Germany: LK Neustadt a.d.Aisch-Bad Windsheim (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 11:12:35 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neustadt a.d.Aisch-Bad Windsheim", weeks=5);
2023-03-07T11:12:53.069248 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 97.3 LK Neustadt a.d.Aisch-Bad Windsheim, 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 80 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 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 Neustadt a.d.Aisch-Bad Windsheim cases daily growth factor Germany-LK Neustadt a.d.Aisch-Bad Windsheim cases daily growth factor (rolling mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim 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 Neustadt a.d.Aisch-Bad Windsheim deaths daily growth factor Germany-LK Neustadt a.d.Aisch-Bad Windsheim deaths daily growth factor (rolling mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim 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 Neustadt a.d.Aisch-Bad Windsheim doubling time cases (rolling mean) 0.0 20.4 40.7 61.1 81.4 daily change Germany-LK Neustadt a.d.Aisch-Bad Windsheim new cases (rolling 7d mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim new cases 0.000 0.204 0.407 0.611 0.814 daily change Germany-LK Neustadt a.d.Aisch-Bad Windsheim new deaths (rolling 7d mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim new deaths 0.000 0.270 0.539 0.809
In [4]:
overview(country="Germany", subregion="LK Neustadt a.d.Aisch-Bad Windsheim");
2023-03-07T11:13:14.813948 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) 97.3 LK Neustadt a.d.Aisch-Bad Windsheim, 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 1 2 3 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 Neustadt a.d.Aisch-Bad Windsheim cases daily growth factor Germany-LK Neustadt a.d.Aisch-Bad Windsheim cases daily growth factor (rolling mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim 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 Neustadt a.d.Aisch-Bad Windsheim deaths daily growth factor Germany-LK Neustadt a.d.Aisch-Bad Windsheim deaths daily growth factor (rolling mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim 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 cases doubling time [days] Germany-LK Neustadt a.d.Aisch-Bad Windsheim doubling time cases (rolling mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim doubling time deaths (rolling mean) 0.0 203.6 407.2 610.7 daily change Germany-LK Neustadt a.d.Aisch-Bad Windsheim new cases (rolling 7d mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim new cases 0.000 1.018 2.036 3.054 daily change Germany-LK Neustadt a.d.Aisch-Bad Windsheim new deaths (rolling 7d mean) Germany-LK Neustadt a.d.Aisch-Bad Windsheim new deaths 0.0 42.8 85.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neustadt a.d.Aisch-Bad Windsheim", dates="2020-03-15:");
2023-03-07T11:16:04.790056 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 Neustadt a.d.Aisch-Bad Windsheim LK Neustadt a.d.Aisch-Bad Windsheim 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 Neustadt a.d.Aisch-Bad Windsheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neustadt a.d.Aisch-Bad Windsheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neustadt a.d.Aisch-Bad Windsheim")
print(f'Population of country="Germany", subregion="LK Neustadt a.d.Aisch-Bad Windsheim": {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 Neustadt a.d.Aisch-Bad Windsheim": 101788 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 51236 10 185 0
2023-03-03 51226 10 185 0
2023-03-02 51216 14 185 0
2023-03-01 51202 37 185 0
2023-02-28 51165 28 185 0
2023-02-27 51137 61 185 0
2023-02-24 51076 24 185 0
2023-02-23 51052 35 185 0
2023-02-22 51017 84 185 1
2023-02-20 50933 47 184 0
2023-02-17 50886 24 184 0
2023-02-16 50862 30 184 0
2023-02-15 50832 34 184 0
2023-02-14 50798 43 184 0
2023-02-13 50755 34 184 0
2023-02-10 50721 22 184 0
2023-02-09 50699 25 184 0
2023-02-08 50674 41 184 0
2023-02-07 50633 26 184 0
2023-02-06 50607 59 184 0
2023-02-03 50548 18 184 0
2023-02-02 50530 16 184 0
2023-02-01 50514 18 184 0
2023-01-31 50496 33 184 0
2023-01-30 50463 30 184 0
2023-01-27 50433 13 184 0
2023-01-26 50420 17 184 0
2023-01-25 50403 11 184 0
2023-01-24 50392 19 184 0
2023-01-23 50373 24 184 0
2023-01-20 50349 9 184 0
2023-01-19 50340 9 184 0
2023-01-18 50331 16 184 0
2023-01-17 50315 12 184 0
2023-01-16 50303 24 184 0
2023-01-13 50279 4 184 1
2023-01-12 50275 8 183 0
2023-01-11 50267 25 183 0
2023-01-10 50242 18 183 1
2023-01-09 50224 32 182 0
2023-01-05 50192 13 182 1
2023-01-04 50179 28 181 0
2023-01-03 50151 31 181 0
2023-01-02 50120 44 181 0
2022-12-30 50076 14 181 0
2022-12-29 50062 41 181 1
2022-12-28 50021 25 180 0
2022-12-27 49996 57 180 0
2022-12-23 49939 21 180 0
2022-12-22 49918 26 180 0
2022-12-21 49892 26 180 0
2022-12-20 49866 31 180 0
2022-12-19 49835 35 180 1
2022-12-16 49800 23 179 0
2022-12-15 49777 16 179 0
2022-12-14 49761 31 179 0
2022-12-13 49730 24 179 0
2022-12-12 49706 38 179 0
2022-12-09 49668 29 179 1
2022-12-08 49639 21 178 0
2022-12-07 49618 36 178 0
2022-12-06 49582 27 178 0
2022-12-05 49555 26 178 0
2022-12-02 49529 24 178 0
2022-12-01 49505 14 178 0
2022-11-30 49491 30 178 0
2022-11-29 49461 35 178 0
2022-11-28 49426 44 178 0
2022-11-25 49382 40 178 1
2022-11-24 49342 18 177 0
2022-11-23 49324 20 177 0
2022-11-22 49304 25 177 0
2022-11-21 49279 32 177 0
2022-11-18 49247 21 177 0
2022-11-17 49226 21 177 0
2022-11-16 49205 24 177 0
2022-11-15 49181 39 177 0
2022-11-14 49142 40 177 0
2022-11-11 49102 20 177 0
2022-11-10 49082 34 177 1
2022-11-09 49048 44 176 0
2022-11-08 49004 58 176 0
2022-11-07 48946 68 176 0
2022-11-04 48878 47 176 1
2022-11-03 48831 41 175 1
2022-11-02 48790 75 174 0
2022-10-31 48715 77 174 1
2022-10-28 48638 51 173 0
2022-10-27 48587 52 173 0
2022-10-26 48535 100 173 0
2022-10-25 48435 95 173 0
2022-10-24 48340 223 173 1
2022-10-21 48117 135 172 0
2022-10-20 47982 111 172 1
2022-10-19 47871 220 171 0
2022-10-18 47651 202 171 0
2022-10-17 47449 227 171 2
2022-10-15 47222 1 169 0
2022-10-14 47221 145 169 0
2022-10-13 47076 164 169 0
2022-10-12 46912 239 169 0
2022-10-11 46673 161 169 1
2022-10-10 46512 346 168 4
2022-10-07 46166 192 164 0
2022-10-06 45974 215 164 0
2022-10-05 45759 184 164 1
2022-10-04 45575 179 163 2
2022-09-30 45396 116 161 0
2022-09-29 45280 130 161 0
2022-09-28 45150 134 161 0
2022-09-27 45016 106 161 0
2022-09-26 44910 104 161 0
2022-09-23 44806 68 161 0
2022-09-22 44738 62 161 0
2022-09-21 44676 62 161 0
2022-09-20 44614 54 161 0
2022-09-19 44560 48 161 0
2022-09-16 44512 45 161 0
2022-09-15 44467 34 161 0
2022-09-14 44433 51 161 0
2022-09-13 44382 54 161 0
2022-09-12 44328 54 161 0
2022-09-09 44274 51 161 0
2022-09-08 44223 46 161 1
2022-09-07 44177 63 160 0
2022-09-06 44114 31 160 0
2022-09-05 44083 49 160 0
2022-09-02 44034 20 160 0
2022-09-01 44014 26 160 0
2022-08-31 43988 52 160 0
2022-08-30 43936 50 160 0
2022-08-29 43886 51 160 0
2022-08-26 43835 38 160 0
2022-08-25 43797 24 160 0
2022-08-24 43773 61 160 0
2022-08-23 43712 48 160 0
2022-08-22 43664 66 160 0
2022-08-19 43598 37 160 0
2022-08-18 43561 36 160 0
2022-08-17 43525 70 160 0
2022-08-16 43455 44 160 0
2022-08-15 43411 57 160 0
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2022-08-08 43164 106 160 0
2022-08-05 43058 51 160 0
2022-08-04 43007 55 160 0
2022-08-03 42952 110 160 0
2022-08-02 42842 69 160 0
2022-08-01 42773 104 160 0
2022-07-29 42669 79 160 0
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2022-07-23 41937 1 160 0
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2022-07-19 41375 132 160 1
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2021-01-04 1919 7 46 0
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2021-01-01 1894 2 46 0
2020-12-31 1892 25 46 0
2020-12-30 1867 29 46 0
2020-12-29 1838 22 46 0
2020-12-28 1816 26 46 2
2020-12-27 1790 14 44 0
2020-12-26 1776 9 44 1
2020-12-25 1767 9 43 1
2020-12-24 1758 41 42 0
2020-12-23 1717 39 42 3
2020-12-22 1678 15 39 1
2020-12-21 1663 10 38 0
2020-12-20 1653 6 38 0
2020-12-19 1647 5 38 0
2020-12-18 1642 47 38 0
2020-12-17 1595 34 38 1
2020-12-16 1561 30 37 0
2020-12-15 1531 11 37 0
2020-12-14 1520 41 37 3
2020-12-13 1479 2 34 0
2020-12-12 1477 6 34 1
2020-12-11 1471 24 33 1
2020-12-10 1447 54 32 0
2020-12-09 1393 42 32 1
2020-12-08 1351 26 31 1
2020-12-07 1325 25 30 2
2020-12-06 1300 4 28 0
2020-12-05 1296 10 28 2
2020-12-04 1286 30 26 1
2020-12-03 1256 51 25 1
2020-12-02 1205 13 24 0
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2020-11-28 1144 5 22 0
2020-11-27 1139 12 22 0
2020-11-26 1127 53 22 1
2020-11-25 1074 36 21 0
2020-11-24 1038 75 21 2
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2020-11-20 906 25 16 0
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2020-11-16 796 62 14 4
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2020-11-03 469 32 6 0
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2020-10-26 360 4 6 0
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2020-09-24 294 1 6 0
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2020-09-20 287 1 6 0
2020-09-19 286 1 6 0
2020-09-18 285 3 6 0
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2020-08-31 273 1 6 0
2020-08-29 272 1 6 0
2020-08-26 271 1 6 0
2020-08-23 270 1 6 0
2020-08-21 269 1 6 0
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2020-08-17 264 3 6 0
2020-08-10 261 1 6 0
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2020-07-13 255 1 6 0
2020-07-09 254 1 6 0
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2020-07-03 252 1 6 0
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2020-06-23 244 1 6 0
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2020-05-19 242 1 6 0
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2020-05-12 238 3 6 0
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2020-05-02 225 1 6 0
2020-04-30 224 1 6 0
2020-04-25 223 3 6 0
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2020-04-21 214 2 6 1
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2020-04-17 201 27 5 0
2020-04-16 174 6 5 0
2020-04-15 168 9 5 0
2020-04-14 159 4 5 0
2020-04-13 155 10 5 0
2020-04-11 145 17 5 0
2020-04-10 128 8 5 0
2020-04-09 120 5 5 0
2020-04-08 115 3 5 0
2020-04-07 112 1 5 0
2020-04-06 111 4 5 0
2020-04-05 107 11 5 0
2020-04-04 96 7 5 0
2020-04-03 89 13 5 2
2020-04-02 76 2 3 1
2020-04-01 74 7 2 0
2020-03-31 67 8 2 0
2020-03-30 59 1 2 0
2020-03-29 58 3 2 0
2020-03-28 55 5 2 0
2020-03-27 50 8 2 1
2020-03-26 42 5 1 0
2020-03-25 37 7 1 0
2020-03-24 30 3 1 0
2020-03-23 27 3 1 0
2020-03-22 24 9 1 1
2020-03-21 15 2 0 0
2020-03-20 13 4 0 0
2020-03-19 9 3 0 0
2020-03-16 6 1 0 0
2020-03-13 5 1 0 0
2020-03-10 4 2 0 0

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: 0:03:46.477600