Germany: LK Main-Spessart (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 11:03:22 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Main-Spessart", weeks=5);
2023-03-07T11:03:33.289592 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 77.7 LK Main-Spessart, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Main-Spessart new deaths (rolling 7d mean) Germany-LK Main-Spessart new deaths 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 Main-Spessart cases daily growth factor Germany-LK Main-Spessart cases daily growth factor (rolling mean) Germany-LK Main-Spessart estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Main-Spessart deaths daily growth factor Germany-LK Main-Spessart deaths daily growth factor (rolling mean) Germany-LK Main-Spessart 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 Main-Spessart doubling time cases (rolling mean) 0.00 25.22 50.44 daily change Germany-LK Main-Spessart new cases (rolling 7d mean) Germany-LK Main-Spessart new cases 0.000 0.210 0.419 0.629 0.838
In [4]:
overview(country="Germany", subregion="LK Main-Spessart");
2023-03-07T11:03:51.617316 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) 77.7 LK Main-Spessart, 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 0.0 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 Main-Spessart cases daily growth factor Germany-LK Main-Spessart cases daily growth factor (rolling mean) Germany-LK Main-Spessart estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Main-Spessart deaths daily growth factor Germany-LK Main-Spessart deaths daily growth factor (rolling mean) Germany-LK Main-Spessart 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 Main-Spessart doubling time cases (rolling mean) Germany-LK Main-Spessart doubling time deaths (rolling mean) 0 252 504 757 daily change Germany-LK Main-Spessart new cases (rolling 7d mean) Germany-LK Main-Spessart new cases 0.00 3.15 6.31 9.46 12.61 daily change Germany-LK Main-Spessart new deaths (rolling 7d mean) Germany-LK Main-Spessart new deaths 0.0 68.7 137.4 206.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Main-Spessart", dates="2020-03-15:");
2023-03-07T11:05:51.898796 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 Main-Spessart LK Main-Spessart 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 Main-Spessart Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Main-Spessart")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Main-Spessart")
print(f'Population of country="Germany", subregion="LK Main-Spessart": {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 Main-Spessart": 126105 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 57911 14 276 0
2023-03-03 57897 8 276 0
2023-03-02 57889 15 276 0
2023-03-01 57874 22 276 0
2023-02-28 57852 39 276 0
2023-02-27 57813 73 276 0
2023-02-24 57740 23 276 0
2023-02-23 57717 33 276 0
2023-02-22 57684 58 276 0
2023-02-20 57626 53 276 0
2023-02-17 57573 25 276 0
2023-02-16 57548 17 276 0
2023-02-15 57531 28 276 0
2023-02-14 57503 27 276 0
2023-02-13 57476 39 276 0
2023-02-10 57437 20 276 0
2023-02-09 57417 18 276 0
2023-02-08 57399 27 276 0
2023-02-07 57372 28 276 0
2023-02-06 57344 34 276 0
2023-02-03 57310 11 276 0
2023-02-02 57299 14 276 0
2023-02-01 57285 16 276 0
2023-01-31 57269 22 276 0
2023-01-30 57247 31 276 0
2023-01-27 57216 7 276 0
2023-01-26 57209 14 276 0
2023-01-25 57195 15 276 0
2023-01-24 57180 24 276 0
2023-01-23 57156 19 276 0
2023-01-20 57137 12 276 0
2023-01-19 57125 10 276 0
2023-01-18 57115 21 276 0
2023-01-17 57094 13 276 0
2023-01-16 57081 23 276 0
2023-01-13 57058 8 276 0
2023-01-12 57050 11 276 0
2023-01-11 57039 22 276 0
2023-01-10 57017 19 276 0
2023-01-09 56998 59 276 1
2023-01-05 56939 26 275 0
2023-01-04 56913 16 275 1
2023-01-03 56897 40 274 1
2023-01-02 56857 68 273 0
2022-12-30 56789 14 273 0
2022-12-29 56775 35 273 0
2022-12-28 56740 47 273 0
2022-12-27 56693 74 273 0
2022-12-23 56619 33 273 0
2022-12-22 56586 48 273 0
2022-12-21 56538 38 273 0
2022-12-20 56500 56 273 0
2022-12-19 56444 52 273 0
2022-12-16 56392 24 273 0
2022-12-15 56368 35 273 0
2022-12-14 56333 35 273 0
2022-12-13 56298 30 273 0
2022-12-12 56268 53 273 0
2022-12-09 56215 26 273 0
2022-12-08 56189 21 273 0
2022-12-07 56168 31 273 0
2022-12-06 56137 34 273 1
2022-12-05 56103 76 272 0
2022-12-02 56027 18 272 0
2022-12-01 56009 14 272 0
2022-11-30 55995 29 272 0
2022-11-29 55966 40 272 0
2022-11-28 55926 38 272 0
2022-11-25 55888 26 272 0
2022-11-24 55862 29 272 1
2022-11-23 55833 37 271 0
2022-11-22 55796 36 271 1
2022-11-21 55760 38 270 0
2022-11-18 55722 35 270 0
2022-11-17 55687 22 270 0
2022-11-16 55665 37 270 0
2022-11-15 55628 42 270 0
2022-11-14 55586 68 270 0
2022-11-11 55518 26 270 0
2022-11-10 55492 43 270 1
2022-11-09 55449 84 269 0
2022-11-08 55365 49 269 0
2022-11-07 55316 103 269 0
2022-11-04 55213 55 269 1
2022-11-03 55158 57 268 0
2022-11-02 55101 105 268 0
2022-10-31 54996 173 268 0
2022-10-28 54823 43 268 0
2022-10-27 54780 69 268 0
2022-10-26 54711 172 268 1
2022-10-25 54539 104 267 0
2022-10-24 54435 246 267 0
2022-10-21 54189 107 267 1
2022-10-20 54082 119 266 0
2022-10-19 53963 196 266 1
2022-10-18 53767 146 265 0
2022-10-17 53621 245 265 0
2022-10-14 53376 99 265 0
2022-10-13 53277 172 265 0
2022-10-12 53105 263 265 0
2022-10-11 52842 139 265 0
2022-10-10 52703 320 265 0
2022-10-07 52383 180 265 0
2022-10-06 52203 308 265 1
2022-10-05 51895 212 264 0
2022-10-04 51683 208 264 1
2022-09-30 51475 76 263 0
2022-09-29 51399 112 263 0
2022-09-28 51287 173 263 0
2022-09-27 51114 94 263 0
2022-09-26 51020 118 263 0
2022-09-23 50902 46 263 0
2022-09-22 50856 36 263 0
2022-09-21 50820 95 263 0
2022-09-20 50725 47 263 0
2022-09-19 50678 104 263 0
2022-09-16 50574 33 263 0
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2022-09-14 50499 79 263 0
2022-09-13 50420 43 263 0
2022-09-12 50377 73 263 0
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2022-09-02 50050 26 263 0
2022-09-01 50024 33 263 0
2022-08-31 49991 77 263 0
2022-08-30 49914 65 263 0
2022-08-29 49849 126 263 0
2022-08-26 49723 55 263 0
2022-08-25 49668 61 263 0
2022-08-24 49607 175 263 0
2022-08-23 49432 90 263 0
2022-08-22 49342 262 263 0
2022-08-19 49080 86 263 0
2022-08-18 48994 170 263 1
2022-08-17 48824 140 262 0
2022-08-16 48684 184 262 0
2022-08-12 48500 138 262 0
2022-08-11 48362 86 262 0
2022-08-10 48276 123 262 0
2022-08-09 48153 62 262 0
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2022-08-04 47846 109 262 0
2022-08-03 47737 208 262 1
2022-08-02 47529 84 261 1
2022-08-01 47445 266 260 0
2022-07-29 47179 113 260 0
2022-07-28 47066 160 260 0
2022-07-27 46906 316 260 2
2022-07-26 46590 155 258 0
2022-07-25 46435 375 258 0
2022-07-22 46060 147 258 0
2022-07-21 45913 160 258 0
2022-07-20 45753 301 258 0
2022-07-19 45452 129 258 0
2022-07-18 45323 316 258 0
2022-07-15 45007 120 258 0
2022-07-14 44887 124 258 1
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2022-04-24 36949 45 252 0
2022-04-23 36904 198 252 0
2022-04-22 36706 262 252 0
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2020-12-11 1854 76 134 14
2020-12-10 1778 37 120 5
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2020-12-03 1449 66 94 9
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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: 0:02:39.606769