Germany: LK Miltenberg (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 11:03:04 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Miltenberg", weeks=5);
2023-03-07T11:03:20.813192 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 125 125 150 150 175 175 200 200 7-day incidence rate (per 100K people) 125.8 LK Miltenberg, 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 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 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 R & growth factor (based on cases) Germany-LK Miltenberg cases daily growth factor Germany-LK Miltenberg cases daily growth factor (rolling mean) Germany-LK Miltenberg 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 Miltenberg deaths daily growth factor Germany-LK Miltenberg deaths daily growth factor (rolling mean) Germany-LK Miltenberg 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 Miltenberg doubling time cases (rolling mean) 0.0 25.8 51.5 77.3 daily change Germany-LK Miltenberg new cases (rolling 7d mean) Germany-LK Miltenberg new cases 0.000 0.258 0.515 0.773 daily change Germany-LK Miltenberg new deaths (rolling 7d mean) Germany-LK Miltenberg new deaths 0.000 0.307 0.613 0.920
In [4]:
overview(country="Germany", subregion="LK Miltenberg");
2023-03-07T11:03:35.570484 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) 125.8 LK Miltenberg, 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 0 200 400 600 800 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Miltenberg cases daily growth factor Germany-LK Miltenberg cases daily growth factor (rolling mean) Germany-LK Miltenberg 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 Miltenberg deaths daily growth factor Germany-LK Miltenberg deaths daily growth factor (rolling mean) Germany-LK Miltenberg 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 8000 cases doubling time [days] Germany-LK Miltenberg doubling time cases (rolling mean) Germany-LK Miltenberg doubling time deaths (rolling mean) 0 258 515 773 1030 daily change Germany-LK Miltenberg new cases (rolling 7d mean) Germany-LK Miltenberg new cases 0.000 1.288 2.576 3.863 5.151 daily change Germany-LK Miltenberg new deaths (rolling 7d mean) Germany-LK Miltenberg new deaths 0.0 53.9 107.8 161.7 215.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Miltenberg", dates="2020-03-15:");
2023-03-07T11:05:57.728130 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 Miltenberg LK Miltenberg 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 Miltenberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Miltenberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Miltenberg")
print(f'Population of country="Germany", subregion="LK Miltenberg": {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 Miltenberg": 128782 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 67989 20 272 0
2023-03-03 67969 15 272 0
2023-03-02 67954 20 272 0
2023-03-01 67934 65 272 1
2023-02-28 67869 42 271 0
2023-02-27 67827 83 271 0
2023-02-24 67744 47 271 0
2023-02-23 67697 28 271 0
2023-02-22 67669 32 271 0
2023-02-21 67637 39 271 0
2023-02-20 67598 59 271 0
2023-02-17 67539 30 271 0
2023-02-16 67509 37 271 0
2023-02-15 67472 58 271 1
2023-02-14 67414 51 270 0
2023-02-13 67363 52 270 0
2023-02-12 67311 1 270 0
2023-02-10 67310 28 270 0
2023-02-09 67282 34 270 0
2023-02-08 67248 36 270 0
2023-02-07 67212 45 270 0
2023-02-06 67167 53 270 1
2023-02-03 67114 28 269 0
2023-02-02 67086 36 269 0
2023-02-01 67050 42 269 1
2023-01-31 67008 33 268 0
2023-01-30 66975 37 268 2
2023-01-27 66938 26 266 0
2023-01-26 66912 11 266 0
2023-01-25 66901 31 266 0
2023-01-24 66870 28 266 0
2023-01-23 66842 34 266 0
2023-01-20 66808 19 266 0
2023-01-19 66789 24 266 0
2023-01-18 66765 28 266 0
2023-01-17 66737 31 266 0
2023-01-16 66706 33 266 0
2023-01-13 66673 25 266 1
2023-01-12 66648 32 265 0
2023-01-11 66616 21 265 1
2023-01-10 66595 41 264 0
2023-01-09 66554 69 264 1
2023-01-05 66485 26 263 0
2023-01-04 66459 52 263 1
2023-01-03 66407 35 262 0
2023-01-02 66372 38 262 0
2022-12-30 66334 37 262 1
2022-12-29 66297 26 261 0
2022-12-28 66271 50 261 3
2022-12-27 66221 59 258 2
2022-12-26 66162 1 256 0
2022-12-25 66161 1 256 0
2022-12-24 66160 3 256 0
2022-12-23 66157 37 256 0
2022-12-22 66120 56 256 0
2022-12-21 66064 64 256 0
2022-12-20 66000 62 256 1
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2022-12-14 65796 50 253 1
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2022-12-12 65684 61 252 0
2022-12-09 65623 26 252 0
2022-12-08 65597 35 252 1
2022-12-07 65562 51 251 0
2022-12-06 65511 41 251 1
2022-12-05 65470 54 250 0
2022-12-02 65416 16 250 1
2022-12-01 65400 18 249 0
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2022-11-07 64797 75 246 1
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2022-10-31 64500 117 245 1
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2022-10-26 64233 115 244 0
2022-10-25 64118 178 244 0
2022-10-24 63940 213 244 1
2022-10-21 63727 117 243 1
2022-10-20 63610 205 242 0
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2022-10-18 63170 151 242 0
2022-10-17 63019 269 242 3
2022-10-15 62750 16 239 0
2022-10-14 62734 114 239 1
2022-10-13 62620 282 238 2
2022-10-12 62338 224 236 0
2022-10-11 62114 211 236 1
2022-10-10 61903 334 235 0
2022-10-07 61569 191 235 0
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2022-10-05 61016 248 235 1
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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: 0:03:41.154568