Germany: LK Saale-Holzland-Kreis (Thüringen)¶

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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 16:09:35 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Saale-Holzland-Kreis", weeks=5);
2023-03-07T16:09:47.464818 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 63.0 LK Saale-Holzland-Kreis, 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 40 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 Saale-Holzland-Kreis new deaths (rolling 7d mean) Germany-LK Saale-Holzland-Kreis new deaths 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 Saale-Holzland-Kreis cases daily growth factor Germany-LK Saale-Holzland-Kreis cases daily growth factor (rolling mean) Germany-LK Saale-Holzland-Kreis 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 Saale-Holzland-Kreis deaths daily growth factor Germany-LK Saale-Holzland-Kreis deaths daily growth factor (rolling mean) Germany-LK Saale-Holzland-Kreis 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 Saale-Holzland-Kreis doubling time cases (rolling mean) 0.00 8.25 16.50 24.75 33.01 daily change Germany-LK Saale-Holzland-Kreis new cases (rolling 7d mean) Germany-LK Saale-Holzland-Kreis new cases 0.000 0.256 0.512 0.767
In [4]:
overview(country="Germany", subregion="LK Saale-Holzland-Kreis");
2023-03-07T16:10:05.361482 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) 63.0 LK Saale-Holzland-Kreis, 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 5 10 15 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 Saale-Holzland-Kreis cases daily growth factor Germany-LK Saale-Holzland-Kreis cases daily growth factor (rolling mean) Germany-LK Saale-Holzland-Kreis 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 Saale-Holzland-Kreis deaths daily growth factor Germany-LK Saale-Holzland-Kreis deaths daily growth factor (rolling mean) Germany-LK Saale-Holzland-Kreis 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 Saale-Holzland-Kreis doubling time cases (rolling mean) Germany-LK Saale-Holzland-Kreis doubling time deaths (rolling mean) 0.0 165.0 330.1 495.1 660.1 daily change Germany-LK Saale-Holzland-Kreis new cases (rolling 7d mean) Germany-LK Saale-Holzland-Kreis new cases 0.00 4.13 8.25 12.38 daily change Germany-LK Saale-Holzland-Kreis new deaths (rolling 7d mean) Germany-LK Saale-Holzland-Kreis new deaths 0.0 49.2 98.5 147.7 196.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Saale-Holzland-Kreis", dates="2020-03-15:");
2023-03-07T16:11:56.407930 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 Saale-Holzland-Kreis LK Saale-Holzland-Kreis 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 Saale-Holzland-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Saale-Holzland-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Saale-Holzland-Kreis")
print(f'Population of country="Germany", subregion="LK Saale-Holzland-Kreis": {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 Saale-Holzland-Kreis": 82513 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 37815 12 260 0
2023-03-03 37803 6 260 0
2023-03-02 37797 6 260 0
2023-03-01 37791 7 260 0
2023-02-28 37784 21 260 0
2023-02-27 37763 3 260 0
2023-02-26 37760 13 260 0
2023-02-24 37747 5 260 0
2023-02-23 37742 11 260 0
2023-02-22 37731 12 260 0
2023-02-21 37719 34 260 0
2023-02-20 37685 7 260 0
2023-02-19 37678 2 260 0
2023-02-18 37676 7 260 0
2023-02-17 37669 9 260 0
2023-02-16 37660 8 260 0
2023-02-15 37652 5 260 0
2023-02-14 37647 15 260 0
2023-02-13 37632 6 260 0
2023-02-12 37626 2 260 0
2023-02-11 37624 11 260 0
2023-02-10 37613 7 260 0
2023-02-09 37606 10 260 0
2023-02-08 37596 8 260 0
2023-02-07 37588 9 260 0
2023-02-06 37579 10 260 0
2023-02-03 37569 4 260 0
2023-02-02 37565 8 260 0
2023-02-01 37557 8 260 0
2023-01-31 37549 10 260 0
2023-01-29 37539 3 260 0
2023-01-28 37536 4 260 0
2023-01-27 37532 2 260 0
2023-01-26 37530 4 260 0
2023-01-25 37526 3 260 0
2023-01-24 37523 13 260 0
2023-01-23 37510 1 260 0
2023-01-21 37509 12 260 0
2023-01-20 37497 6 260 0
2023-01-19 37491 8 260 0
2023-01-18 37483 5 260 0
2023-01-17 37478 5 260 0
2023-01-15 37473 10 260 0
2023-01-13 37463 2 260 0
2023-01-12 37461 10 260 0
2023-01-11 37451 11 260 0
2023-01-10 37440 17 260 0
2023-01-09 37423 3 260 1
2023-01-08 37420 1 259 0
2023-01-07 37419 11 259 0
2023-01-06 37408 9 259 0
2023-01-05 37399 10 259 0
2023-01-04 37389 10 259 0
2023-01-03 37379 21 259 0
2023-01-02 37358 4 259 1
2023-01-01 37354 10 258 0
2022-12-31 37344 24 258 0
2022-12-30 37320 8 258 0
2022-12-29 37312 20 258 0
2022-12-28 37292 15 258 0
2022-12-27 37277 5 258 0
2022-12-26 37272 8 258 0
2022-12-25 37264 4 258 0
2022-12-24 37260 18 258 0
2022-12-23 37242 12 258 0
2022-12-22 37230 38 258 0
2022-12-21 37192 34 258 0
2022-12-20 37158 24 258 1
2022-12-19 37134 6 257 0
2022-12-18 37128 27 257 1
2022-12-16 37101 27 256 0
2022-12-15 37074 17 256 0
2022-12-14 37057 32 256 0
2022-12-13 37025 25 256 0
2022-12-12 37000 4 256 0
2022-12-11 36996 3 256 0
2022-12-10 36993 26 256 1
2022-12-09 36967 27 255 0
2022-12-08 36940 24 255 0
2022-12-07 36916 25 255 0
2022-12-06 36891 38 255 0
2022-12-05 36853 3 255 0
2022-12-04 36850 4 255 0
2022-12-03 36846 20 255 1
2022-12-02 36826 20 254 0
2022-12-01 36806 23 254 0
2022-11-30 36783 50 254 0
2022-11-29 36733 39 254 0
2022-11-28 36694 8 254 0
2022-11-27 36686 17 254 0
2022-11-26 36669 8 254 0
2022-11-25 36661 16 254 0
2022-11-24 36645 26 254 1
2022-11-23 36619 22 253 0
2022-11-22 36597 30 253 0
2022-11-21 36567 10 253 0
2022-11-20 36557 25 253 0
2022-11-18 36532 14 253 0
2022-11-17 36518 29 253 0
2022-11-16 36489 30 253 0
2022-11-15 36459 17 253 0
2022-11-14 36442 8 253 0
2022-11-13 36434 9 253 0
2022-11-12 36425 11 253 0
2022-11-11 36414 13 253 0
2022-11-10 36401 18 253 0
2022-11-09 36383 28 253 0
2022-11-08 36355 29 253 0
2022-11-07 36326 11 253 0
2022-11-06 36315 5 253 0
2022-11-05 36310 13 253 0
2022-11-04 36297 18 253 0
2022-11-03 36279 35 253 0
2022-11-02 36244 69 253 0
2022-11-01 36175 17 253 0
2022-10-31 36158 5 253 0
2022-10-30 36153 11 253 0
2022-10-29 36142 7 253 1
2022-10-28 36135 30 252 0
2022-10-27 36105 56 252 0
2022-10-26 36049 79 252 0
2022-10-25 35970 84 252 1
2022-10-24 35886 15 251 0
2022-10-23 35871 5 251 0
2022-10-22 35866 53 251 0
2022-10-21 35813 53 251 1
2022-10-20 35760 79 250 1
2022-10-19 35681 77 249 1
2022-10-18 35604 137 248 1
2022-10-17 35467 20 247 0
2022-10-16 35447 28 247 0
2022-10-15 35419 74 247 0
2022-10-14 35345 101 247 0
2022-10-13 35244 134 247 1
2022-10-12 35110 121 246 0
2022-10-11 34989 140 246 1
2022-10-10 34849 14 245 0
2022-10-09 34835 42 245 0
2022-10-08 34793 60 245 2
2022-10-07 34733 101 243 1
2022-10-06 34632 202 242 0
2022-10-05 34430 152 242 0
2022-10-04 34278 16 242 0
2022-10-03 34262 42 242 0
2022-10-02 34220 58 242 0
2022-09-30 34162 59 242 1
2022-09-29 34103 104 241 0
2022-09-28 33999 72 241 0
2022-09-27 33927 78 241 0
2022-09-26 33849 15 241 0
2022-09-25 33834 45 241 0
2022-09-24 33789 36 241 0
2022-09-23 33753 86 241 0
2022-09-22 33667 55 241 0
2022-09-21 33612 64 241 0
2022-09-20 33548 33 241 0
2022-09-18 33515 7 241 0
2022-09-16 33508 30 241 0
2022-09-15 33478 53 241 0
2022-09-14 33425 28 241 0
2022-09-13 33397 27 241 0
2022-09-12 33370 32 241 0
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2022-09-08 33309 12 241 0
2022-09-07 33297 36 241 0
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2022-09-05 33226 30 241 1
2022-09-02 33196 10 240 0
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2022-08-16 32792 34 238 0
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2022-07-30 32267 28 234 0
2022-07-29 32239 59 234 0
2022-07-28 32180 33 234 0
2022-07-27 32147 87 234 0
2022-07-26 32060 98 234 0
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2022-07-22 31870 42 234 0
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2022-07-13 31371 91 233 1
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2022-07-11 31198 97 231 0
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2022-07-01 30807 24 231 0
2022-06-30 30783 65 231 0
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2021-01-16 2179 33 85 2
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2021-01-14 2100 65 80 5
2021-01-13 2035 50 75 3
2021-01-12 1985 29 72 0
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2021-01-10 1936 25 70 0
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2021-01-04 1678 12 63 0
2021-01-03 1666 14 63 1
2021-01-02 1652 17 62 0
2021-01-01 1635 41 62 2
2020-12-31 1594 57 60 2
2020-12-30 1537 113 58 14
2020-12-29 1424 30 44 1
2020-12-28 1394 22 43 2
2020-12-27 1372 2 41 0
2020-12-26 1370 7 41 1
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2020-12-24 1322 52 40 0
2020-12-23 1270 68 40 2
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2020-12-20 1129 36 35 4
2020-12-19 1093 43 31 1
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2020-12-17 1014 42 29 2
2020-12-16 972 44 27 2
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2020-10-23 205 8 6 0
2020-10-22 197 15 6 0
2020-10-21 182 15 6 0
2020-10-20 167 7 6 0
2020-10-19 160 9 6 0
2020-10-17 151 4 6 0
2020-10-16 147 6 6 1
2020-10-15 141 8 5 0
2020-10-14 133 3 5 0
2020-10-13 130 5 5 0
2020-10-12 125 1 5 0
2020-10-10 124 7 5 0
2020-10-09 117 2 5 0
2020-10-08 115 3 5 0
2020-10-07 112 3 5 0
2020-10-06 109 1 5 0
2020-10-05 108 1 5 0
2020-10-03 107 3 5 0
2020-10-02 104 2 5 0
2020-09-30 102 1 5 1
2020-09-29 101 1 4 0
2020-09-24 100 1 4 0
2020-09-22 99 2 4 0
2020-09-21 97 2 4 1
2020-09-20 95 3 3 0
2020-09-12 92 1 3 0
2020-09-11 91 1 3 0
2020-09-09 90 1 3 0
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2020-09-03 84 5 3 0
2020-08-29 79 1 3 0
2020-08-27 78 1 3 0
2020-08-21 77 2 3 0
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2020-08-12 74 1 3 0
2020-06-10 73 1 3 0
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2020-05-16 68 1 3 0
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2020-05-09 66 4 3 0
2020-05-06 62 2 3 1
2020-05-05 60 1 2 0
2020-05-02 59 1 2 1
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2020-04-27 56 1 1 0
2020-04-23 55 2 1 0
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2020-04-21 51 1 1 0
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2020-04-07 47 1 0 0
2020-04-06 46 1 0 0
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2020-04-02 35 4 0 0
2020-04-01 31 1 0 0
2020-03-31 30 2 0 0
2020-03-30 28 1 0 0
2020-03-28 27 5 0 0
2020-03-27 22 1 0 0
2020-03-26 21 1 0 0
2020-03-25 20 3 0 0
2020-03-21 17 3 0 0
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2020-03-12 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: 0:02:29.696957