Germany: LK Schweinfurt (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 11:29:15 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schweinfurt", weeks=5);
2023-03-07T11:29:33.535759 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) 65.4 LK Schweinfurt, 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 0.2 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 Schweinfurt cases daily growth factor Germany-LK Schweinfurt cases daily growth factor (rolling mean) Germany-LK Schweinfurt estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Schweinfurt deaths daily growth factor Germany-LK Schweinfurt deaths daily growth factor (rolling mean) Germany-LK Schweinfurt 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 Schweinfurt doubling time cases (rolling mean) 0.00 11.61 23.23 34.84 46.45 daily change Germany-LK Schweinfurt new cases (rolling 7d mean) Germany-LK Schweinfurt new cases 0.000 0.232 0.465 0.697 0.929 daily change Germany-LK Schweinfurt new deaths (rolling 7d mean) Germany-LK Schweinfurt new deaths 0.000 0.289 0.578 0.866
In [4]:
overview(country="Germany", subregion="LK Schweinfurt");
2023-03-07T11:29:49.250804 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) 65.4 LK Schweinfurt, 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 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 Schweinfurt cases daily growth factor Germany-LK Schweinfurt cases daily growth factor (rolling mean) Germany-LK Schweinfurt estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Schweinfurt deaths daily growth factor Germany-LK Schweinfurt deaths daily growth factor (rolling mean) Germany-LK Schweinfurt 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 Schweinfurt doubling time cases (rolling mean) Germany-LK Schweinfurt doubling time deaths (rolling mean) 0.0 232.3 464.5 696.8 daily change Germany-LK Schweinfurt new cases (rolling 7d mean) Germany-LK Schweinfurt new cases 0.000 2.323 4.645 daily change Germany-LK Schweinfurt new deaths (rolling 7d mean) Germany-LK Schweinfurt new deaths 0.0 52.9 105.9 158.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schweinfurt", dates="2020-03-15:");
2023-03-07T11:32:13.137379 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 Schweinfurt LK Schweinfurt 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 Schweinfurt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schweinfurt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schweinfurt")
print(f'Population of country="Germany", subregion="LK Schweinfurt": {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 Schweinfurt": 116134 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 63961 3 276 0
2023-03-05 63958 3 276 0
2023-03-04 63955 3 276 0
2023-03-03 63952 9 276 0
2023-03-02 63943 7 276 0
2023-03-01 63936 6 276 0
2023-02-28 63930 45 276 0
2023-02-27 63885 37 276 0
2023-02-26 63848 7 276 0
2023-02-25 63841 18 276 0
2023-02-24 63823 43 276 0
2023-02-23 63780 39 276 0
2023-02-22 63741 33 276 0
2023-02-21 63708 40 276 0
2023-02-20 63668 37 276 0
2023-02-19 63631 8 276 0
2023-02-18 63623 10 276 0
2023-02-17 63613 21 276 0
2023-02-16 63592 24 276 0
2023-02-15 63568 21 276 0
2023-02-14 63547 47 276 0
2023-02-13 63500 48 276 1
2023-02-12 63452 6 275 0
2023-02-11 63446 6 275 0
2023-02-10 63440 29 275 0
2023-02-09 63411 33 275 0
2023-02-08 63378 20 275 1
2023-02-07 63358 29 274 0
2023-02-06 63329 35 274 0
2023-02-05 63294 2 274 0
2023-02-04 63292 6 274 0
2023-02-03 63286 23 274 0
2023-02-02 63263 31 274 0
2023-02-01 63232 23 274 0
2023-01-31 63209 31 274 0
2023-01-30 63178 33 274 0
2023-01-29 63145 6 274 1
2023-01-28 63139 5 273 0
2023-01-27 63134 19 273 0
2023-01-26 63115 18 273 0
2023-01-25 63097 11 273 0
2023-01-24 63086 14 273 0
2023-01-23 63072 30 273 0
2023-01-22 63042 1 273 0
2023-01-21 63041 6 273 0
2023-01-20 63035 6 273 0
2023-01-19 63029 15 273 0
2023-01-18 63014 6 273 0
2023-01-17 63008 14 273 0
2023-01-16 62994 23 273 0
2023-01-15 62971 2 273 0
2023-01-14 62969 5 273 0
2023-01-13 62964 12 273 0
2023-01-12 62952 12 273 0
2023-01-11 62940 11 273 0
2023-01-10 62929 18 273 0
2023-01-09 62911 23 273 0
2023-01-08 62888 3 273 1
2023-01-07 62885 7 272 0
2023-01-06 62878 18 272 0
2023-01-05 62860 15 272 0
2023-01-04 62845 19 272 0
2023-01-03 62826 11 272 1
2023-01-02 62815 32 271 0
2023-01-01 62783 7 271 0
2022-12-31 62776 4 271 0
2022-12-30 62772 11 271 0
2022-12-29 62761 25 271 0
2022-12-28 62736 21 271 1
2022-12-27 62715 49 270 0
2022-12-26 62666 6 270 0
2022-12-25 62660 3 270 0
2022-12-24 62657 12 270 0
2022-12-23 62645 23 270 0
2022-12-22 62622 33 270 0
2022-12-21 62589 21 270 1
2022-12-20 62568 50 269 1
2022-12-19 62518 30 268 0
2022-12-18 62488 5 268 0
2022-12-17 62483 26 268 1
2022-12-16 62457 16 267 0
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2022-12-14 62410 22 267 1
2022-12-13 62388 28 266 0
2022-12-12 62360 36 266 0
2022-12-11 62324 10 266 0
2022-12-10 62314 17 266 0
2022-12-09 62297 13 266 1
2022-12-08 62284 24 265 1
2022-12-07 62260 24 264 1
2022-12-06 62236 38 263 0
2022-12-05 62198 15 263 0
2022-12-04 62183 8 263 0
2022-12-03 62175 15 263 0
2022-12-02 62160 30 263 0
2022-12-01 62130 21 263 0
2022-11-30 62109 20 263 1
2022-11-29 62089 34 262 0
2022-11-28 62055 32 262 1
2022-11-27 62023 3 261 0
2022-11-26 62020 5 261 0
2022-11-25 62015 20 261 0
2022-11-24 61995 19 261 0
2022-11-23 61976 30 261 1
2022-11-22 61946 25 260 0
2022-11-21 61921 14 260 0
2022-11-20 61907 9 260 0
2022-11-19 61898 12 260 0
2022-11-18 61886 17 260 0
2022-11-17 61869 17 260 0
2022-11-16 61852 23 260 0
2022-11-15 61829 27 260 0
2022-11-14 61802 25 260 0
2022-11-13 61777 4 260 0
2022-11-12 61773 17 260 0
2022-11-11 61756 39 260 0
2022-11-10 61717 22 260 0
2022-11-09 61695 36 260 1
2022-11-08 61659 42 259 0
2022-11-07 61617 62 259 0
2022-11-06 61555 3 259 0
2022-11-05 61552 29 259 0
2022-11-04 61523 20 259 0
2022-11-03 61503 64 259 0
2022-11-02 61439 65 259 0
2022-11-01 61374 44 259 0
2022-10-31 61330 53 259 1
2022-10-30 61277 30 258 0
2022-10-29 61247 37 258 0
2022-10-28 61210 65 258 0
2022-10-27 61145 69 258 1
2022-10-26 61076 73 257 0
2022-10-25 61003 91 257 0
2022-10-24 60912 184 257 1
2022-10-23 60728 37 256 0
2022-10-22 60691 59 256 0
2022-10-21 60632 97 256 0
2022-10-20 60535 93 256 0
2022-10-19 60442 132 256 1
2022-10-18 60310 170 255 0
2022-10-17 60140 143 255 0
2022-10-16 59997 73 255 1
2022-10-15 59924 74 254 0
2022-10-14 59850 162 254 0
2022-10-13 59688 167 254 0
2022-10-12 59521 200 254 0
2022-10-11 59321 254 254 2
2022-10-10 59067 299 252 0
2022-10-09 58768 26 252 0
2022-10-08 58742 178 252 0
2022-10-07 58564 124 252 1
2022-10-06 58440 288 251 0
2022-10-05 58152 239 251 0
2022-10-04 57913 231 251 1
2022-10-03 57682 24 250 0
2022-10-02 57658 23 250 0
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2022-09-29 57438 68 250 0
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2022-09-27 57216 146 250 0
2022-09-26 57070 121 250 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: 0:03:13.980647