Germany: LK Sonneberg (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 16:15:56 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Sonneberg", weeks=5);
2023-03-07T16:16:14.905010 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 80 100 100 7-day incidence rate (per 100K people) 54.9 LK Sonneberg, 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 0.5 1.0 1.5 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 Sonneberg cases daily growth factor Germany-LK Sonneberg cases daily growth factor (rolling mean) Germany-LK Sonneberg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Sonneberg deaths daily growth factor Germany-LK Sonneberg deaths daily growth factor (rolling mean) Germany-LK Sonneberg 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 Sonneberg doubling time cases (rolling mean) 0.00 5.65 11.30 16.95 daily change Germany-LK Sonneberg new cases (rolling 7d mean) Germany-LK Sonneberg new cases 0.000 0.283 0.565 0.848 daily change Germany-LK Sonneberg new deaths (rolling 7d mean) Germany-LK Sonneberg new deaths 0.000 0.276 0.552 0.828
In [4]:
overview(country="Germany", subregion="LK Sonneberg");
2023-03-07T16:16:32.839949 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 54.9 LK Sonneberg, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 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 Sonneberg cases daily growth factor Germany-LK Sonneberg cases daily growth factor (rolling mean) Germany-LK Sonneberg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Sonneberg deaths daily growth factor Germany-LK Sonneberg deaths daily growth factor (rolling mean) Germany-LK Sonneberg 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 Sonneberg doubling time cases (rolling mean) Germany-LK Sonneberg doubling time deaths (rolling mean) 0.0 56.5 113.0 169.5 226.0 daily change Germany-LK Sonneberg new cases (rolling 7d mean) Germany-LK Sonneberg new cases 0.000 1.413 2.825 4.238 5.650 daily change Germany-LK Sonneberg new deaths (rolling 7d mean) Germany-LK Sonneberg new deaths 0.0 56.9 113.7 170.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Sonneberg", dates="2020-03-15:");
2023-03-07T16:19:03.879266 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 Sonneberg LK Sonneberg 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 Sonneberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Sonneberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Sonneberg")
print(f'Population of country="Germany", subregion="LK Sonneberg": {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 Sonneberg": 56504 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 24662 6 307 0
2023-03-03 24656 5 307 0
2023-03-02 24651 5 307 0
2023-03-01 24646 9 307 0
2023-02-28 24637 6 307 0
2023-02-27 24631 16 307 1
2023-02-24 24615 4 306 0
2023-02-23 24611 10 306 0
2023-02-22 24601 18 306 0
2023-02-21 24583 4 306 1
2023-02-20 24579 11 305 0
2023-02-17 24568 7 305 0
2023-02-16 24561 7 305 0
2023-02-15 24554 6 305 0
2023-02-14 24548 6 305 0
2023-02-13 24542 16 305 0
2023-02-10 24526 6 305 0
2023-02-09 24520 14 305 0
2023-02-08 24506 8 305 0
2023-02-07 24498 11 305 0
2023-02-06 24487 6 305 0
2023-02-03 24481 5 305 0
2023-02-02 24476 1 305 0
2023-02-01 24475 5 305 0
2023-01-31 24470 1 305 0
2023-01-30 24469 8 305 0
2023-01-27 24461 1 305 0
2023-01-25 24460 3 305 0
2023-01-24 24457 2 305 0
2023-01-23 24455 3 305 0
2023-01-20 24452 3 305 1
2023-01-19 24449 3 304 0
2023-01-18 24446 2 304 0
2023-01-16 24444 7 304 0
2023-01-13 24437 3 304 0
2023-01-12 24434 2 304 0
2023-01-11 24432 9 304 0
2023-01-10 24423 3 304 0
2023-01-09 24420 4 304 0
2023-01-06 24416 3 304 0
2023-01-05 24413 10 304 0
2023-01-04 24403 13 304 1
2023-01-03 24390 9 303 0
2023-01-02 24381 5 303 0
2022-12-30 24376 3 303 0
2022-12-29 24373 23 303 0
2022-12-28 24350 6 303 0
2022-12-27 24344 15 303 1
2022-12-23 24329 5 302 0
2022-12-22 24324 2 302 0
2022-12-21 24322 13 302 0
2022-12-20 24309 2 302 0
2022-12-19 24307 22 302 0
2022-12-16 24285 6 302 0
2022-12-15 24279 6 302 0
2022-12-14 24273 33 302 0
2022-12-13 24240 9 302 0
2022-12-12 24231 9 302 0
2022-12-09 24222 8 302 0
2022-12-08 24214 8 302 0
2022-12-07 24206 13 302 0
2022-12-06 24193 3 302 0
2022-12-05 24190 33 302 0
2022-12-02 24157 9 302 0
2022-12-01 24148 13 302 0
2022-11-30 24135 19 302 0
2022-11-29 24116 8 302 0
2022-11-28 24108 21 302 0
2022-11-25 24087 11 302 0
2022-11-24 24076 8 302 0
2022-11-23 24068 13 302 0
2022-11-22 24055 16 302 0
2022-11-21 24039 10 302 0
2022-11-20 24029 1 302 0
2022-11-18 24028 9 302 0
2022-11-17 24019 15 302 0
2022-11-16 24004 24 302 0
2022-11-15 23980 11 302 1
2022-11-14 23969 24 301 0
2022-11-11 23945 17 301 0
2022-11-10 23928 5 301 0
2022-11-09 23923 19 301 0
2022-11-08 23904 11 301 1
2022-11-07 23893 24 300 0
2022-11-04 23869 10 300 0
2022-11-03 23859 33 300 0
2022-11-02 23826 43 300 0
2022-11-01 23783 33 300 1
2022-10-30 23750 1 299 0
2022-10-28 23749 21 299 0
2022-10-27 23728 42 299 0
2022-10-26 23686 39 299 0
2022-10-25 23647 18 299 0
2022-10-24 23629 50 299 1
2022-10-21 23579 33 298 0
2022-10-20 23546 40 298 0
2022-10-19 23506 64 298 2
2022-10-18 23442 69 296 0
2022-10-17 23373 108 296 0
2022-10-14 23265 53 296 0
2022-10-13 23212 64 296 0
2022-10-12 23148 53 296 0
2022-10-11 23095 79 296 0
2022-10-10 23016 98 296 1
2022-10-09 22918 1 295 0
2022-10-08 22917 1 295 0
2022-10-07 22916 52 295 0
2022-10-06 22864 108 295 0
2022-10-05 22756 51 295 0
2022-10-04 22705 86 295 3
2022-09-30 22619 37 292 0
2022-09-29 22582 38 292 0
2022-09-28 22544 51 292 0
2022-09-27 22493 42 292 0
2022-09-26 22451 60 292 0
2022-09-23 22391 45 292 0
2022-09-22 22346 39 292 0
2022-09-21 22307 64 292 0
2022-09-19 22243 23 292 0
2022-09-16 22220 20 292 0
2022-09-15 22200 21 292 0
2022-09-14 22179 26 292 0
2022-09-13 22153 13 292 0
2022-09-12 22140 41 292 1
2022-09-09 22099 21 291 0
2022-09-08 22078 11 291 0
2022-09-07 22067 31 291 0
2022-09-06 22036 19 291 0
2022-09-05 22017 41 291 0
2022-09-02 21976 5 291 0
2022-09-01 21971 16 291 0
2022-08-31 21955 24 291 0
2022-08-30 21931 18 291 0
2022-08-29 21913 49 291 0
2022-08-26 21864 8 291 0
2022-08-25 21856 15 291 0
2022-08-24 21841 29 291 0
2022-08-23 21812 20 291 0
2022-08-22 21792 44 291 0
2022-08-19 21748 22 291 1
2022-08-18 21726 26 290 0
2022-08-17 21700 32 290 0
2022-08-16 21668 18 290 0
2022-08-15 21650 21 290 0
2022-08-12 21629 17 290 0
2022-08-11 21612 14 290 0
2022-08-10 21598 33 290 0
2022-08-09 21565 7 290 0
2022-08-08 21558 42 290 0
2022-08-05 21516 15 290 0
2022-08-04 21501 29 290 1
2022-08-03 21472 42 289 1
2022-08-02 21430 32 288 0
2022-08-01 21398 46 288 0
2022-07-29 21352 27 288 1
2022-07-28 21325 31 287 0
2022-07-27 21294 40 287 0
2022-07-26 21254 42 287 0
2022-07-25 21212 57 287 0
2022-07-22 21155 28 287 0
2022-07-21 21127 37 287 0
2022-07-20 21090 61 287 0
2022-07-19 21029 33 287 0
2022-07-18 20996 71 287 1
2022-07-15 20925 30 286 0
2022-07-14 20895 36 286 0
2022-07-13 20859 23 286 0
2022-07-12 20836 44 286 0
2022-07-11 20792 50 286 0
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2022-07-01 20607 17 286 0
2022-06-30 20590 17 286 0
2022-06-29 20573 42 286 0
2022-06-28 20531 23 286 0
2022-06-27 20508 33 286 1
2022-06-24 20475 9 285 0
2022-06-23 20466 34 285 0
2022-06-22 20432 19 285 0
2022-06-21 20413 22 285 0
2022-06-20 20391 22 285 0
2022-06-17 20369 14 285 0
2022-06-16 20355 17 285 0
2022-06-15 20338 11 285 0
2022-06-14 20327 17 285 0
2022-06-13 20310 21 285 0
2022-06-10 20289 6 285 0
2022-06-09 20283 17 285 0
2022-06-08 20266 8 285 0
2022-06-07 20258 13 285 0
2022-06-03 20245 4 285 0
2022-06-02 20241 4 285 0
2022-06-01 20237 12 285 0
2022-05-31 20225 12 285 0
2022-05-30 20213 20 285 0
2022-05-25 20193 9 285 0
2022-05-24 20184 17 285 0
2022-05-23 20167 18 285 0
2022-05-20 20149 12 285 0
2022-05-19 20137 9 285 0
2022-05-18 20128 10 285 0
2022-05-17 20118 12 285 0
2022-05-16 20106 11 285 0
2022-05-15 20095 1 285 0
2022-05-13 20094 14 285 0
2022-05-12 20080 14 285 0
2022-05-11 20066 27 285 0
2022-05-10 20039 22 285 0
2022-05-09 20017 39 285 1
2022-05-06 19978 11 284 0
2022-05-05 19967 18 284 0
2022-05-04 19949 34 284 0
2022-05-03 19915 33 284 0
2022-05-02 19882 36 284 0
2022-04-30 19846 1 284 0
2022-04-29 19845 13 284 0
2022-04-28 19832 43 284 0
2022-04-27 19789 33 284 0
2022-04-26 19756 49 284 0
2022-04-25 19707 66 284 0
2022-04-24 19641 2 284 0
2022-04-22 19639 24 284 0
2022-04-21 19615 83 284 0
2022-04-20 19532 40 284 0
2022-04-19 19492 57 284 0
2022-04-18 19435 2 284 0
2022-04-16 19433 68 284 0
2022-04-15 19365 58 284 0
2022-04-14 19307 89 284 0
2022-04-13 19218 184 284 0
2022-04-12 19034 139 284 0
2022-04-11 18895 112 284 0
2022-04-10 18783 84 284 0
2022-04-09 18699 76 284 0
2022-04-08 18623 144 284 0
2022-04-07 18479 158 284 0
2022-04-06 18321 232 284 1
2022-04-05 18089 208 283 0
2022-04-04 17881 89 283 1
2022-04-03 17792 166 282 0
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2022-03-31 17402 257 282 0
2022-03-30 17145 203 282 0
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2020-12-24 1793 59 102 4
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2020-12-20 1596 13 92 1
2020-12-19 1583 52 91 5
2020-12-18 1531 40 86 2
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2020-12-16 1452 40 82 6
2020-12-15 1412 24 76 2
2020-12-14 1388 34 74 3
2020-12-13 1354 29 71 2
2020-12-12 1325 31 69 2
2020-12-11 1294 63 67 4
2020-12-10 1231 34 63 3
2020-12-09 1197 49 60 1
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2020-12-07 1108 11 57 0
2020-12-06 1097 19 57 1
2020-12-05 1078 19 56 3
2020-12-04 1059 29 53 0
2020-12-03 1030 40 53 0
2020-12-02 990 58 53 0
2020-12-01 932 25 53 1
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2020-05-06 129 5 15 2
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2020-03-28 20 4 0 0
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2020-03-22 6 1 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: 0:04:34.654906