07-15 34865 115 233 0
2022-07-14 34750 192 233 0
2022-07-13 34558 297 233 0
2022-07-12 34261 191 233 0
2022-07-11 34070 165 233 1
2022-07-08 33905 117 232 0
2022-07-07 33788 120 232 0
2022-07-06 33668 236 232 0
2022-07-05 33432 150 232 1
2022-07-04 33282 172 231 3
2022-07-01 33110 194 228 1
2022-06-30 32916 140 227 0
2022-06-29 32776 237 227 0
2022-06-28 32539 109 227 0
2022-06-27 32430 163 227 0
2022-06-24 32267 168 227 0
2022-06-23 32099 86 227 0
2022-06-22 32013 289 227 0
2022-06-21 31724 216 227 0
2022-06-20 31508 237 227 0
2022-06-17 31271 200 227 0
2022-06-15 31071 137 227 1
2022-06-14 30934 184 226 0
2022-06-13 30750 157 226 0
2022-06-10 30593 100 226 0
2022-06-09 30493 116 226 1
2022-06-08 30377 131 225 0
2022-06-07 30246 117 225 0
2022-06-03 30129 50 225 0
2022-06-02 30079 37 225 0
2022-06-01 30042 40 225 0
2022-05-31 30002 68 225 0
2022-05-30 29934 50 225 0
2022-05-27 29884 37 225 0
2022-05-25 29847 60 225 0
2022-05-24 29787 53 225 0
2022-05-23 29734 51 225 0
2022-05-20 29683 42 225 0
2022-05-19 29641 62 225 0
2022-05-18 29579 57 225 0
2022-05-17 29522 149 225 0
2022-05-16 29373 73 225 0
2022-05-13 29300 87 225 0
2022-05-12 29213 68 225 0
2022-05-11 29145 87 225 0
2022-05-10 29058 131 225 0
2022-05-09 28927 148 225 1
2022-05-07 28779 20 224 0
2022-05-06 28759 63 224 0
2022-05-05 28696 92 224 0
2022-05-04 28604 113 224 0
2022-05-03 28491 201 224 1
2022-05-02 28290 111 223 0
2022-04-30 28179 55 223 0
2022-04-29 28124 53 223 0
2022-04-28 28071 128 223 0
2022-04-27 27943 118 223 1
2022-04-26 27825 266 222 0
2022-04-25 27559 119 222 0
2022-04-23 27440 38 222 0
2022-04-22 27402 150 222 0
2022-04-21 27252 183 222 0
2022-04-20 27069 386 222 0
2022-04-19 26683 401 222 0
2022-04-16 26282 40 222 0
2022-04-14 26242 179 222 0
2022-04-13 26063 394 222 0
2022-04-12 25669 400 222 1
2022-04-11 25269 300 221 0
2022-04-10 24969 5 221 0
2022-04-09 24964 50 221 0
2022-04-08 24914 200 221 0
2022-04-07 24714 252 221 0
2022-04-06 24462 247 221 0
2022-04-05 24215 242 221 0
2022-04-04 23973 156 221 0
2022-04-03 23817 4 221 0
2022-04-02 23813 50 221 0
2022-04-01 23763 200 221 0
2022-03-31 23563 210 221 0
2022-03-30 23353 266 221 0
2022-03-29 23087 294 221 2
2022-03-28 22793 404 219 0
2022-03-27 22389 6 219 0
2022-03-26 22383 35 219 1
2022-03-25 22348 270 218 0
2022-03-24 22078 300 218 0
2022-03-23 21778 311 218 0
2022-03-22 21467 384 218 0
2022-03-21 21083 443 218 0
2022-03-20 20640 4 218 0
2022-03-19 20636 51 218 0
2022-03-18 20585 283 218 0
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2022-03-10 18712 265 216 0
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2022-03-06 17609 2 216 0
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2022-03-01 16972 219 216 2
2022-02-28 16753 195 214 0
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2022-02-25 16526 130 214 0
2022-02-24 16396 171 214 0
2022-02-23 16225 178 214 0
2022-02-22 16047 220 214 0
2022-02-21 15827 268 214 0
2022-02-20 15559 6 214 0
2022-02-19 15553 49 214 0
2022-02-18 15504 187 214 0
2022-02-17 15317 235 214 1
2022-02-16 15082 246 213 1
2022-02-15 14836 400 212 0
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2022-02-13 14036 5 212 0
2022-02-12 14031 128 212 1
2022-02-11 13903 288 211 1
2022-02-10 13615 318 210 0
2022-02-09 13297 306 210 1
2022-02-08 12991 267 209 0
2022-02-07 12724 279 209 2
2022-02-06 12445 3 207 0
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2022-02-02 11728 289 206 1
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2022-01-31 11127 231 205 1
2022-01-29 10896 223 204 0
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2022-01-27 10443 240 204 0
2022-01-26 10203 269 204 0
2022-01-25 9934 259 204 0
2022-01-24 9675 130 204 1
2022-01-23 9545 31 203 0
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2022-01-18 8987 162 203 0
2022-01-17 8825 35 203 0
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2022-01-15 8772 73 203 0
2022-01-14 8699 82 203 0
2022-01-13 8617 100 203 0
2022-01-12 8517 33 203 0
2022-01-11 8484 125 203 0
2022-01-10 8359 29 203 0
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2021-12-20 7771 4 202 0
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2021-12-18 7760 20 202 1
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2021-07-07 4449 2 172 0
2021-07-06 4447 1 172 0
2021-07-02 4446 1 172 0
2021-06-29 4445 2 172 0
2021-06-25 4443 2 172 0
2021-06-24 4441 1 172 0
2021-06-23 4440 2 172 0
2021-06-21 4438 1 172 0
2021-06-18 4437 2 172 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: 0:03:33.777618