Germany: LK Freyung-Grafenau (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 10:47:49 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Freyung-Grafenau", weeks=5);
2023-03-07T10:48:00.909467 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 96.7 LK Freyung-Grafenau, 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 0.5 1.0 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 Freyung-Grafenau cases daily growth factor Germany-LK Freyung-Grafenau cases daily growth factor (rolling mean) Germany-LK Freyung-Grafenau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Freyung-Grafenau deaths daily growth factor Germany-LK Freyung-Grafenau deaths daily growth factor (rolling mean) Germany-LK Freyung-Grafenau estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Freyung-Grafenau doubling time cases (rolling mean) 0.00 7.86 15.73 23.59 31.45 daily change Germany-LK Freyung-Grafenau new cases (rolling 7d mean) Germany-LK Freyung-Grafenau new cases 0.000 0.393 0.786 daily change Germany-LK Freyung-Grafenau new deaths (rolling 7d mean) Germany-LK Freyung-Grafenau new deaths 0.000 0.223 0.445 0.668 0.891
In [4]:
overview(country="Germany", subregion="LK Freyung-Grafenau");
2023-03-07T10:48:20.426456 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 3000 3000 7-day incidence rate (per 100K people) 96.7 LK Freyung-Grafenau, 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 Freyung-Grafenau cases daily growth factor Germany-LK Freyung-Grafenau cases daily growth factor (rolling mean) Germany-LK Freyung-Grafenau estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Freyung-Grafenau deaths daily growth factor Germany-LK Freyung-Grafenau deaths daily growth factor (rolling mean) Germany-LK Freyung-Grafenau 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 Freyung-Grafenau doubling time cases (rolling mean) Germany-LK Freyung-Grafenau doubling time deaths (rolling mean) 0.0 157.3 314.5 471.8 daily change Germany-LK Freyung-Grafenau new cases (rolling 7d mean) Germany-LK Freyung-Grafenau new cases 0.000 1.573 3.145 4.718 daily change Germany-LK Freyung-Grafenau new deaths (rolling 7d mean) Germany-LK Freyung-Grafenau new deaths 0.0 53.2 106.4 159.6 212.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Freyung-Grafenau", dates="2020-03-15:");
2023-03-07T10:50:37.658168 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 Freyung-Grafenau LK Freyung-Grafenau 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 Freyung-Grafenau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Freyung-Grafenau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Freyung-Grafenau")
print(f'Population of country="Germany", subregion="LK Freyung-Grafenau": {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 Freyung-Grafenau": 78632 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 43145 17 248 0
2023-03-03 43128 10 248 0
2023-03-02 43118 13 248 0
2023-03-01 43105 13 248 0
2023-02-28 43092 23 248 0
2023-02-27 43069 18 248 0
2023-02-26 43051 1 248 0
2023-02-24 43050 20 248 0
2023-02-23 43030 13 248 0
2023-02-22 43017 36 248 0
2023-02-20 42981 28 248 0
2023-02-17 42953 12 248 0
2023-02-16 42941 16 248 0
2023-02-15 42925 24 248 0
2023-02-14 42901 33 248 0
2023-02-13 42868 22 248 1
2023-02-10 42846 12 247 0
2023-02-09 42834 17 247 0
2023-02-08 42817 29 247 0
2023-02-07 42788 28 247 0
2023-02-06 42760 31 247 0
2023-02-03 42729 19 247 0
2023-02-02 42710 13 247 0
2023-02-01 42697 18 247 0
2023-01-31 42679 21 247 0
2023-01-30 42658 31 247 1
2023-01-27 42627 17 246 0
2023-01-26 42610 16 246 0
2023-01-25 42594 24 246 0
2023-01-24 42570 13 246 0
2023-01-23 42557 15 246 0
2023-01-20 42542 12 246 0
2023-01-19 42530 6 246 0
2023-01-18 42524 10 246 0
2023-01-17 42514 14 246 0
2023-01-16 42500 13 246 0
2023-01-13 42487 12 246 0
2023-01-12 42475 9 246 0
2023-01-11 42466 18 246 0
2023-01-10 42448 16 246 0
2023-01-09 42432 15 246 0
2023-01-05 42417 14 246 0
2023-01-04 42403 18 246 0
2023-01-03 42385 13 246 0
2023-01-02 42372 6 246 0
2023-01-01 42366 1 246 0
2022-12-31 42365 1 246 1
2022-12-30 42364 11 245 1
2022-12-29 42353 8 244 0
2022-12-28 42345 6 244 0
2022-12-27 42339 17 244 0
2022-12-23 42322 16 244 0
2022-12-22 42306 18 244 0
2022-12-21 42288 18 244 0
2022-12-20 42270 18 244 0
2022-12-19 42252 24 244 1
2022-12-16 42228 16 243 0
2022-12-15 42212 10 243 0
2022-12-14 42202 14 243 0
2022-12-13 42188 23 243 0
2022-12-12 42165 19 243 0
2022-12-09 42146 11 243 0
2022-12-08 42135 11 243 1
2022-12-07 42124 16 242 0
2022-12-06 42108 17 242 0
2022-12-05 42091 14 242 0
2022-12-02 42077 12 242 1
2022-12-01 42065 11 241 0
2022-11-30 42054 10 241 0
2022-11-29 42044 20 241 1
2022-11-28 42024 21 240 1
2022-11-25 42003 7 239 0
2022-11-24 41996 12 239 0
2022-11-23 41984 11 239 0
2022-11-22 41973 12 239 0
2022-11-21 41961 6 239 0
2022-11-19 41955 8 239 0
2022-11-18 41947 6 239 0
2022-11-17 41941 13 239 0
2022-11-16 41928 8 239 0
2022-11-15 41920 10 239 0
2022-11-14 41910 15 239 0
2022-11-12 41895 7 239 0
2022-11-11 41888 14 239 0
2022-11-10 41874 9 239 0
2022-11-09 41865 26 239 0
2022-11-08 41839 22 239 0
2022-11-07 41817 22 239 0
2022-11-05 41795 29 239 0
2022-11-04 41766 27 239 0
2022-11-03 41739 21 239 0
2022-11-02 41718 46 239 0
2022-10-31 41672 42 239 1
2022-10-29 41630 19 238 0
2022-10-28 41611 59 238 0
2022-10-27 41552 43 238 0
2022-10-26 41509 56 238 0
2022-10-25 41453 78 238 0
2022-10-24 41375 80 238 0
2022-10-22 41295 3 238 0
2022-10-21 41292 71 238 0
2022-10-20 41221 71 238 0
2022-10-19 41150 91 238 0
2022-10-18 41059 107 238 0
2022-10-17 40952 96 238 0
2022-10-15 40856 57 238 0
2022-10-14 40799 73 238 0
2022-10-13 40726 102 238 1
2022-10-12 40624 147 237 0
2022-10-11 40477 115 237 0
2022-10-10 40362 143 237 0
2022-10-09 40219 1 237 0
2022-10-08 40218 47 237 0
2022-10-07 40171 127 237 0
2022-10-06 40044 88 237 0
2022-10-05 39956 145 237 0
2022-10-04 39811 125 237 0
2022-10-01 39686 57 237 0
2022-09-30 39629 82 237 0
2022-09-29 39547 85 237 0
2022-09-28 39462 76 237 1
2022-09-27 39386 90 236 0
2022-09-26 39296 79 236 1
2022-09-24 39217 42 235 0
2022-09-23 39175 30 235 0
2022-09-22 39145 40 235 0
2022-09-21 39105 28 235 0
2022-09-20 39077 57 235 0
2022-09-19 39020 54 235 1
2022-09-17 38966 12 234 0
2022-09-16 38954 27 234 0
2022-09-15 38927 35 234 0
2022-09-14 38892 40 234 0
2022-09-13 38852 42 234 0
2022-09-12 38810 29 234 0
2022-09-10 38781 18 234 0
2022-09-09 38763 18 234 1
2022-09-08 38745 23 233 0
2022-09-07 38722 17 233 0
2022-09-06 38705 31 233 0
2022-09-05 38674 15 233 0
2022-09-03 38659 12 233 0
2022-09-02 38647 18 233 0
2022-09-01 38629 16 233 0
2022-08-31 38613 20 233 0
2022-08-30 38593 20 233 0
2022-08-29 38573 26 233 0
2022-08-27 38547 22 233 0
2022-08-26 38525 19 233 0
2022-08-25 38506 27 233 0
2022-08-24 38479 15 233 0
2022-08-23 38464 22 233 0
2022-08-22 38442 15 233 0
2022-08-20 38427 11 233 0
2022-08-19 38416 11 233 0
2022-08-18 38405 14 233 0
2022-08-17 38391 24 233 0
2022-08-16 38367 29 233 0
2022-08-13 38338 9 233 0
2022-08-12 38329 23 233 0
2022-08-11 38306 36 233 0
2022-08-10 38270 28 233 0
2022-08-09 38242 38 233 0
2022-08-08 38204 51 233 1
2022-08-06 38153 21 232 0
2022-08-05 38132 38 232 0
2022-08-04 38094 43 232 0
2022-08-03 38051 56 232 0
2022-08-02 37995 50 232 0
2022-08-01 37945 63 232 0
2022-07-31 37882 1 232 0
2022-07-30 37881 43 232 0
2022-07-29 37838 55 232 0
2022-07-28 37783 66 232 0
2022-07-27 37717 92 232 0
2022-07-26 37625 126 232 1
2022-07-25 37499 124 231 0
2022-07-23 37375 73 231 1
2022-07-22 37302 158 230 1
2022-07-21 37144 112 229 1
2022-07-20 37032 193 228 0
2022-07-19 36839 199 228 0
2022-07-18 36640 259 228 1
2022-07-17 36381 5 227 0
2022-07-16 36376 106 227 0
2022-07-15 36270 252 227 0
2022-07-14 36018 218 227 0
2022-07-13 35800 320 227 0
2022-07-12 35480 167 227 1
2022-07-11 35313 321 226 1
2022-07-09 34992 109 225 0
2022-07-08 34883 183 225 0
2022-07-07 34700 74 225 1
2022-07-06 34626 126 224 0
2022-07-05 34500 77 224 0
2022-07-04 34423 82 224 0
2022-07-02 34341 59 224 0
2022-07-01 34282 36 224 0
2022-06-30 34246 71 224 0
2022-06-29 34175 64 224 0
2022-06-28 34111 61 224 0
2022-06-27 34050 56 224 0
2022-06-25 33994 44 224 1
2022-06-24 33950 39 223 0
2022-06-23 33911 30 223 0
2022-06-22 33881 32 223 0
2022-06-21 33849 55 223 0
2022-06-20 33794 67 223 0
2022-06-19 33727 1 223 0
2022-06-18 33726 44 223 0
2022-06-17 33682 23 223 0
2022-06-16 33659 26 223 0
2022-06-15 33633 49 223 0
2022-06-14 33584 49 223 0
2022-06-13 33535 57 223 0
2022-06-12 33478 1 223 0
2022-06-11 33477 20 223 0
2022-06-10 33457 18 223 0
2022-06-09 33439 37 223 0
2022-06-08 33402 45 223 0
2022-06-07 33357 47 223 0
2022-06-05 33310 20 223 0
2022-06-04 33290 16 223 0
2022-06-03 33274 46 223 1
2022-06-02 33228 29 222 0
2022-06-01 33199 53 222 0
2022-05-31 33146 39 222 0
2022-05-30 33107 24 222 0
2022-05-29 33083 8 222 0
2022-05-28 33075 17 222 0
2022-05-27 33058 45 222 0
2022-05-25 33013 17 222 0
2022-05-24 32996 35 222 0
2022-05-23 32961 24 222 0
2022-05-21 32937 26 222 0
2022-05-20 32911 35 222 0
2022-05-19 32876 21 222 0
2022-05-18 32855 43 222 0
2022-05-17 32812 70 222 0
2022-05-16 32742 23 222 0
2022-05-15 32719 6 222 0
2022-05-14 32713 47 222 0
2022-05-13 32666 34 222 0
2022-05-12 32632 71 222 0
2022-05-11 32561 50 222 0
2022-05-10 32511 59 222 0
2022-05-09 32452 27 222 0
2022-05-08 32425 13 222 0
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2022-05-06 32373 49 222 1
2022-05-05 32324 69 221 0
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2022-05-03 32196 105 221 0
2022-05-02 32091 41 221 0
2022-05-01 32050 13 221 0
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2022-02-25 19738 423 198 1
2022-02-24 19315 509 197 1
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2022-02-20 17757 281 195 0
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2021-09-11 5042 8 130 1
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2021-09-07 5017 6 129 0
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2021-09-05 5001 1 129 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: 0:03:16.622525