Germany: LK Spree-Neiße (Brandenburg)¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
  • Execute this Jupyter Notebook using myBinder
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 12:20:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Spree-Neiße", weeks=5);
2023-03-07T12:21:08.723340 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 55.4 LK Spree-Neiße, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Spree-Neiße cases daily growth factor Germany-LK Spree-Neiße cases daily growth factor (rolling mean) Germany-LK Spree-Neiße 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 Spree-Neiße deaths daily growth factor Germany-LK Spree-Neiße deaths daily growth factor (rolling mean) Germany-LK Spree-Neiße 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 Spree-Neiße doubling time cases (rolling mean) 0.00 11.20 22.39 33.59 daily change Germany-LK Spree-Neiße new cases (rolling 7d mean) Germany-LK Spree-Neiße new cases 0.000 0.224 0.448 0.672 0.896 daily change Germany-LK Spree-Neiße new deaths (rolling 7d mean) Germany-LK Spree-Neiße new deaths 0.000 0.276 0.552 0.828
In [4]:
overview(country="Germany", subregion="LK Spree-Neiße");
2023-03-07T12:21:29.065054 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) 55.4 LK Spree-Neiße, 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 6 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 Spree-Neiße cases daily growth factor Germany-LK Spree-Neiße cases daily growth factor (rolling mean) Germany-LK Spree-Neiße 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 Spree-Neiße deaths daily growth factor Germany-LK Spree-Neiße deaths daily growth factor (rolling mean) Germany-LK Spree-Neiße 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 Spree-Neiße doubling time cases (rolling mean) Germany-LK Spree-Neiße doubling time deaths (rolling mean) 0.0 223.9 447.8 671.7 daily change Germany-LK Spree-Neiße new cases (rolling 7d mean) Germany-LK Spree-Neiße new cases 0.000 2.239 4.478 6.717 daily change Germany-LK Spree-Neiße new deaths (rolling 7d mean) Germany-LK Spree-Neiße new deaths 0.0 52.8 105.6 158.4 211.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Spree-Neiße", dates="2020-03-15:");
2023-03-07T12:23:46.657542 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 Spree-Neiße LK Spree-Neiße 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 Spree-Neiße Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Spree-Neiße")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Spree-Neiße")
print(f'Population of country="Germany", subregion="LK Spree-Neiße": {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 Spree-Neiße": 111955 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 56704 2 355 0
2023-03-03 56702 6 355 0
2023-03-02 56696 7 355 0
2023-03-01 56689 21 355 0
2023-02-28 56668 26 355 0
2023-02-27 56642 24 355 0
2023-02-24 56618 14 355 0
2023-02-23 56604 19 355 0
2023-02-22 56585 20 355 1
2023-02-21 56565 28 354 0
2023-02-20 56537 15 354 0
2023-02-17 56522 10 354 0
2023-02-16 56512 18 354 0
2023-02-15 56494 24 354 0
2023-02-14 56470 22 354 0
2023-02-13 56448 13 354 0
2023-02-10 56435 12 354 0
2023-02-09 56423 11 354 0
2023-02-08 56412 15 354 0
2023-02-07 56397 35 354 0
2023-02-06 56362 14 354 0
2023-02-03 56348 6 354 0
2023-02-02 56342 12 354 0
2023-02-01 56330 25 354 0
2023-01-31 56305 20 354 0
2023-01-30 56285 23 354 0
2023-01-27 56262 6 354 0
2023-01-26 56256 5 354 0
2023-01-25 56251 9 354 0
2023-01-24 56242 18 354 0
2023-01-23 56224 13 354 0
2023-01-20 56211 9 354 0
2023-01-19 56202 16 354 0
2023-01-18 56186 16 354 0
2023-01-17 56170 24 354 0
2023-01-16 56146 11 354 0
2023-01-13 56135 14 354 0
2023-01-12 56121 15 354 0
2023-01-11 56106 13 354 0
2023-01-10 56093 38 354 0
2023-01-09 56055 30 354 0
2023-01-06 56025 22 354 0
2023-01-05 56003 19 354 0
2023-01-04 55984 32 354 0
2023-01-03 55952 73 354 0
2023-01-02 55879 46 354 0
2022-12-30 55833 23 354 0
2022-12-29 55810 34 354 0
2022-12-28 55776 72 354 0
2022-12-27 55704 102 354 1
2022-12-23 55602 1 353 0
2022-12-22 55601 49 353 0
2022-12-21 55552 67 353 0
2022-12-20 55485 109 353 0
2022-12-19 55376 72 353 0
2022-12-16 55304 63 353 0
2022-12-15 55241 67 353 0
2022-12-14 55174 75 353 0
2022-12-13 55099 180 353 0
2022-12-12 54919 85 353 1
2022-12-10 54834 1 352 0
2022-12-09 54833 69 352 0
2022-12-08 54764 14 352 0
2022-12-07 54750 46 352 0
2022-12-06 54704 162 352 0
2022-12-05 54542 52 352 0
2022-12-04 54490 55 352 0
2022-12-02 54435 31 352 0
2022-12-01 54404 62 352 0
2022-11-30 54342 94 352 2
2022-11-29 54248 90 350 0
2022-11-28 54158 56 350 0
2022-11-27 54102 14 350 0
2022-11-25 54088 58 350 0
2022-11-24 54030 45 350 0
2022-11-23 53985 52 350 0
2022-11-22 53933 70 350 0
2022-11-21 53863 64 350 0
2022-11-20 53799 2 350 0
2022-11-19 53797 22 350 0
2022-11-18 53775 26 350 0
2022-11-17 53749 49 350 0
2022-11-16 53700 48 350 0
2022-11-15 53652 74 350 0
2022-11-14 53578 36 350 0
2022-11-13 53542 19 350 0
2022-11-11 53523 33 350 0
2022-11-10 53490 36 350 0
2022-11-09 53454 31 350 0
2022-11-08 53423 59 350 0
2022-11-07 53364 77 350 0
2022-11-06 53287 1 350 0
2022-11-05 53286 13 350 0
2022-11-04 53273 34 350 0
2022-11-03 53239 55 350 1
2022-11-02 53184 89 349 0
2022-11-01 53095 117 349 0
2022-10-31 52978 18 349 1
2022-10-30 52960 43 348 0
2022-10-29 52917 31 348 0
2022-10-28 52886 31 348 0
2022-10-27 52855 61 348 0
2022-10-26 52794 110 348 2
2022-10-25 52684 183 346 1
2022-10-24 52501 118 345 0
2022-10-23 52383 46 345 0
2022-10-22 52337 16 345 0
2022-10-21 52321 168 345 0
2022-10-20 52153 96 345 0
2022-10-19 52057 180 345 0
2022-10-18 51877 264 345 0
2022-10-17 51613 246 345 0
2022-10-16 51367 12 345 0
2022-10-15 51355 83 345 0
2022-10-14 51272 100 345 0
2022-10-13 51172 172 345 0
2022-10-12 51000 128 345 0
2022-10-11 50872 342 345 0
2022-10-10 50530 206 345 0
2022-10-09 50324 2 345 0
2022-10-08 50322 25 345 0
2022-10-07 50297 248 345 0
2022-10-06 50049 251 345 0
2022-10-05 49798 214 345 0
2022-10-04 49584 132 345 1
2022-10-03 49452 6 344 1
2022-10-02 49446 2 343 0
2022-10-01 49444 8 343 1
2022-09-30 49436 99 342 1
2022-09-29 49337 103 341 0
2022-09-28 49234 170 341 1
2022-09-27 49064 170 340 0
2022-09-26 48894 117 340 0
2022-09-25 48777 7 340 0
2022-09-24 48770 11 340 0
2022-09-23 48759 112 340 0
2022-09-22 48647 63 340 0
2022-09-21 48584 91 340 0
2022-09-20 48493 163 340 0
2022-09-19 48330 62 340 0
2022-09-18 48268 3 340 0
2022-09-17 48265 27 340 0
2022-09-16 48238 46 340 0
2022-09-15 48192 68 340 0
2022-09-14 48124 67 340 0
2022-09-13 48057 109 340 0
2022-09-12 47948 46 340 0
2022-09-11 47902 4 340 0
2022-09-10 47898 7 340 0
2022-09-09 47891 61 340 0
2022-09-08 47830 44 340 0
2022-09-07 47786 69 340 0
2022-09-06 47717 119 340 0
2022-09-05 47598 66 340 0
2022-09-04 47532 7 340 0
2022-09-03 47525 1 340 0
2022-09-02 47524 36 340 0
2022-09-01 47488 43 340 0
2022-08-31 47445 40 340 0
2022-08-30 47405 102 340 0
2022-08-29 47303 69 340 0
2022-08-28 47234 10 340 0
2022-08-27 47224 6 340 0
2022-08-26 47218 63 340 0
2022-08-25 47155 69 340 0
2022-08-24 47086 71 340 0
2022-08-23 47015 108 340 0
2022-08-22 46907 83 340 0
2022-08-21 46824 5 340 0
2022-08-20 46819 2 340 0
2022-08-19 46817 61 340 0
2022-08-18 46756 67 340 0
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2022-08-16 46592 119 340 1
2022-08-15 46473 156 339 0
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2022-08-12 46310 17 339 1
2022-08-11 46293 49 338 0
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2022-08-09 46126 169 337 0
2022-08-08 45957 84 337 1
2022-08-07 45873 3 336 0
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2022-07-29 45394 60 334 0
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2022-07-27 45275 103 334 1
2022-07-26 45172 155 333 0
2022-07-25 45017 99 333 0
2022-07-24 44918 37 333 0
2022-07-23 44881 45 333 0
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2022-07-21 44781 203 333 0
2022-07-20 44578 17 333 0
2022-07-19 44561 92 333 0
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2022-07-01 43484 71 332 0
2022-06-30 43413 61 332 0
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2022-06-27 43161 64 332 0
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2022-06-23 43008 41 332 0
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2020-12-31 3431 51 111 2
2020-12-30 3380 143 109 5
2020-12-29 3237 106 104 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: 0:03:04.787698