Germany: SK Memmingen (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 11:51:14 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Memmingen", weeks=5);
2023-03-07T11:51:26.011366 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 60.4 SK Memmingen, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Memmingen new deaths (rolling 7d mean) Germany-SK Memmingen new deaths 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Memmingen cases daily growth factor Germany-SK Memmingen cases daily growth factor (rolling mean) Germany-SK Memmingen 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-SK Memmingen deaths daily growth factor Germany-SK Memmingen deaths daily growth factor (rolling mean) Germany-SK Memmingen 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-SK Memmingen doubling time cases (rolling mean) 0.00 4.47 8.94 13.42 17.89 daily change Germany-SK Memmingen new cases (rolling 7d mean) Germany-SK Memmingen new cases 0.000 0.306 0.612 0.918
In [4]:
overview(country="Germany", subregion="SK Memmingen");
2023-03-07T11:51:42.747386 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) 60.4 SK Memmingen, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Memmingen cases daily growth factor Germany-SK Memmingen cases daily growth factor (rolling mean) Germany-SK Memmingen 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-SK Memmingen deaths daily growth factor Germany-SK Memmingen deaths daily growth factor (rolling mean) Germany-SK Memmingen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-SK Memmingen doubling time cases (rolling mean) Germany-SK Memmingen doubling time deaths (rolling mean) 0.0 89.4 178.9 268.3 daily change Germany-SK Memmingen new cases (rolling 7d mean) Germany-SK Memmingen new cases 0.000 0.447 0.894 1.342 1.789 daily change Germany-SK Memmingen new deaths (rolling 7d mean) Germany-SK Memmingen new deaths 0.000 1.618 3.236 4.854 6.471 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Memmingen", dates="2020-03-15:");
2023-03-07T11:53:52.731940 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: SK Memmingen SK Memmingen 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 SK Memmingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Memmingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Memmingen")
print(f'Population of country="Germany", subregion="SK Memmingen": {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="SK Memmingen": 44721 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 21459 6 44 0
2023-03-05 21453 2 44 0
2023-03-03 21451 2 44 0
2023-03-02 21449 4 44 0
2023-03-01 21445 7 44 0
2023-02-28 21438 6 44 0
2023-02-27 21432 3 44 0
2023-02-26 21429 3 44 0
2023-02-25 21426 1 44 0
2023-02-24 21425 3 44 0
2023-02-23 21422 2 44 0
2023-02-22 21420 5 44 0
2023-02-21 21415 19 44 0
2023-02-20 21396 8 44 0
2023-02-19 21388 5 44 0
2023-02-18 21383 6 44 0
2023-02-17 21377 6 44 0
2023-02-16 21371 4 44 0
2023-02-15 21367 8 44 0
2023-02-14 21359 4 44 0
2023-02-13 21355 13 44 0
2023-02-12 21342 1 44 0
2023-02-11 21341 4 44 0
2023-02-10 21337 1 44 0
2023-02-09 21336 4 44 0
2023-02-08 21332 5 44 0
2023-02-07 21327 6 44 0
2023-02-06 21321 7 44 0
2023-02-04 21314 1 44 0
2023-02-03 21313 4 44 0
2023-02-02 21309 4 44 0
2023-02-01 21305 6 44 0
2023-01-31 21299 5 44 0
2023-01-30 21294 3 44 0
2023-01-29 21291 2 44 0
2023-01-28 21289 1 44 0
2023-01-27 21288 8 44 0
2023-01-26 21280 7 44 0
2023-01-25 21273 3 44 0
2023-01-24 21270 3 44 0
2023-01-23 21267 1 44 0
2023-01-21 21266 3 44 0
2023-01-20 21263 1 44 0
2023-01-19 21262 1 44 0
2023-01-18 21261 6 44 0
2023-01-17 21255 5 44 0
2023-01-16 21250 3 44 0
2023-01-14 21247 3 44 0
2023-01-13 21244 5 44 0
2023-01-12 21239 2 44 0
2023-01-11 21237 4 44 0
2023-01-10 21233 3 44 0
2023-01-09 21230 11 44 0
2023-01-07 21219 3 44 0
2023-01-06 21216 3 44 0
2023-01-05 21213 9 44 0
2023-01-04 21204 7 44 0
2023-01-03 21197 19 44 0
2023-01-02 21178 5 44 1
2023-01-01 21173 3 43 0
2022-12-31 21170 1 43 0
2022-12-30 21169 3 43 0
2022-12-29 21166 10 43 0
2022-12-28 21156 7 43 0
2022-12-27 21149 3 43 0
2022-12-26 21146 3 43 0
2022-12-25 21143 3 43 0
2022-12-24 21140 1 43 0
2022-12-23 21139 6 43 0
2022-12-22 21133 10 43 0
2022-12-21 21123 13 43 0
2022-12-20 21110 15 43 0
2022-12-19 21095 9 43 0
2022-12-18 21086 1 43 0
2022-12-17 21085 5 43 0
2022-12-16 21080 6 43 0
2022-12-15 21074 1 43 0
2022-12-14 21073 13 43 0
2022-12-13 21060 13 43 0
2022-12-12 21047 4 43 0
2022-12-11 21043 2 43 0
2022-12-10 21041 4 43 0
2022-12-09 21037 7 43 0
2022-12-08 21030 7 43 0
2022-12-07 21023 6 43 0
2022-12-06 21017 5 43 0
2022-12-05 21012 9 43 0
2022-12-03 21003 2 43 0
2022-12-02 21001 7 43 0
2022-12-01 20994 8 43 0
2022-11-30 20986 3 43 0
2022-11-29 20983 5 43 0
2022-11-28 20978 9 43 0
2022-11-26 20969 2 43 0
2022-11-25 20967 7 43 0
2022-11-24 20960 4 43 0
2022-11-23 20956 7 43 0
2022-11-22 20949 9 43 0
2022-11-21 20940 8 43 0
2022-11-20 20932 1 43 0
2022-11-19 20931 4 43 0
2022-11-18 20927 8 43 0
2022-11-17 20919 9 43 0
2022-11-16 20910 9 43 0
2022-11-15 20901 14 43 0
2022-11-14 20887 7 43 0
2022-11-13 20880 1 43 0
2022-11-12 20879 9 43 0
2022-11-11 20870 7 43 0
2022-11-10 20863 8 43 0
2022-11-09 20855 11 43 0
2022-11-08 20844 24 43 0
2022-11-07 20820 6 43 0
2022-11-06 20814 3 43 0
2022-11-05 20811 6 43 0
2022-11-04 20805 16 43 0
2022-11-03 20789 26 43 0
2022-11-02 20763 28 43 0
2022-11-01 20735 2 43 0
2022-10-31 20733 45 43 0
2022-10-30 20688 1 43 1
2022-10-29 20687 18 42 0
2022-10-28 20669 3 42 0
2022-10-27 20666 20 42 0
2022-10-26 20646 28 42 0
2022-10-25 20618 40 42 0
2022-10-24 20578 55 42 0
2022-10-23 20523 4 42 0
2022-10-22 20519 11 42 0
2022-10-21 20508 22 42 0
2022-10-20 20486 42 42 0
2022-10-19 20444 35 42 0
2022-10-18 20409 78 42 0
2022-10-17 20331 35 42 1
2022-10-16 20296 9 41 0
2022-10-15 20287 40 41 0
2022-10-14 20247 38 41 0
2022-10-13 20209 48 41 0
2022-10-12 20161 56 41 0
2022-10-11 20105 74 41 0
2022-10-10 20031 66 41 0
2022-10-09 19965 5 41 1
2022-10-08 19960 41 40 0
2022-10-07 19919 59 40 0
2022-10-06 19860 83 40 0
2022-10-05 19777 106 40 0
2022-10-04 19671 65 40 0
2022-10-03 19606 12 40 0
2022-10-02 19594 3 40 0
2022-10-01 19591 34 40 0
2022-09-30 19557 37 40 0
2022-09-29 19520 46 40 0
2022-09-28 19474 49 40 0
2022-09-27 19425 57 40 0
2022-09-26 19368 27 40 0
2022-09-25 19341 3 40 0
2022-09-24 19338 17 40 0
2022-09-23 19321 35 40 0
2022-09-22 19286 24 40 0
2022-09-21 19262 24 40 0
2022-09-20 19238 36 40 0
2022-09-19 19202 18 40 0
2022-09-18 19184 3 40 0
2022-09-17 19181 15 40 0
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2022-09-14 19129 8 40 0
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2022-07-29 18405 35 39 0
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2022-07-27 18333 36 39 0
2022-07-26 18297 61 39 0
2022-07-25 18236 43 39 0
2022-07-24 18193 29 39 0
2022-07-23 18164 12 39 0
2022-07-22 18152 26 39 0
2022-07-21 18126 69 39 0
2022-07-20 18057 51 39 0
2022-07-19 18006 74 39 0
2022-07-18 17932 64 39 0
2022-07-17 17868 5 39 0
2022-07-16 17863 40 39 0
2022-07-15 17823 44 39 0
2022-07-14 17779 65 39 0
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2022-07-07 17476 63 39 0
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2020-12-25 853 23 15 1
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2020-12-22 789 18 13 0
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2020-10-02 148 7 1 1
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2020-09-19 129 1 0 0
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2020-03-31 30 1 0 0
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2020-03-27 27 2 0 0
2020-03-26 25 2 0 0
2020-03-25 23 1 0 0
2020-03-24 22 2 0 0
2020-03-23 20 3 0 0
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2020-03-19 11 3 0 0
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2020-03-16 3 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: 0:02:49.944253