Germany: LK Berchtesgadener Land (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 10:33:05 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Berchtesgadener Land", weeks=5);
2023-03-07T10:33:19.898782 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 20 20 40 40 60 60 80 80 7-day incidence rate (per 100K people) 16.9 LK Berchtesgadener Land, 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 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-LK Berchtesgadener Land new deaths (rolling 7d mean) Germany-LK Berchtesgadener Land 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 R & growth factor (based on cases) Germany-LK Berchtesgadener Land cases daily growth factor Germany-LK Berchtesgadener Land cases daily growth factor (rolling mean) Germany-LK Berchtesgadener Land 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-LK Berchtesgadener Land deaths daily growth factor Germany-LK Berchtesgadener Land deaths daily growth factor (rolling mean) Germany-LK Berchtesgadener Land estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 15000 20000 cases doubling time [days] Germany-LK Berchtesgadener Land doubling time cases (rolling mean) 0.00 5.32 10.64 15.96 daily change Germany-LK Berchtesgadener Land new cases (rolling 7d mean) Germany-LK Berchtesgadener Land new cases 0.000 0.201 0.403 0.604 0.805
In [4]:
overview(country="Germany", subregion="LK Berchtesgadener Land");
2023-03-07T10:33:38.879017 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) 16.9 LK Berchtesgadener Land, 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 Berchtesgadener Land cases daily growth factor Germany-LK Berchtesgadener Land cases daily growth factor (rolling mean) Germany-LK Berchtesgadener Land 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-LK Berchtesgadener Land deaths daily growth factor Germany-LK Berchtesgadener Land deaths daily growth factor (rolling mean) Germany-LK Berchtesgadener Land estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 15000 20000 cases doubling time [days] Germany-LK Berchtesgadener Land doubling time cases (rolling mean) Germany-LK Berchtesgadener Land doubling time deaths (rolling mean) 0.0 212.8 425.6 638.3 daily change Germany-LK Berchtesgadener Land new cases (rolling 7d mean) Germany-LK Berchtesgadener Land new cases 0.000 1.064 2.128 3.192 daily change Germany-LK Berchtesgadener Land new deaths (rolling 7d mean) Germany-LK Berchtesgadener Land new deaths 0.0 29.5 59.0 88.5 118.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Berchtesgadener Land", dates="2020-03-15:");
2023-03-07T10:36:57.342836 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 Berchtesgadener Land LK Berchtesgadener Land 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 Berchtesgadener Land Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Berchtesgadener Land")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Berchtesgadener Land")
print(f'Population of country="Germany", subregion="LK Berchtesgadener Land": {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 Berchtesgadener Land": 106389 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 54497 1 216 0
2023-03-03 54496 2 216 0
2023-03-02 54494 1 216 0
2023-03-01 54493 4 216 0
2023-02-28 54489 10 216 0
2023-02-27 54479 3 216 0
2023-02-26 54476 1 216 0
2023-02-25 54475 6 216 0
2023-02-24 54469 1 216 0
2023-02-23 54468 13 216 0
2023-02-22 54455 4 216 0
2023-02-21 54451 17 216 0
2023-02-20 54434 8 216 0
2023-02-18 54426 6 216 0
2023-02-17 54420 3 216 0
2023-02-16 54417 7 216 0
2023-02-15 54410 6 216 0
2023-02-14 54404 12 216 0
2023-02-13 54392 8 216 0
2023-02-12 54384 2 216 0
2023-02-11 54382 5 216 0
2023-02-10 54377 11 216 0
2023-02-09 54366 19 216 0
2023-02-08 54347 16 216 0
2023-02-07 54331 19 216 0
2023-02-06 54312 17 216 0
2023-02-05 54295 3 216 0
2023-02-04 54292 7 216 0
2023-02-03 54285 6 216 0
2023-02-02 54279 15 216 0
2023-02-01 54264 12 216 0
2023-01-31 54252 18 216 0
2023-01-30 54234 10 216 0
2023-01-29 54224 2 216 0
2023-01-28 54222 1 216 0
2023-01-27 54221 6 216 0
2023-01-26 54215 15 216 0
2023-01-25 54200 25 216 0
2023-01-24 54175 3 216 0
2023-01-23 54172 2 216 0
2023-01-22 54170 1 216 0
2023-01-21 54169 10 216 0
2023-01-20 54159 7 216 0
2023-01-19 54152 12 216 0
2023-01-18 54140 7 216 0
2023-01-17 54133 15 216 0
2023-01-16 54118 9 216 1
2023-01-14 54109 6 215 0
2023-01-13 54103 4 215 0
2023-01-12 54099 11 215 0
2023-01-11 54088 8 215 0
2023-01-10 54080 46 215 0
2023-01-09 54034 4 215 0
2023-01-08 54030 6 215 0
2023-01-07 54024 6 215 0
2023-01-06 54018 6 215 0
2023-01-05 54012 18 215 0
2023-01-04 53994 5 215 0
2023-01-03 53989 19 215 0
2023-01-02 53970 6 215 0
2023-01-01 53964 3 215 0
2022-12-31 53961 11 215 0
2022-12-30 53950 15 215 0
2022-12-29 53935 26 215 0
2022-12-28 53909 28 215 0
2022-12-27 53881 13 215 0
2022-12-26 53868 11 215 0
2022-12-25 53857 14 215 0
2022-12-24 53843 21 215 0
2022-12-23 53822 19 215 0
2022-12-22 53803 38 215 0
2022-12-21 53765 32 215 1
2022-12-20 53733 32 214 0
2022-12-19 53701 12 214 0
2022-12-18 53689 21 214 0
2022-12-17 53668 6 214 1
2022-12-16 53662 24 213 0
2022-12-15 53638 18 213 1
2022-12-14 53620 31 212 0
2022-12-13 53589 29 212 0
2022-12-12 53560 19 212 0
2022-12-11 53541 7 212 0
2022-12-10 53534 9 212 0
2022-12-09 53525 15 212 0
2022-12-08 53510 15 212 0
2022-12-07 53495 16 212 0
2022-12-06 53479 27 212 2
2022-12-05 53452 19 210 0
2022-12-04 53433 12 210 0
2022-12-03 53421 6 210 0
2022-12-02 53415 18 210 0
2022-12-01 53397 14 210 0
2022-11-30 53383 22 210 0
2022-11-29 53361 24 210 0
2022-11-28 53337 8 210 0
2022-11-27 53329 8 210 0
2022-11-26 53321 14 210 0
2022-11-25 53307 17 210 0
2022-11-24 53290 15 210 0
2022-11-23 53275 23 210 0
2022-11-22 53252 25 210 0
2022-11-21 53227 24 210 0
2022-11-20 53203 13 210 0
2022-11-19 53190 7 210 0
2022-11-18 53183 20 210 0
2022-11-17 53163 19 210 1
2022-11-16 53144 11 209 1
2022-11-15 53133 23 208 0
2022-11-14 53110 9 208 0
2022-11-13 53101 17 208 0
2022-11-12 53084 15 208 0
2022-11-11 53069 17 208 0
2022-11-10 53052 22 208 0
2022-11-09 53030 21 208 0
2022-11-08 53009 25 208 0
2022-11-07 52984 15 208 0
2022-11-06 52969 13 208 0
2022-11-05 52956 17 208 0
2022-11-04 52939 32 208 0
2022-11-03 52907 42 208 0
2022-11-02 52865 22 208 0
2022-11-01 52843 35 208 1
2022-10-31 52808 6 207 0
2022-10-30 52802 21 207 0
2022-10-29 52781 24 207 0
2022-10-28 52757 19 207 0
2022-10-27 52738 52 207 0
2022-10-26 52686 36 207 0
2022-10-25 52650 89 207 0
2022-10-24 52561 28 207 0
2022-10-23 52533 23 207 0
2022-10-22 52510 40 207 1
2022-10-21 52470 48 206 0
2022-10-20 52422 62 206 0
2022-10-19 52360 64 206 0
2022-10-18 52296 122 206 2
2022-10-17 52174 61 204 0
2022-10-16 52113 32 204 0
2022-10-15 52081 83 204 0
2022-10-14 51998 97 204 0
2022-10-13 51901 134 204 0
2022-10-12 51767 117 204 0
2022-10-11 51650 218 204 0
2022-10-10 51432 100 204 0
2022-10-09 51332 41 204 0
2022-10-08 51291 119 204 1
2022-10-07 51172 148 203 0
2022-10-06 51024 212 203 0
2022-10-05 50812 309 203 0
2022-10-04 50503 52 203 1
2022-10-03 50451 41 202 1
2022-10-02 50410 79 201 0
2022-10-01 50331 98 201 0
2022-09-30 50233 141 201 2
2022-09-29 50092 86 199 0
2022-09-28 50006 154 199 1
2022-09-27 49852 203 198 0
2022-09-26 49649 18 198 0
2022-09-25 49631 75 198 0
2022-09-24 49556 55 198 0
2022-09-23 49501 62 198 0
2022-09-22 49439 66 198 0
2022-09-21 49373 70 198 1
2022-09-20 49303 89 197 0
2022-09-19 49214 23 197 0
2022-09-18 49191 29 197 0
2022-09-17 49162 24 197 0
2022-09-16 49138 34 197 0
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2022-08-23 48548 44 195 0
2022-08-22 48504 26 195 1
2022-08-21 48478 1 194 0
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2022-08-19 48456 19 194 0
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2022-08-14 48345 9 194 0
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2022-08-10 48249 27 193 0
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2022-07-28 47687 68 193 0
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2022-07-26 47520 135 193 0
2022-07-25 47385 9 193 0
2022-07-24 47376 28 193 0
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2022-07-22 47289 107 193 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: 0:04:08.745163