Germany: LK Dingolfing-Landau (Bayern)¶

  • Homepage of project: https://oscovida.github.io
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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 10:40:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Dingolfing-Landau", weeks=5);
2023-03-07T10:41:00.016745 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 41.8 LK Dingolfing-Landau, 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 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 Dingolfing-Landau cases daily growth factor Germany-LK Dingolfing-Landau cases daily growth factor (rolling mean) Germany-LK Dingolfing-Landau estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 R & growth factor (based on deaths) Germany-LK Dingolfing-Landau deaths daily growth factor Germany-LK Dingolfing-Landau deaths daily growth factor (rolling mean) Germany-LK Dingolfing-Landau estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Dingolfing-Landau doubling time cases (rolling mean) 0.00 9.80 19.61 daily change Germany-LK Dingolfing-Landau new cases (rolling 7d mean) Germany-LK Dingolfing-Landau new cases 0.000 0.245 0.490 0.735 0.980 daily change Germany-LK Dingolfing-Landau new deaths (rolling 7d mean) Germany-LK Dingolfing-Landau new deaths 0.000 0.205 0.410 0.616 0.821
In [4]:
overview(country="Germany", subregion="LK Dingolfing-Landau");
2023-03-07T10:41:18.331041 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 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) 41.8 LK Dingolfing-Landau, Germany, last data point from 2023-03-06 Jan 20 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 Jan 20 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 Jan 20 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 Dingolfing-Landau cases daily growth factor Germany-LK Dingolfing-Landau cases daily growth factor (rolling mean) Germany-LK Dingolfing-Landau estimated R (using cases) Jan 20 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 R & growth factor (based on deaths) Germany-LK Dingolfing-Landau deaths daily growth factor Germany-LK Dingolfing-Landau deaths daily growth factor (rolling mean) Germany-LK Dingolfing-Landau estimated R (using deaths) Jan 20 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 Dingolfing-Landau doubling time cases (rolling mean) Germany-LK Dingolfing-Landau doubling time deaths (rolling mean) 0.0 196.1 392.2 daily change Germany-LK Dingolfing-Landau new cases (rolling 7d mean) Germany-LK Dingolfing-Landau new cases 0.000 1.961 3.922 5.883 daily change Germany-LK Dingolfing-Landau new deaths (rolling 7d mean) Germany-LK Dingolfing-Landau new deaths 0.0 51.7 103.4 155.1 206.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Dingolfing-Landau", dates="2020-03-15:");
2023-03-07T10:43:50.254568 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 Dingolfing-Landau LK Dingolfing-Landau 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 Dingolfing-Landau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Dingolfing-Landau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Dingolfing-Landau")
print(f'Population of country="Germany", subregion="LK Dingolfing-Landau": {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 Dingolfing-Landau": 98045 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 52245 2 301 0
2023-03-04 52243 1 301 0
2023-03-03 52242 8 301 0
2023-03-02 52234 5 301 0
2023-03-01 52229 9 301 0
2023-02-28 52220 16 301 0
2023-02-27 52204 19 301 0
2023-02-26 52185 6 301 0
2023-02-25 52179 3 301 0
2023-02-24 52176 29 301 0
2023-02-23 52147 22 301 0
2023-02-22 52125 18 301 0
2023-02-21 52107 16 301 0
2023-02-20 52091 19 301 0
2023-02-19 52072 4 301 0
2023-02-18 52068 5 301 0
2023-02-17 52063 8 301 0
2023-02-16 52055 17 301 0
2023-02-15 52038 22 301 0
2023-02-14 52016 20 301 0
2023-02-13 51996 26 301 1
2023-02-12 51970 6 300 1
2023-02-11 51964 7 299 0
2023-02-10 51957 9 299 0
2023-02-09 51948 14 299 0
2023-02-08 51934 20 299 1
2023-02-07 51914 22 298 0
2023-02-06 51892 17 298 0
2023-02-05 51875 6 298 0
2023-02-04 51869 2 298 0
2023-02-03 51867 21 298 0
2023-02-02 51846 21 298 0
2023-02-01 51825 7 298 0
2023-01-31 51818 22 298 0
2023-01-30 51796 18 298 0
2023-01-29 51778 4 298 0
2023-01-28 51774 5 298 0
2023-01-27 51769 12 298 0
2023-01-26 51757 6 298 0
2023-01-25 51751 4 298 0
2023-01-24 51747 22 298 1
2023-01-23 51725 17 297 1
2023-01-21 51708 3 296 0
2023-01-20 51705 9 296 0
2023-01-19 51696 18 296 1
2023-01-18 51678 8 295 1
2023-01-17 51670 9 294 0
2023-01-16 51661 18 294 0
2023-01-15 51643 1 294 0
2023-01-13 51642 14 294 0
2023-01-12 51628 9 294 0
2023-01-11 51619 9 294 0
2023-01-10 51610 11 294 1
2023-01-09 51599 19 293 0
2023-01-07 51580 3 293 0
2023-01-05 51577 15 293 0
2023-01-04 51562 7 293 1
2023-01-03 51555 9 292 0
2023-01-02 51546 6 292 0
2022-12-31 51540 3 292 0
2022-12-30 51537 10 292 0
2022-12-29 51527 15 292 0
2022-12-28 51512 13 292 0
2022-12-27 51499 11 292 0
2022-12-25 51488 4 292 0
2022-12-24 51484 5 292 0
2022-12-23 51479 10 292 0
2022-12-22 51469 10 292 1
2022-12-21 51459 19 291 0
2022-12-20 51440 15 291 1
2022-12-19 51425 28 290 0
2022-12-18 51397 1 290 0
2022-12-17 51396 7 290 0
2022-12-16 51389 10 290 0
2022-12-15 51379 7 290 0
2022-12-14 51372 13 290 0
2022-12-13 51359 13 290 0
2022-12-12 51346 30 290 1
2022-12-11 51316 4 289 0
2022-12-10 51312 4 289 0
2022-12-09 51308 17 289 0
2022-12-08 51291 2 289 0
2022-12-07 51289 19 289 0
2022-12-06 51270 18 289 0
2022-12-05 51252 11 289 0
2022-12-03 51241 5 289 0
2022-12-02 51236 9 289 0
2022-12-01 51227 8 289 0
2022-11-30 51219 16 289 0
2022-11-29 51203 26 289 1
2022-11-28 51177 13 288 2
2022-11-27 51164 1 286 0
2022-11-26 51163 5 286 0
2022-11-25 51158 3 286 0
2022-11-24 51155 10 286 0
2022-11-23 51145 8 286 0
2022-11-22 51137 21 286 0
2022-11-21 51116 17 286 0
2022-11-20 51099 1 286 0
2022-11-19 51098 4 286 0
2022-11-18 51094 11 286 0
2022-11-17 51083 6 286 0
2022-11-16 51077 10 286 0
2022-11-15 51067 22 286 0
2022-11-14 51045 19 286 0
2022-11-13 51026 1 286 0
2022-11-12 51025 7 286 0
2022-11-11 51018 19 286 0
2022-11-10 50999 28 286 1
2022-11-09 50971 26 285 1
2022-11-08 50945 25 284 0
2022-11-07 50920 42 284 0
2022-11-05 50878 34 284 2
2022-11-04 50844 38 282 1
2022-11-03 50806 40 281 0
2022-11-02 50766 75 281 1
2022-11-01 50691 7 280 0
2022-10-31 50684 50 280 0
2022-10-30 50634 2 280 1
2022-10-29 50632 36 279 0
2022-10-28 50596 50 279 0
2022-10-27 50546 58 279 0
2022-10-26 50488 63 279 0
2022-10-25 50425 84 279 0
2022-10-24 50341 140 279 1
2022-10-23 50201 2 278 0
2022-10-22 50199 89 278 0
2022-10-21 50110 76 278 1
2022-10-20 50034 122 277 1
2022-10-19 49912 109 276 2
2022-10-18 49803 92 274 0
2022-10-17 49711 102 274 0
2022-10-16 49609 7 274 0
2022-10-15 49602 89 274 0
2022-10-14 49513 117 274 0
2022-10-13 49396 70 274 0
2022-10-12 49326 136 274 0
2022-10-11 49190 166 274 2
2022-10-10 49024 125 272 0
2022-10-09 48899 49 272 0
2022-10-08 48850 52 272 1
2022-10-07 48798 172 271 0
2022-10-06 48626 197 271 0
2022-10-05 48429 150 271 2
2022-10-04 48279 140 269 0
2022-10-03 48139 7 269 1
2022-10-02 48132 19 268 0
2022-10-01 48113 62 268 0
2022-09-30 48051 121 268 1
2022-09-29 47930 122 267 2
2022-09-28 47808 128 265 1
2022-09-27 47680 130 264 2
2022-09-26 47550 118 262 0
2022-09-25 47432 5 262 0
2022-09-24 47427 67 262 0
2022-09-23 47360 66 262 0
2022-09-22 47294 79 262 0
2022-09-21 47215 81 262 0
2022-09-20 47134 84 262 0
2022-09-19 47050 68 262 0
2022-09-18 46982 8 262 0
2022-09-17 46974 49 262 0
2022-09-16 46925 55 262 0
2022-09-15 46870 39 262 0
2022-09-14 46831 57 262 0
2022-09-13 46774 47 262 0
2022-09-12 46727 45 262 0
2022-09-11 46682 2 262 0
2022-09-10 46680 12 262 0
2022-09-09 46668 39 262 0
2022-09-08 46629 28 262 0
2022-09-07 46601 38 262 1
2022-09-06 46563 31 261 0
2022-09-05 46532 55 261 0
2022-09-04 46477 1 261 0
2022-09-03 46476 29 261 1
2022-09-02 46447 43 260 0
2022-09-01 46404 16 260 0
2022-08-31 46388 40 260 0
2022-08-30 46348 67 260 0
2022-08-29 46281 70 260 0
2022-08-28 46211 1 260 0
2022-08-27 46210 27 260 1
2022-08-26 46183 59 259 0
2022-08-25 46124 49 259 0
2022-08-24 46075 35 259 0
2022-08-23 46040 61 259 0
2022-08-22 45979 80 259 0
2022-08-21 45899 2 259 0
2022-08-20 45897 20 259 0
2022-08-19 45877 33 259 0
2022-08-18 45844 25 259 1
2022-08-17 45819 50 258 0
2022-08-16 45769 72 258 1
2022-08-15 45697 6 257 1
2022-08-14 45691 2 256 0
2022-08-13 45689 21 256 0
2022-08-12 45668 52 256 0
2022-08-11 45616 32 256 0
2022-08-10 45584 47 256 0
2022-08-09 45537 59 256 3
2022-08-08 45478 50 253 0
2022-08-07 45428 1 253 0
2022-08-06 45427 33 253 1
2022-08-05 45394 35 252 0
2022-08-04 45359 62 252 0
2022-08-03 45297 46 252 0
2022-08-02 45251 88 252 0
2022-08-01 45163 101 252 0
2022-07-31 45062 5 252 0
2022-07-30 45057 26 252 0
2022-07-29 45031 61 252 0
2022-07-28 44970 125 252 0
2022-07-27 44845 96 252 1
2022-07-26 44749 145 251 0
2022-07-25 44604 163 251 0
2022-07-24 44441 7 251 0
2022-07-23 44434 94 251 0
2022-07-22 44340 173 251 1
2022-07-21 44167 193 250 2
2022-07-20 43974 189 248 0
2022-07-19 43785 177 248 0
2022-07-18 43608 142 248 0
2022-07-17 43466 5 248 0
2022-07-16 43461 142 248 0
2022-07-15 43319 99 248 0
2022-07-14 43220 161 248 4
2022-07-13 43059 135 244 2
2022-07-12 42924 182 242 1
2022-07-11 42742 94 241 0
2022-07-10 42648 8 241 0
2022-07-09 42640 75 241 0
2022-07-08 42565 87 241 1
2022-07-07 42478 122 240 0
2022-07-06 42356 169 240 0
2022-07-05 42187 206 240 1
2022-07-04 41981 113 239 1
2022-07-03 41868 6 238 0
2022-07-02 41862 84 238 0
2022-07-01 41778 117 238 0
2022-06-30 41661 97 238 0
2022-06-29 41564 95 238 0
2022-06-28 41469 102 238 0
2022-06-27 41367 129 238 0
2022-06-25 41238 70 238 0
2022-06-24 41168 46 238 0
2022-06-23 41122 91 238 0
2022-06-22 41031 65 238 0
2022-06-21 40966 76 238 0
2022-06-20 40890 96 238 0
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2021-10-25 7389 51 114 1
2021-10-24 7338 19 113 1
2021-10-23 7319 40 112 0
2021-10-22 7279 64 112 1
2021-10-21 7215 74 111 1
2021-10-20 7141 66 110 2
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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: 0:03:14.839402