Germany: LK Altmarkkreis Salzwedel (Sachsen-Anhalt)¶

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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 15:36:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Altmarkkreis Salzwedel", weeks=5);
2023-03-07T15:36:59.774668 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 48.8 LK Altmarkkreis Salzwedel, 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 0.00 0.25 0.50 0.75 1.00 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 Altmarkkreis Salzwedel cases daily growth factor Germany-LK Altmarkkreis Salzwedel cases daily growth factor (rolling mean) Germany-LK Altmarkkreis Salzwedel 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 Altmarkkreis Salzwedel deaths daily growth factor Germany-LK Altmarkkreis Salzwedel deaths daily growth factor (rolling mean) Germany-LK Altmarkkreis Salzwedel 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 Altmarkkreis Salzwedel doubling time cases (rolling mean) 0.0 4.1 8.2 12.3 16.4 daily change Germany-LK Altmarkkreis Salzwedel new cases (rolling 7d mean) Germany-LK Altmarkkreis Salzwedel new cases 0.000 0.205 0.410 0.615 0.820 daily change Germany-LK Altmarkkreis Salzwedel new deaths (rolling 7d mean) Germany-LK Altmarkkreis Salzwedel new deaths 0.000 0.273 0.545 0.818
In [4]:
overview(country="Germany", subregion="LK Altmarkkreis Salzwedel");
2023-03-07T15:37:17.838517 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) 48.8 LK Altmarkkreis Salzwedel, 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Altmarkkreis Salzwedel cases daily growth factor Germany-LK Altmarkkreis Salzwedel cases daily growth factor (rolling mean) Germany-LK Altmarkkreis Salzwedel 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 Altmarkkreis Salzwedel deaths daily growth factor Germany-LK Altmarkkreis Salzwedel deaths daily growth factor (rolling mean) Germany-LK Altmarkkreis Salzwedel 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 Altmarkkreis Salzwedel doubling time cases (rolling mean) Germany-LK Altmarkkreis Salzwedel doubling time deaths (rolling mean) 0.0 164.0 327.9 491.9 655.9 daily change Germany-LK Altmarkkreis Salzwedel new cases (rolling 7d mean) Germany-LK Altmarkkreis Salzwedel new cases 0.00 0.82 1.64 2.46 3.28 daily change Germany-LK Altmarkkreis Salzwedel new deaths (rolling 7d mean) Germany-LK Altmarkkreis Salzwedel new deaths 0.0 37.6 75.3 112.9 150.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Altmarkkreis Salzwedel", dates="2020-03-15:");
2023-03-07T15:39:32.919334 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 Altmarkkreis Salzwedel LK Altmarkkreis Salzwedel 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 Altmarkkreis Salzwedel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Altmarkkreis Salzwedel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Altmarkkreis Salzwedel")
print(f'Population of country="Germany", subregion="LK Altmarkkreis Salzwedel": {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 Altmarkkreis Salzwedel": 81986 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 38480 8 173 0
2023-03-03 38472 8 173 0
2023-03-02 38464 8 173 0
2023-03-01 38456 5 173 0
2023-02-28 38451 11 173 0
2023-02-27 38440 13 173 0
2023-02-24 38427 11 173 0
2023-02-23 38416 14 173 0
2023-02-22 38402 15 173 0
2023-02-21 38387 9 173 0
2023-02-20 38378 13 173 0
2023-02-17 38365 7 173 0
2023-02-16 38358 15 173 0
2023-02-15 38343 14 173 0
2023-02-14 38329 16 173 0
2023-02-13 38313 12 173 0
2023-02-10 38301 5 173 1
2023-02-09 38296 9 172 0
2023-02-08 38287 8 172 0
2023-02-07 38279 15 172 0
2023-02-06 38264 6 172 0
2023-02-03 38258 3 172 0
2023-02-02 38255 11 172 0
2023-02-01 38244 10 172 0
2023-01-31 38234 18 172 0
2023-01-30 38216 9 172 0
2023-01-27 38207 7 172 0
2023-01-26 38200 13 172 0
2023-01-25 38187 11 172 0
2023-01-24 38176 15 172 0
2023-01-23 38161 12 172 1
2023-01-20 38149 3 171 0
2023-01-19 38146 2 171 0
2023-01-18 38144 8 171 0
2023-01-17 38136 12 171 0
2023-01-16 38124 12 171 0
2023-01-13 38112 12 171 0
2023-01-12 38100 12 171 0
2023-01-11 38088 31 171 0
2023-01-10 38057 22 171 0
2023-01-09 38035 42 171 1
2023-01-05 37993 35 170 0
2023-01-04 37958 42 170 0
2023-01-03 37916 30 170 0
2023-01-02 37886 28 170 0
2022-12-30 37858 32 170 0
2022-12-29 37826 34 170 0
2022-12-28 37792 56 170 0
2022-12-27 37736 43 170 1
2022-12-23 37693 21 169 1
2022-12-22 37672 29 168 0
2022-12-21 37643 53 168 0
2022-12-20 37590 25 168 0
2022-12-19 37565 65 168 2
2022-12-16 37500 38 166 0
2022-12-15 37462 44 166 1
2022-12-14 37418 45 165 0
2022-12-13 37373 69 165 0
2022-12-12 37304 47 165 0
2022-12-09 37257 52 165 0
2022-12-08 37205 50 165 1
2022-12-07 37155 69 164 0
2022-12-06 37086 51 164 0
2022-12-05 37035 45 164 0
2022-12-02 36990 46 164 1
2022-12-01 36944 41 163 0
2022-11-30 36903 84 163 0
2022-11-29 36819 58 163 0
2022-11-28 36761 72 163 0
2022-11-25 36689 44 163 0
2022-11-24 36645 54 163 0
2022-11-23 36591 66 163 0
2022-11-22 36525 50 163 0
2022-11-21 36475 32 163 0
2022-11-18 36443 34 163 0
2022-11-17 36409 21 163 0
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2022-11-11 36255 37 163 1
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2022-10-25 35382 115 161 1
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2022-10-20 35006 109 160 1
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2022-10-11 34228 122 156 0
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2022-10-07 33970 172 156 0
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2022-09-27 33110 68 155 0
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2021-03-03 1520 14 61 0
2021-03-02 1506 20 61 0
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2021-02-27 1483 9 61 0
2021-02-26 1474 9 61 0
2021-02-25 1465 4 61 0
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2021-02-23 1447 11 61 1
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2021-01-28 1245 19 58 0
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2021-01-19 1081 17 53 2
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2021-01-15 1034 30 50 3
2021-01-14 1004 28 47 1
2021-01-13 976 43 46 0
2021-01-12 933 20 46 4
2021-01-11 913 24 42 3
2021-01-10 889 6 39 0
2021-01-09 883 28 39 1
2021-01-08 855 26 38 1
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2021-01-05 758 10 34 2
2021-01-04 748 25 32 3
2021-01-03 723 3 29 1
2021-01-02 720 5 28 0
2021-01-01 715 9 28 0
2020-12-31 706 14 28 0
2020-12-30 692 28 28 1
2020-12-29 664 24 27 0
2020-12-28 640 1 27 0
2020-12-27 639 1 27 0
2020-12-26 638 7 27 1
2020-12-25 631 17 26 0
2020-12-24 614 13 26 1
2020-12-23 601 17 25 0
2020-12-22 584 12 25 2
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2020-12-17 530 21 23 2
2020-12-16 509 16 21 2
2020-12-15 493 17 19 2
2020-12-14 476 8 17 0
2020-12-13 468 10 17 0
2020-12-12 458 13 17 0
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2020-12-05 380 7 12 0
2020-12-04 373 6 12 1
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2020-12-01 346 8 10 1
2020-11-30 338 9 9 1
2020-11-29 329 10 8 0
2020-11-28 319 5 8 0
2020-11-27 314 12 8 1
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2020-11-24 284 11 7 0
2020-11-23 273 3 7 1
2020-11-22 270 2 6 0
2020-11-21 268 9 6 0
2020-11-20 259 4 6 0
2020-11-19 255 20 6 0
2020-11-18 235 2 6 0
2020-11-17 233 16 6 0
2020-11-16 217 6 6 1
2020-11-15 211 1 5 0
2020-11-14 210 4 5 0
2020-11-13 206 7 5 0
2020-11-12 199 12 5 0
2020-11-11 187 5 5 0
2020-11-10 182 7 5 0
2020-11-09 175 4 5 1
2020-11-08 171 6 4 0
2020-11-07 165 9 4 0
2020-11-06 156 14 4 0
2020-11-05 142 14 4 1
2020-11-04 128 14 3 2
2020-11-03 114 2 1 0
2020-11-02 112 14 1 0
2020-11-01 98 2 1 0
2020-10-30 96 2 1 0
2020-10-29 94 5 1 0
2020-10-28 89 2 1 0
2020-10-27 87 5 1 0
2020-10-26 82 8 1 0
2020-10-24 74 2 1 0
2020-10-23 72 3 1 0
2020-10-21 69 4 1 0
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2020-10-19 64 1 1 0
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2020-10-03 58 1 1 0
2020-09-26 57 1 1 0
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2020-08-24 43 1 1 0
2020-08-22 42 2 1 0
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2020-08-18 39 2 1 0
2020-08-15 37 1 1 0
2020-07-24 36 1 1 0
2020-07-22 35 1 1 0
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2020-06-12 33 1 1 0
2020-05-19 32 1 1 1
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2020-04-25 30 1 0 0
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2020-04-10 28 1 0 0
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2020-04-08 25 2 0 0
2020-04-07 23 2 0 0
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2020-04-01 16 1 0 0
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2020-03-30 14 2 0 0
2020-03-27 12 2 0 0
2020-03-25 10 1 0 0
2020-03-24 9 1 0 0
2020-03-23 8 4 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: 0:03:09.315027