Germany: LK Lindau (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 10:55:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Lindau", weeks=5);
2023-03-07T10:55:54.383050 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 37.7 LK Lindau, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Lindau cases daily growth factor Germany-LK Lindau cases daily growth factor (rolling mean) Germany-LK Lindau 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 Lindau deaths daily growth factor Germany-LK Lindau deaths daily growth factor (rolling mean) Germany-LK Lindau estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Lindau doubling time cases (rolling mean) 0.00 4.12 8.23 12.35 16.47 daily change Germany-LK Lindau new cases (rolling 7d mean) Germany-LK Lindau new cases 0.000 0.412 0.823 1.235 1.647 daily change Germany-LK Lindau new deaths (rolling 7d mean) Germany-LK Lindau new deaths 0.000 0.241 0.481 0.722 0.963
In [4]:
overview(country="Germany", subregion="LK Lindau");
2023-03-07T10:56:13.003681 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) 37.7 LK Lindau, 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 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 6 8 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Lindau cases daily growth factor Germany-LK Lindau cases daily growth factor (rolling mean) Germany-LK Lindau 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 R & growth factor (based on deaths) Germany-LK Lindau deaths daily growth factor Germany-LK Lindau deaths daily growth factor (rolling mean) Germany-LK Lindau 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-LK Lindau doubling time cases (rolling mean) Germany-LK Lindau doubling time deaths (rolling mean) 0.0 164.7 329.3 daily change Germany-LK Lindau new cases (rolling 7d mean) Germany-LK Lindau new cases 0.000 1.647 3.293 4.940 6.586 daily change Germany-LK Lindau new deaths (rolling 7d mean) Germany-LK Lindau new deaths 0.00 19.87 39.73 59.60 79.46 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Lindau", dates="2020-03-15:");
2023-03-07T10:58:28.701461 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 Lindau LK Lindau 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 Lindau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Lindau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Lindau")
print(f'Population of country="Germany", subregion="LK Lindau": {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 Lindau": 82330 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 39895 6 151 0
2023-03-03 39889 1 151 0
2023-03-02 39888 2 151 0
2023-03-01 39886 15 151 0
2023-02-28 39871 7 151 0
2023-02-27 39864 13 151 0
2023-02-24 39851 9 151 0
2023-02-23 39842 11 151 2
2023-02-22 39831 12 149 0
2023-02-21 39819 7 149 0
2023-02-20 39812 19 149 1
2023-02-17 39793 10 148 0
2023-02-16 39783 2 148 0
2023-02-15 39781 19 148 0
2023-02-14 39762 15 148 0
2023-02-13 39747 15 148 1
2023-02-10 39732 10 147 0
2023-02-09 39722 9 147 0
2023-02-08 39713 8 147 0
2023-02-07 39705 5 147 0
2023-02-06 39700 12 147 1
2023-02-03 39688 17 146 1
2023-02-02 39671 8 145 0
2023-02-01 39663 5 145 0
2023-01-31 39658 4 145 0
2023-01-30 39654 12 145 0
2023-01-27 39642 8 145 0
2023-01-26 39634 3 145 0
2023-01-25 39631 10 145 0
2023-01-24 39621 6 145 0
2023-01-23 39615 12 145 0
2023-01-20 39603 4 145 0
2023-01-19 39599 8 145 0
2023-01-18 39591 6 145 0
2023-01-17 39585 17 145 0
2023-01-16 39568 7 145 0
2023-01-13 39561 6 145 0
2023-01-12 39555 6 145 0
2023-01-11 39549 10 145 1
2023-01-10 39539 17 144 0
2023-01-09 39522 15 144 0
2023-01-05 39507 7 144 0
2023-01-04 39500 13 144 0
2023-01-03 39487 17 144 0
2023-01-02 39470 28 144 0
2022-12-30 39442 18 144 0
2022-12-29 39424 18 144 0
2022-12-28 39406 22 144 1
2022-12-27 39384 49 143 0
2022-12-23 39335 18 143 0
2022-12-22 39317 26 143 0
2022-12-21 39291 38 143 0
2022-12-20 39253 33 143 0
2022-12-19 39220 38 143 2
2022-12-16 39182 30 141 1
2022-12-15 39152 35 140 0
2022-12-14 39117 26 140 2
2022-12-13 39091 31 138 0
2022-12-12 39060 57 138 0
2022-12-09 39003 23 138 0
2022-12-08 38980 19 138 0
2022-12-07 38961 42 138 0
2022-12-06 38919 53 138 1
2022-12-05 38866 26 137 1
2022-12-02 38840 26 136 0
2022-12-01 38814 19 136 0
2022-11-30 38795 46 136 0
2022-11-29 38749 31 136 0
2022-11-28 38718 21 136 0
2022-11-25 38697 25 136 0
2022-11-24 38672 10 136 0
2022-11-23 38662 19 136 0
2022-11-22 38643 6 136 0
2022-11-21 38637 34 136 0
2022-11-18 38603 13 136 0
2022-11-17 38590 12 136 0
2022-11-16 38578 28 136 0
2022-11-15 38550 22 136 0
2022-11-14 38528 21 136 0
2022-11-11 38507 14 136 0
2022-11-10 38493 22 136 0
2022-11-09 38471 40 136 0
2022-11-08 38431 32 136 0
2022-11-07 38399 28 136 0
2022-11-04 38371 21 136 0
2022-11-03 38350 31 136 0
2022-11-02 38319 53 136 1
2022-10-31 38266 84 135 0
2022-10-28 38182 26 135 0
2022-10-27 38156 36 135 0
2022-10-26 38120 61 135 0
2022-10-25 38059 57 135 0
2022-10-24 38002 120 135 1
2022-10-21 37882 48 134 0
2022-10-20 37834 104 134 0
2022-10-19 37730 75 134 1
2022-10-18 37655 136 133 1
2022-10-17 37519 169 132 0
2022-10-15 37350 2 132 0
2022-10-14 37348 69 132 0
2022-10-13 37279 128 132 0
2022-10-12 37151 214 132 0
2022-10-11 36937 91 132 0
2022-10-10 36846 202 132 1
2022-10-08 36644 2 131 0
2022-10-07 36642 104 131 0
2022-10-06 36538 192 131 1
2022-10-05 36346 181 130 1
2022-10-04 36165 188 129 1
2022-10-03 35977 3 128 0
2022-10-01 35974 11 128 0
2022-09-30 35963 53 128 0
2022-09-29 35910 63 128 1
2022-09-28 35847 109 127 0
2022-09-27 35738 146 127 1
2022-09-26 35592 82 126 0
2022-09-23 35510 33 126 0
2022-09-22 35477 47 126 1
2022-09-21 35430 32 125 0
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2022-09-19 35382 36 125 0
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2022-09-15 35324 22 125 0
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2022-09-12 35236 43 125 0
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2022-09-08 35168 22 125 1
2022-09-07 35146 47 124 0
2022-09-06 35099 24 124 0
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2022-09-02 35016 13 124 0
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2022-08-31 34958 26 124 1
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2022-08-26 34840 5 123 0
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2022-08-05 34285 69 120 1
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2020-12-14 1460 21 36 0
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2020-12-07 1274 19 32 0
2020-12-06 1255 27 32 7
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2020-12-04 1212 20 25 4
2020-12-03 1192 36 21 0
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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: 0:03:01.052106