Germany: SK Coburg (Bayern)¶

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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 11:43:34 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Coburg", weeks=5);
2023-03-07T11:43:48.755779 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 80.6 SK Coburg, 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 2.0 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-SK Coburg cases daily growth factor Germany-SK Coburg cases daily growth factor (rolling mean) Germany-SK Coburg 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-SK Coburg deaths daily growth factor Germany-SK Coburg deaths daily growth factor (rolling mean) Germany-SK Coburg 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-SK Coburg doubling time cases (rolling mean) 0.00 4.10 8.19 12.29 daily change Germany-SK Coburg new cases (rolling 7d mean) Germany-SK Coburg new cases 0.000 0.205 0.410 0.614 0.819 daily change Germany-SK Coburg new deaths (rolling 7d mean) Germany-SK Coburg new deaths 0.000 0.274 0.549 0.823
In [4]:
overview(country="Germany", subregion="SK Coburg");
2023-03-07T11:44:08.576081 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) 80.6 SK Coburg, 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 10 20 30 40 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-SK Coburg cases daily growth factor Germany-SK Coburg cases daily growth factor (rolling mean) Germany-SK Coburg 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-SK Coburg deaths daily growth factor Germany-SK Coburg deaths daily growth factor (rolling mean) Germany-SK Coburg 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 cases doubling time [days] Germany-SK Coburg doubling time cases (rolling mean) Germany-SK Coburg doubling time deaths (rolling mean) 0.0 81.9 163.8 245.7 daily change Germany-SK Coburg new cases (rolling 7d mean) Germany-SK Coburg new cases 0.00 4.10 8.19 12.29 16.38 daily change Germany-SK Coburg new deaths (rolling 7d mean) Germany-SK Coburg new deaths 0.00 19.57 39.14 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Coburg", dates="2020-03-15:");
2023-03-07T11:46:28.665630 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: SK Coburg SK Coburg 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 0.01 0.01 0.1 0.1 1 1 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Coburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Coburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Coburg")
print(f'Population of country="Germany", subregion="SK Coburg": {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="SK Coburg": 40955 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 20302 5 124 0
2023-03-03 20297 4 124 0
2023-03-02 20293 5 124 0
2023-03-01 20288 6 124 0
2023-02-28 20282 13 124 0
2023-02-27 20269 15 124 0
2023-02-24 20254 7 124 0
2023-02-23 20247 8 124 0
2023-02-22 20239 9 124 0
2023-02-21 20230 8 124 0
2023-02-20 20222 13 124 1
2023-02-17 20209 9 123 0
2023-02-16 20200 4 123 0
2023-02-15 20196 6 123 0
2023-02-14 20190 9 123 0
2023-02-13 20181 8 123 0
2023-02-10 20173 6 123 0
2023-02-09 20167 6 123 0
2023-02-08 20161 2 123 0
2023-02-07 20159 5 123 0
2023-02-06 20154 5 123 0
2023-02-03 20149 4 123 0
2023-02-02 20145 2 123 0
2023-02-01 20143 4 123 0
2023-01-31 20139 6 123 0
2023-01-30 20133 4 123 0
2023-01-27 20129 1 123 0
2023-01-26 20128 1 123 0
2023-01-25 20127 1 123 0
2023-01-24 20126 8 123 0
2023-01-23 20118 6 123 0
2023-01-20 20112 1 123 0
2023-01-19 20111 2 123 0
2023-01-18 20109 1 123 0
2023-01-17 20108 3 123 0
2023-01-16 20105 3 123 0
2023-01-11 20102 2 123 0
2023-01-10 20100 4 123 0
2023-01-09 20096 9 123 0
2023-01-05 20087 5 123 0
2023-01-04 20082 7 123 0
2023-01-03 20075 5 123 0
2023-01-02 20070 6 123 0
2022-12-29 20064 10 123 0
2022-12-28 20054 21 123 0
2022-12-27 20033 12 123 0
2022-12-23 20021 8 123 0
2022-12-22 20013 1 123 0
2022-12-21 20012 9 123 0
2022-12-20 20003 16 123 0
2022-12-19 19987 6 123 0
2022-12-16 19981 8 123 0
2022-12-15 19973 6 123 0
2022-12-14 19967 12 123 0
2022-12-13 19955 10 123 0
2022-12-12 19945 17 123 0
2022-12-09 19928 11 123 0
2022-12-08 19917 6 123 0
2022-12-07 19911 4 123 0
2022-12-06 19907 3 123 0
2022-12-05 19904 11 123 0
2022-12-02 19893 4 123 0
2022-12-01 19889 6 123 0
2022-11-30 19883 6 123 0
2022-11-29 19877 8 123 0
2022-11-28 19869 15 123 1
2022-11-25 19854 3 122 0
2022-11-24 19851 3 122 0
2022-11-23 19848 9 122 0
2022-11-22 19839 6 122 0
2022-11-21 19833 15 122 0
2022-11-18 19818 5 122 0
2022-11-17 19813 7 122 0
2022-11-16 19806 13 122 0
2022-11-15 19793 12 122 0
2022-11-14 19781 36 122 0
2022-11-11 19745 7 122 0
2022-11-10 19738 21 122 1
2022-11-09 19717 8 121 0
2022-11-08 19709 19 121 0
2022-11-07 19690 45 121 0
2022-11-04 19645 8 121 0
2022-11-03 19637 40 121 0
2022-11-02 19597 23 121 0
2022-10-31 19574 35 121 0
2022-10-28 19539 32 121 0
2022-10-27 19507 40 121 0
2022-10-26 19467 16 121 0
2022-10-25 19451 30 121 0
2022-10-24 19421 41 121 0
2022-10-21 19380 43 121 0
2022-10-20 19337 36 121 0
2022-10-19 19301 51 121 0
2022-10-18 19250 66 121 0
2022-10-17 19184 96 121 1
2022-10-14 19088 44 120 0
2022-10-13 19044 71 120 0
2022-10-12 18973 78 120 0
2022-10-11 18895 86 120 0
2022-10-10 18809 135 120 0
2022-10-07 18674 78 120 0
2022-10-06 18596 98 120 0
2022-10-05 18498 101 120 0
2022-10-04 18397 108 120 0
2022-09-30 18289 40 120 0
2022-09-29 18249 46 120 0
2022-09-28 18203 61 120 0
2022-09-27 18142 39 120 0
2022-09-26 18103 63 120 0
2022-09-23 18040 14 120 0
2022-09-22 18026 34 120 0
2022-09-21 17992 20 120 0
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2022-09-19 17942 32 120 0
2022-09-16 17910 6 120 0
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2021-05-05 2143 23 89 0
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2021-05-03 2097 3 89 1
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2021-04-30 2074 15 88 0
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2021-04-28 2041 18 87 0
2021-04-27 2023 12 87 1
2021-04-26 2011 5 86 0
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2021-04-12 1833 1 86 0
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2021-04-02 1756 9 86 0
2021-04-01 1747 13 86 0
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2021-03-27 1718 4 86 0
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2021-01-31 1490 7 86 0
2021-01-30 1483 5 86 0
2021-01-29 1478 5 86 0
2021-01-28 1473 17 86 2
2021-01-27 1456 10 84 0
2021-01-26 1446 1 84 0
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2021-01-23 1435 9 84 0
2021-01-22 1426 7 84 0
2021-01-21 1419 28 84 4
2021-01-20 1391 10 80 0
2021-01-19 1381 15 80 1
2021-01-18 1366 9 79 3
2021-01-17 1357 4 76 0
2021-01-16 1353 6 76 0
2021-01-15 1347 10 76 0
2021-01-14 1337 13 76 2
2021-01-13 1324 14 74 1
2021-01-12 1310 7 73 0
2021-01-11 1303 8 73 0
2021-01-10 1295 31 73 7
2021-01-09 1264 20 66 0
2021-01-08 1244 14 66 0
2021-01-07 1230 12 66 1
2021-01-06 1218 29 65 2
2021-01-05 1189 22 63 2
2021-01-04 1167 7 61 0
2021-01-03 1160 13 61 2
2021-01-02 1147 4 59 1
2021-01-01 1143 22 58 0
2020-12-31 1121 15 58 0
2020-12-30 1106 64 58 5
2020-12-29 1042 35 53 6
2020-12-28 1007 24 47 3
2020-12-27 983 9 44 1
2020-12-26 974 26 43 2
2020-12-25 948 25 41 0
2020-12-24 923 35 41 2
2020-12-23 888 71 39 19
2020-12-22 817 30 20 1
2020-12-21 787 23 19 2
2020-12-20 764 13 17 0
2020-12-19 751 32 17 2
2020-12-18 719 27 15 3
2020-12-17 692 17 12 0
2020-12-16 675 32 12 0
2020-12-15 643 12 12 0
2020-12-14 631 28 12 1
2020-12-13 603 4 11 0
2020-12-12 599 20 11 1
2020-12-11 579 20 10 0
2020-12-10 559 19 10 0
2020-12-09 540 14 10 0
2020-12-08 526 13 10 0
2020-12-07 513 4 10 0
2020-12-06 509 20 10 0
2020-12-05 489 8 10 0
2020-12-04 481 6 10 0
2020-12-03 475 21 10 1
2020-12-02 454 19 9 0
2020-12-01 435 8 9 1
2020-11-30 427 9 8 0
2020-11-29 418 5 8 1
2020-11-28 413 8 7 0
2020-11-27 405 14 7 1
2020-11-26 391 5 6 0
2020-11-25 386 12 6 0
2020-11-24 374 10 6 0
2020-11-23 364 6 6 0
2020-11-22 358 4 6 0
2020-11-21 354 15 6 0
2020-11-20 339 10 6 0
2020-11-19 329 4 6 0
2020-11-18 325 25 6 0
2020-11-17 300 1 6 0
2020-11-16 299 4 6 0
2020-11-15 295 4 6 0
2020-11-14 291 8 6 0
2020-11-13 283 5 6 0
2020-11-12 278 4 6 0
2020-11-11 274 9 6 0
2020-11-10 265 2 6 0
2020-11-09 263 2 6 0
2020-11-08 261 3 6 0
2020-11-07 258 8 6 0
2020-11-06 250 18 6 0
2020-11-05 232 4 6 0
2020-11-04 228 5 6 1
2020-11-03 223 3 5 0
2020-11-02 220 3 5 0
2020-11-01 217 6 5 0
2020-10-31 211 4 5 0
2020-10-30 207 4 5 0
2020-10-29 203 11 5 1
2020-10-28 192 4 4 0
2020-10-27 188 2 4 0
2020-10-26 186 2 4 0
2020-10-25 184 4 4 0
2020-10-24 180 5 4 0
2020-10-23 175 3 4 0
2020-10-22 172 1 4 0
2020-10-21 171 11 4 0
2020-10-20 160 1 4 0
2020-10-18 159 3 4 0
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2020-10-16 154 1 4 0
2020-10-15 153 1 4 0
2020-10-14 152 1 4 0
2020-10-10 151 1 4 0
2020-10-08 150 1 4 0
2020-10-05 149 1 4 0
2020-10-02 148 1 4 0
2020-09-30 147 1 4 0
2020-09-24 146 1 4 0
2020-09-22 145 1 4 0
2020-09-19 144 2 4 0
2020-09-17 142 2 4 0
2020-09-16 140 1 4 0
2020-09-15 139 1 4 0
2020-09-13 138 1 4 0
2020-09-12 137 1 4 0
2020-09-06 136 1 4 0
2020-09-04 135 2 4 0
2020-09-01 133 1 4 0
2020-08-26 132 1 4 0
2020-08-21 131 1 4 0
2020-08-19 130 4 4 0
2020-08-17 126 1 4 0
2020-08-10 125 1 4 0
2020-08-04 124 1 4 0
2020-07-30 123 1 4 0
2020-07-29 122 2 4 0
2020-07-19 120 1 4 0
2020-07-17 119 1 4 0
2020-07-14 118 1 4 0
2020-06-30 117 1 4 0
2020-06-27 116 3 4 1
2020-06-25 113 1 3 1
2020-06-24 112 4 2 0
2020-06-23 108 1 2 0
2020-06-22 107 2 2 0
2020-06-18 105 1 2 0
2020-06-17 104 3 2 0
2020-06-15 101 1 2 0
2020-06-13 100 1 2 0
2020-06-11 99 1 2 0
2020-06-09 98 1 2 0
2020-06-07 97 3 2 0
2020-06-06 94 1 2 0
2020-06-04 93 3 2 0
2020-06-01 90 1 2 0
2020-05-30 89 1 2 0
2020-05-28 88 2 2 0
2020-05-27 86 3 2 0
2020-05-26 83 1 2 0
2020-05-25 82 1 2 0
2020-05-24 81 1 2 0
2020-05-23 80 2 2 0
2020-05-22 78 1 2 0
2020-05-21 77 1 2 0
2020-05-20 76 3 2 0
2020-05-19 73 1 2 0
2020-05-18 72 3 2 0
2020-05-16 69 1 2 0
2020-05-13 68 1 2 0
2020-05-12 67 1 2 1
2020-05-07 66 2 1 0
2020-05-03 64 3 1 0
2020-05-02 61 1 1 0
2020-05-01 60 2 1 1
2020-04-30 58 1 0 0
2020-04-26 57 1 0 0
2020-04-24 56 2 0 0
2020-04-21 54 1 0 0
2020-04-20 53 1 0 0
2020-04-18 52 1 0 0
2020-04-17 51 3 0 0
2020-04-16 48 1 0 0
2020-04-15 47 2 0 0
2020-04-13 45 7 0 0
2020-04-08 38 7 0 0
2020-04-05 31 1 0 0
2020-04-04 30 4 0 0
2020-04-02 26 1 0 0
2020-04-01 25 2 0 0
2020-03-29 23 3 0 0
2020-03-28 20 3 0 0
2020-03-27 17 1 0 0
2020-03-25 16 4 0 0
2020-03-24 12 1 0 0
2020-03-22 11 2 0 0
2020-03-21 9 3 0 0
2020-03-20 6 3 0 0
2020-03-18 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: 0:03:13.409756