Germany: LK Harz (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 15:54:04 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Harz", weeks=5);
2023-03-07T15:54:15.513199 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 45 45 50 50 55 55 7-day incidence rate (per 100K people) 37.3 LK Harz, 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 0.1 0.2 0.3 0.4 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 Harz cases daily growth factor Germany-LK Harz cases daily growth factor (rolling mean) Germany-LK Harz estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Harz deaths daily growth factor Germany-LK Harz deaths daily growth factor (rolling mean) Germany-LK Harz 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 Harz doubling time cases (rolling mean) 0.00 10.46 20.91 31.37 41.82 daily change Germany-LK Harz new cases (rolling 7d mean) Germany-LK Harz new cases 0.000 0.209 0.418 0.627 0.836 daily change Germany-LK Harz new deaths (rolling 7d mean) Germany-LK Harz new deaths 0.00 0.33 0.66 0.99
In [4]:
overview(country="Germany", subregion="LK Harz");
2023-03-07T15:54:32.572289 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) 37.3 LK Harz, 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 Harz cases daily growth factor Germany-LK Harz cases daily growth factor (rolling mean) Germany-LK Harz estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 R & growth factor (based on deaths) Germany-LK Harz deaths daily growth factor Germany-LK Harz deaths daily growth factor (rolling mean) Germany-LK Harz 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 cases doubling time [days] Germany-LK Harz doubling time cases (rolling mean) Germany-LK Harz doubling time deaths (rolling mean) 0 418 836 1255 daily change Germany-LK Harz new cases (rolling 7d mean) Germany-LK Harz new cases 0.00 4.18 8.36 12.55 daily change Germany-LK Harz new deaths (rolling 7d mean) Germany-LK Harz new deaths 0.0 176.4 352.7 529.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Harz", dates="2020-03-15:");
2023-03-07T15:56:24.893242 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 Harz LK Harz 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 Harz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Harz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Harz")
print(f'Population of country="Germany", subregion="LK Harz": {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 Harz": 209117 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 92571 14 597 0
2023-03-03 92557 21 597 0
2023-03-02 92536 13 597 0
2023-03-01 92523 14 597 0
2023-02-28 92509 16 597 0
2023-02-27 92493 48 597 0
2023-02-23 92445 15 597 0
2023-02-22 92430 15 597 0
2023-02-21 92415 22 597 0
2023-02-20 92393 29 597 0
2023-02-17 92364 18 597 0
2023-02-16 92346 13 597 1
2023-02-15 92333 22 596 0
2023-02-14 92311 22 596 1
2023-02-13 92289 29 595 1
2023-02-10 92260 11 594 0
2023-02-09 92249 17 594 1
2023-02-08 92232 11 593 0
2023-02-07 92221 23 593 1
2023-02-06 92198 17 592 0
2023-02-03 92181 15 592 0
2023-02-02 92166 21 592 0
2023-02-01 92145 24 592 0
2023-01-31 92121 18 592 0
2023-01-30 92103 35 592 0
2023-01-27 92068 16 592 1
2023-01-26 92052 6 591 0
2023-01-25 92046 11 591 0
2023-01-24 92035 27 591 2
2023-01-23 92008 21 589 1
2023-01-20 91987 12 588 0
2023-01-19 91975 8 588 0
2023-01-18 91967 13 588 0
2023-01-17 91954 26 588 0
2023-01-16 91928 25 588 1
2023-01-13 91903 18 587 0
2023-01-12 91885 16 587 0
2023-01-11 91869 34 587 0
2023-01-10 91835 42 587 0
2023-01-09 91793 84 587 1
2023-01-05 91709 34 586 0
2023-01-04 91675 46 586 0
2023-01-03 91629 53 586 0
2023-01-02 91576 86 586 0
2022-12-30 91490 42 586 0
2022-12-29 91448 72 586 1
2022-12-28 91376 109 585 1
2022-12-27 91267 155 584 1
2022-12-23 91112 64 583 1
2022-12-22 91048 90 582 0
2022-12-21 90958 84 582 1
2022-12-20 90874 103 581 0
2022-12-19 90771 118 581 1
2022-12-16 90653 75 580 1
2022-12-15 90578 81 579 1
2022-12-14 90497 98 578 0
2022-12-13 90399 93 578 0
2022-12-12 90306 133 578 1
2022-12-09 90173 105 577 0
2022-12-08 90068 86 577 0
2022-12-07 89982 133 577 0
2022-12-06 89849 80 577 0
2022-12-05 89769 122 577 0
2022-12-02 89647 40 577 0
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2022-11-30 89527 86 577 0
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2022-10-27 87323 118 571 2
2022-10-26 87205 194 569 0
2022-10-25 87011 178 569 1
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2022-10-21 86562 167 567 1
2022-10-20 86395 182 566 0
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2022-10-18 85870 201 566 2
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2022-10-14 85315 223 563 0
2022-10-13 85092 272 563 1
2022-10-12 84820 259 562 0
2022-10-11 84561 246 562 0
2022-10-10 84315 345 562 0
2022-10-07 83970 278 562 0
2022-10-06 83692 367 562 1
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2021-01-21 3837 74 176 4
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2021-01-15 3521 59 156 4
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2021-01-10 3210 1 141 0
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2021-01-04 2989 82 134 3
2021-01-03 2907 30 131 0
2021-01-02 2877 73 131 14
2021-01-01 2804 99 117 5
2020-12-31 2705 81 112 3
2020-12-30 2624 33 109 5
2020-12-29 2591 94 104 5
2020-12-28 2497 66 99 4
2020-12-27 2431 29 95 1
2020-12-26 2402 15 94 1
2020-12-25 2387 87 93 5
2020-12-24 2300 74 88 4
2020-12-23 2226 72 84 3
2020-12-22 2154 61 81 5
2020-12-21 2093 29 76 0
2020-12-20 2064 7 76 1
2020-12-19 2057 80 75 1
2020-12-18 1977 71 74 2
2020-12-17 1906 81 72 5
2020-12-16 1825 90 67 4
2020-12-15 1735 77 63 5
2020-12-14 1658 38 58 2
2020-12-13 1620 35 56 2
2020-12-12 1585 46 54 2
2020-12-11 1539 42 52 4
2020-12-10 1497 63 48 4
2020-12-09 1434 88 44 11
2020-12-08 1346 46 33 2
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2020-12-06 1264 24 31 1
2020-12-05 1240 53 30 2
2020-12-04 1187 65 28 2
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2020-12-02 1084 29 25 1
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2020-11-30 1020 15 23 2
2020-11-29 1005 7 21 0
2020-11-28 998 18 21 4
2020-11-27 980 34 17 0
2020-11-26 946 34 17 0
2020-11-25 912 38 17 0
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2020-11-06 589 21 8 0
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2020-10-20 348 6 6 0
2020-10-18 342 7 6 0
2020-10-17 335 9 6 0
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2020-10-15 318 4 6 0
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2020-10-11 309 4 6 0
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2020-10-09 301 2 6 0
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2020-10-04 290 4 6 0
2020-10-03 286 2 6 0
2020-09-30 284 1 6 0
2020-09-27 283 1 6 0
2020-09-26 282 1 6 0
2020-09-20 281 1 6 0
2020-09-19 280 1 6 0
2020-09-16 279 1 6 0
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2020-09-10 276 1 5 0
2020-09-07 275 1 5 0
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2020-08-29 272 1 5 0
2020-08-28 271 1 5 0
2020-08-21 270 1 5 0
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2020-07-31 263 1 5 0
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2020-07-10 261 2 5 0
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2020-05-05 254 4 5 0
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2020-04-22 226 1 4 0
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2020-04-19 222 4 4 0
2020-04-18 218 11 4 0
2020-04-17 207 45 4 0
2020-04-16 162 9 4 1
2020-04-15 153 3 3 0
2020-04-14 150 9 3 0
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2020-04-10 137 11 3 0
2020-04-09 126 4 3 0
2020-04-08 122 13 3 0
2020-04-07 109 4 3 0
2020-04-06 105 13 3 0
2020-04-05 92 17 3 0
2020-04-04 75 2 3 0
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2020-03-28 44 2 2 0
2020-03-27 42 7 2 0
2020-03-26 35 6 2 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: 0:02:33.006452