Germany: LK Dillingen a.d.Donau (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 10:40:51 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Dillingen a.d.Donau", weeks=5);
2023-03-07T10:41:04.975856 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 7-day incidence rate (per 100K people) 72.5 LK Dillingen a.d.Donau, 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 0.00 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Dillingen a.d.Donau cases daily growth factor Germany-LK Dillingen a.d.Donau cases daily growth factor (rolling mean) Germany-LK Dillingen a.d.Donau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Dillingen a.d.Donau deaths daily growth factor Germany-LK Dillingen a.d.Donau deaths daily growth factor (rolling mean) Germany-LK Dillingen a.d.Donau 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 Dillingen a.d.Donau doubling time cases (rolling mean) 0.00 19.60 39.19 58.79 daily change Germany-LK Dillingen a.d.Donau new cases (rolling 7d mean) Germany-LK Dillingen a.d.Donau new cases 0.000 0.245 0.490 0.735 0.980 daily change Germany-LK Dillingen a.d.Donau new deaths (rolling 7d mean) Germany-LK Dillingen a.d.Donau new deaths 0.00 0.33 0.66 0.99
In [4]:
overview(country="Germany", subregion="LK Dillingen a.d.Donau");
2023-03-07T10:41:23.436186 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) 72.5 LK Dillingen a.d.Donau, 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 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 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Dillingen a.d.Donau cases daily growth factor Germany-LK Dillingen a.d.Donau cases daily growth factor (rolling mean) Germany-LK Dillingen a.d.Donau estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Dillingen a.d.Donau deaths daily growth factor Germany-LK Dillingen a.d.Donau deaths daily growth factor (rolling mean) Germany-LK Dillingen a.d.Donau 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 Dillingen a.d.Donau doubling time cases (rolling mean) Germany-LK Dillingen a.d.Donau doubling time deaths (rolling mean) 0 196 392 daily change Germany-LK Dillingen a.d.Donau new cases (rolling 7d mean) Germany-LK Dillingen a.d.Donau new cases 0.000 1.960 3.919 5.879 7.839 daily change Germany-LK Dillingen a.d.Donau new deaths (rolling 7d mean) Germany-LK Dillingen a.d.Donau new deaths 0.0 94.1 188.2 282.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Dillingen a.d.Donau", dates="2020-03-15:");
2023-03-07T10:43:59.614680 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 Dillingen a.d.Donau LK Dillingen a.d.Donau 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 Dillingen a.d.Donau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Dillingen a.d.Donau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Dillingen a.d.Donau")
print(f'Population of country="Germany", subregion="LK Dillingen a.d.Donau": {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 Dillingen a.d.Donau": 97985 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 54450 10 260 0
2023-03-05 54440 2 260 0
2023-03-04 54438 1 260 0
2023-03-03 54437 7 260 0
2023-03-02 54430 9 260 0
2023-03-01 54421 19 260 0
2023-02-28 54402 23 260 0
2023-02-27 54379 27 260 1
2023-02-26 54352 5 259 0
2023-02-25 54347 10 259 0
2023-02-24 54337 34 259 0
2023-02-23 54303 39 259 0
2023-02-22 54264 31 259 0
2023-02-21 54233 38 259 0
2023-02-20 54195 12 259 0
2023-02-19 54183 8 259 0
2023-02-18 54175 10 259 0
2023-02-17 54165 27 259 0
2023-02-16 54138 28 259 0
2023-02-15 54110 34 259 0
2023-02-14 54076 62 259 0
2023-02-13 54014 18 259 0
2023-02-12 53996 7 259 0
2023-02-11 53989 9 259 0
2023-02-10 53980 26 259 0
2023-02-09 53954 30 259 0
2023-02-08 53924 25 259 0
2023-02-07 53899 25 259 0
2023-02-06 53874 32 259 0
2023-02-05 53842 7 259 0
2023-02-04 53835 10 259 0
2023-02-03 53825 14 259 0
2023-02-02 53811 27 259 0
2023-02-01 53784 18 259 0
2023-01-31 53766 50 259 0
2023-01-30 53716 26 259 0
2023-01-29 53690 2 259 0
2023-01-28 53688 9 259 0
2023-01-27 53679 11 259 0
2023-01-26 53668 17 259 0
2023-01-25 53651 24 259 0
2023-01-24 53627 30 259 0
2023-01-23 53597 24 259 0
2023-01-22 53573 2 259 0
2023-01-21 53571 7 259 0
2023-01-20 53564 9 259 0
2023-01-19 53555 10 259 0
2023-01-18 53545 11 259 0
2023-01-17 53534 21 259 1
2023-01-16 53513 18 258 1
2023-01-15 53495 1 257 0
2023-01-14 53494 4 257 0
2023-01-13 53490 21 257 0
2023-01-12 53469 17 257 0
2023-01-11 53452 17 257 0
2023-01-10 53435 34 257 0
2023-01-09 53401 21 257 0
2023-01-08 53380 2 257 0
2023-01-07 53378 2 257 1
2023-01-06 53376 10 256 1
2023-01-05 53366 9 255 0
2023-01-04 53357 14 255 0
2023-01-03 53343 29 255 1
2023-01-02 53314 35 254 0
2023-01-01 53279 5 254 0
2022-12-31 53274 6 254 0
2022-12-30 53268 30 254 0
2022-12-29 53238 28 254 0
2022-12-28 53210 29 254 0
2022-12-27 53181 40 254 1
2022-12-26 53141 4 253 0
2022-12-25 53137 5 253 0
2022-12-24 53132 8 253 0
2022-12-23 53124 18 253 0
2022-12-22 53106 31 253 0
2022-12-21 53075 32 253 0
2022-12-20 53043 39 253 0
2022-12-19 53004 33 253 0
2022-12-18 52971 7 253 0
2022-12-17 52964 5 253 0
2022-12-16 52959 21 253 0
2022-12-15 52938 21 253 0
2022-12-14 52917 37 253 0
2022-12-13 52880 32 253 0
2022-12-12 52848 35 253 0
2022-12-11 52813 1 253 0
2022-12-10 52812 3 253 0
2022-12-09 52809 22 253 1
2022-12-08 52787 17 252 0
2022-12-07 52770 28 252 0
2022-12-06 52742 51 252 1
2022-12-05 52691 18 251 0
2022-12-04 52673 1 251 0
2022-12-03 52672 4 251 0
2022-12-02 52668 22 251 0
2022-12-01 52646 23 251 0
2022-11-30 52623 21 251 1
2022-11-29 52602 33 250 0
2022-11-28 52569 18 250 0
2022-11-27 52551 2 250 0
2022-11-26 52549 7 250 0
2022-11-25 52542 18 250 0
2022-11-24 52524 18 250 0
2022-11-23 52506 25 250 0
2022-11-22 52481 44 250 0
2022-11-21 52437 35 250 0
2022-11-20 52402 2 250 0
2022-11-19 52400 10 250 0
2022-11-18 52390 26 250 0
2022-11-17 52364 19 250 0
2022-11-16 52345 21 250 0
2022-11-15 52324 41 250 1
2022-11-14 52283 50 249 0
2022-11-13 52233 4 249 0
2022-11-12 52229 12 249 0
2022-11-11 52217 20 249 0
2022-11-10 52197 32 249 0
2022-11-09 52165 38 249 0
2022-11-08 52127 57 249 0
2022-11-07 52070 91 249 2
2022-11-06 51979 6 247 0
2022-11-05 51973 10 247 0
2022-11-04 51963 40 247 0
2022-11-03 51923 65 247 0
2022-11-02 51858 57 247 0
2022-11-01 51801 45 247 0
2022-10-31 51756 99 247 0
2022-10-30 51657 2 247 0
2022-10-29 51655 19 247 0
2022-10-28 51636 64 247 1
2022-10-27 51572 74 246 0
2022-10-26 51498 113 246 1
2022-10-25 51385 148 245 0
2022-10-24 51237 106 245 0
2022-10-23 51131 8 245 0
2022-10-22 51123 37 245 0
2022-10-21 51086 93 245 0
2022-10-20 50993 130 245 0
2022-10-19 50863 122 245 0
2022-10-18 50741 232 245 1
2022-10-17 50509 338 244 3
2022-10-14 50171 85 241 0
2022-10-13 50086 202 241 0
2022-10-12 49884 319 241 2
2022-10-11 49565 277 239 2
2022-10-10 49288 366 237 1
2022-10-07 48922 189 236 0
2022-10-06 48733 283 236 0
2022-10-05 48450 325 236 1
2022-10-04 48125 364 235 1
2022-09-30 47761 134 234 0
2022-09-29 47627 144 234 1
2022-09-28 47483 167 233 1
2022-09-27 47316 195 232 0
2022-09-26 47121 204 232 0
2022-09-23 46917 45 232 0
2022-09-22 46872 85 232 1
2022-09-21 46787 81 231 0
2022-09-20 46706 80 231 0
2022-09-19 46626 102 231 1
2022-09-16 46524 35 230 0
2022-09-15 46489 42 230 1
2022-09-14 46447 45 229 0
2022-09-13 46402 76 229 1
2022-09-12 46326 71 228 0
2022-09-09 46255 39 228 0
2022-09-08 46216 40 228 0
2022-09-07 46176 58 228 1
2022-09-06 46118 57 227 0
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2022-08-31 45950 27 227 0
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2022-08-29 45864 73 227 1
2022-08-26 45791 23 226 0
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2022-08-23 45686 62 226 0
2022-08-22 45624 79 226 0
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2022-08-11 45301 52 224 0
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2022-08-04 44982 61 224 0
2022-08-03 44921 77 224 1
2022-08-02 44844 123 223 1
2022-08-01 44721 196 222 1
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2022-07-25 43922 302 221 1
2022-07-22 43620 119 220 0
2022-07-21 43501 171 220 0
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2022-07-15 42546 176 217 0
2022-07-14 42370 183 217 0
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2022-07-08 41491 68 217 1
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2022-07-01 40707 69 216 1
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2022-06-28 40383 110 215 0
2022-06-27 40273 137 215 0
2022-06-24 40136 48 215 0
2022-06-23 40088 106 215 0
2022-06-22 39982 92 215 0
2022-06-21 39890 103 215 0
2022-06-20 39787 144 215 1
2022-06-17 39643 93 214 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: 0:03:21.612870