Germany: LK Sankt Wendel (Saarland)¶

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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 15:19:58 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Sankt Wendel", weeks=5);
2023-03-07T15:20:09.648450 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 200 200 250 250 300 300 350 350 7-day incidence rate (per 100K people) 309.9 LK Sankt Wendel, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 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 Sankt Wendel cases daily growth factor Germany-LK Sankt Wendel cases daily growth factor (rolling mean) Germany-LK Sankt Wendel 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 R & growth factor (based on deaths) Germany-LK Sankt Wendel deaths daily growth factor Germany-LK Sankt Wendel deaths daily growth factor (rolling mean) Germany-LK Sankt Wendel estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 cases doubling time [days] Germany-LK Sankt Wendel doubling time cases (rolling mean) 0.0 43.1 86.2 daily change Germany-LK Sankt Wendel new cases (rolling 7d mean) Germany-LK Sankt Wendel new cases 0.000 0.215 0.431 0.646 0.862 daily change Germany-LK Sankt Wendel new deaths (rolling 7d mean) Germany-LK Sankt Wendel new deaths 0.000 0.282 0.564 0.846
In [4]:
overview(country="Germany", subregion="LK Sankt Wendel");
2023-03-07T15:20:29.362048 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) 309.9 LK Sankt Wendel, 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 5 10 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 Sankt Wendel cases daily growth factor Germany-LK Sankt Wendel cases daily growth factor (rolling mean) Germany-LK Sankt Wendel 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 R & growth factor (based on deaths) Germany-LK Sankt Wendel deaths daily growth factor Germany-LK Sankt Wendel deaths daily growth factor (rolling mean) Germany-LK Sankt Wendel 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 cases doubling time [days] Germany-LK Sankt Wendel doubling time cases (rolling mean) Germany-LK Sankt Wendel doubling time deaths (rolling mean) 0.0 172.3 344.7 517.0 daily change Germany-LK Sankt Wendel new cases (rolling 7d mean) Germany-LK Sankt Wendel new cases 0.00 4.31 8.62 daily change Germany-LK Sankt Wendel new deaths (rolling 7d mean) Germany-LK Sankt Wendel new deaths 0.0 40.9 81.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Sankt Wendel", dates="2020-03-15:");
2023-03-07T15:22:51.715166 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 Sankt Wendel LK Sankt Wendel 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 Sankt Wendel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Sankt Wendel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Sankt Wendel")
print(f'Population of country="Germany", subregion="LK Sankt Wendel": {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 Sankt Wendel": 86170 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 46683 33 204 0
2023-03-03 46650 19 204 0
2023-03-02 46631 54 204 0
2023-03-01 46577 114 204 0
2023-02-28 46463 47 204 0
2023-02-27 46416 77 204 0
2023-02-24 46339 34 204 0
2023-02-23 46305 42 204 0
2023-02-22 46263 64 204 0
2023-02-21 46199 74 204 0
2023-02-17 46125 33 204 0
2023-02-16 46092 28 204 0
2023-02-15 46064 37 204 0
2023-02-14 46027 48 204 0
2023-02-13 45979 52 204 1
2023-02-10 45927 30 203 0
2023-02-09 45897 32 203 0
2023-02-08 45865 36 203 0
2023-02-07 45829 35 203 0
2023-02-06 45794 73 203 1
2023-02-03 45721 28 202 1
2023-02-02 45693 30 201 0
2023-02-01 45663 20 201 0
2023-01-31 45643 34 201 0
2023-01-30 45609 61 201 0
2023-01-27 45548 21 201 0
2023-01-26 45527 28 201 0
2023-01-25 45499 21 201 0
2023-01-24 45478 19 201 0
2023-01-23 45459 40 201 0
2023-01-20 45419 5 201 0
2023-01-19 45414 16 201 0
2023-01-18 45398 8 201 0
2023-01-17 45390 15 201 0
2023-01-16 45375 28 201 1
2023-01-13 45347 11 200 0
2023-01-12 45336 16 200 0
2023-01-11 45320 22 200 0
2023-01-10 45298 20 200 1
2023-01-09 45278 50 199 0
2023-01-06 45228 30 199 1
2023-01-05 45198 16 198 0
2023-01-04 45182 41 198 0
2023-01-03 45141 37 198 0
2023-01-02 45104 69 198 1
2022-12-30 45035 27 197 0
2022-12-29 45008 64 197 0
2022-12-28 44944 46 197 1
2022-12-27 44898 90 196 1
2022-12-23 44808 30 195 0
2022-12-22 44778 55 195 1
2022-12-21 44723 52 194 0
2022-12-20 44671 53 194 1
2022-12-19 44618 87 193 1
2022-12-16 44531 40 192 0
2022-12-15 44491 35 192 0
2022-12-14 44456 65 192 0
2022-12-13 44391 49 192 0
2022-12-12 44342 72 192 1
2022-12-09 44270 67 191 0
2022-12-08 44203 34 191 1
2022-12-07 44169 49 190 1
2022-12-06 44120 35 189 0
2022-12-05 44085 32 189 0
2022-12-02 44053 24 189 0
2022-12-01 44029 26 189 1
2022-11-30 44003 42 188 0
2022-11-29 43961 30 188 0
2022-11-28 43931 44 188 0
2022-11-25 43887 25 188 0
2022-11-24 43862 19 188 0
2022-11-23 43843 27 188 0
2022-11-22 43816 38 188 0
2022-11-21 43778 66 188 0
2022-11-18 43712 44 188 0
2022-11-17 43668 33 188 0
2022-11-16 43635 34 188 0
2022-11-15 43601 27 188 0
2022-11-14 43574 57 188 0
2022-11-11 43517 31 188 0
2022-11-10 43486 23 188 0
2022-11-09 43463 43 188 0
2022-11-08 43420 38 188 0
2022-11-07 43382 79 188 0
2022-11-04 43303 33 188 0
2022-11-03 43270 44 188 0
2022-11-02 43226 85 188 1
2022-10-31 43141 91 187 0
2022-10-28 43050 55 187 0
2022-10-27 42995 75 187 0
2022-10-26 42920 93 187 0
2022-10-25 42827 121 187 0
2022-10-24 42706 197 187 0
2022-10-21 42509 125 187 0
2022-10-20 42384 150 187 1
2022-10-19 42234 168 186 0
2022-10-18 42066 219 186 0
2022-10-17 41847 334 186 1
2022-10-14 41513 194 185 0
2022-10-13 41319 160 185 0
2022-10-12 41159 257 185 0
2022-10-11 40902 286 185 1
2022-10-10 40616 452 184 0
2022-10-07 40164 315 184 0
2022-10-06 39849 467 184 0
2022-10-05 39382 277 184 2
2022-10-04 39105 248 182 3
2022-09-30 38857 112 179 0
2022-09-29 38745 168 179 0
2022-09-28 38577 215 179 1
2022-09-27 38362 147 178 0
2022-09-26 38215 195 178 1
2022-09-23 38020 103 177 0
2022-09-22 37917 84 177 0
2022-09-21 37833 137 177 0
2022-09-20 37696 88 177 0
2022-09-19 37608 131 177 0
2022-09-16 37477 68 177 1
2022-09-15 37409 74 176 0
2022-09-14 37335 98 176 0
2022-09-13 37237 84 176 0
2022-09-12 37153 101 176 0
2022-09-09 37052 49 176 0
2022-09-08 37003 55 176 0
2022-09-07 36948 73 176 0
2022-09-06 36875 45 176 0
2022-09-05 36830 125 176 0
2022-09-02 36705 45 176 0
2022-09-01 36660 37 176 0
2022-08-31 36623 33 176 0
2022-08-30 36590 40 176 0
2022-08-29 36550 88 176 1
2022-08-26 36462 43 175 0
2022-08-25 36419 47 175 0
2022-08-24 36372 83 175 0
2022-08-23 36289 93 175 0
2022-08-22 36196 119 175 0
2022-08-19 36077 65 175 0
2022-08-18 36012 82 175 0
2022-08-17 35930 60 175 0
2022-08-16 35870 162 175 2
2022-08-12 35708 88 173 1
2022-08-11 35620 67 172 1
2022-08-10 35553 57 171 0
2022-08-09 35496 67 171 1
2022-08-08 35429 101 170 0
2022-08-05 35328 68 170 0
2022-08-04 35260 82 170 0
2022-08-03 35178 90 170 1
2022-08-02 35088 105 169 0
2022-08-01 34983 168 169 0
2022-07-29 34815 77 169 0
2022-07-28 34738 101 169 0
2022-07-27 34637 153 169 0
2022-07-26 34484 127 169 1
2022-07-25 34357 324 168 0
2022-07-22 34033 132 168 1
2022-07-21 33901 193 167 0
2022-07-20 33708 211 167 2
2022-07-19 33497 299 165 0
2022-07-18 33198 298 165 0
2022-07-15 32900 180 165 0
2022-07-14 32720 166 165 0
2022-07-13 32554 275 165 2
2022-07-12 32279 222 163 0
2022-07-11 32057 261 163 1
2022-07-08 31796 160 162 0
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2022-07-05 31305 152 161 0
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2022-06-30 30787 109 161 0
2022-06-29 30678 143 161 0
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2022-06-24 30235 151 161 0
2022-06-23 30084 115 161 1
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2022-06-21 29789 218 159 0
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2022-06-17 29341 163 159 0
2022-06-15 29178 134 159 0
2022-06-14 29044 102 159 0
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2022-06-10 28703 104 159 0
2022-06-09 28599 83 159 0
2022-06-08 28516 85 159 0
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2022-06-03 28297 47 159 0
2022-06-02 28250 31 159 0
2022-06-01 28219 49 159 0
2022-05-31 28170 46 159 1
2022-05-30 28124 91 158 0
2022-05-27 28033 69 158 0
2022-05-25 27964 30 158 0
2022-05-24 27934 38 158 0
2022-05-23 27896 55 158 0
2022-05-20 27841 71 158 0
2022-05-19 27770 52 158 1
2022-05-18 27718 30 157 0
2022-05-17 27688 63 157 0
2022-05-16 27625 79 157 0
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2022-05-03 26681 144 155 0
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2022-04-30 26390 80 155 0
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2022-04-28 26169 117 154 0
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2022-04-26 25938 231 154 0
2022-04-25 25707 121 154 0
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2022-04-21 25314 217 154 0
2022-04-20 25097 351 154 1
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2022-04-13 23949 289 153 1
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2021-01-05 1664 3 72 0
2021-01-04 1661 5 72 0
2021-01-03 1656 52 72 12
2021-01-02 1604 2 60 0
2020-12-31 1602 1 60 0
2020-12-30 1601 18 60 0
2020-12-29 1583 27 60 5
2020-12-28 1556 6 55 0
2020-12-27 1550 16 55 0
2020-12-24 1534 14 55 0
2020-12-23 1520 17 55 0
2020-12-22 1503 24 55 2
2020-12-21 1479 9 53 2
2020-12-19 1470 28 51 3
2020-12-18 1442 13 48 0
2020-12-17 1429 26 48 3
2020-12-16 1403 25 45 2
2020-12-15 1378 11 43 0
2020-12-14 1367 4 43 0
2020-12-13 1363 12 43 0
2020-12-12 1351 33 43 1
2020-12-11 1318 19 42 0
2020-12-10 1299 31 42 1
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2020-12-08 1235 26 40 2
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2020-12-06 1207 37 38 2
2020-12-05 1170 32 36 0
2020-12-04 1138 29 36 5
2020-12-03 1109 9 31 0
2020-12-02 1100 10 31 1
2020-12-01 1090 9 30 1
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2020-11-28 1069 31 29 2
2020-11-27 1038 6 27 0
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2020-11-24 1006 7 26 0
2020-11-23 999 1 26 0
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2020-11-20 982 25 26 1
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2020-11-17 934 14 25 0
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2020-11-13 896 24 24 0
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2020-11-04 746 13 23 0
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2020-10-24 495 9 15 0
2020-10-23 486 28 15 2
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2020-10-19 409 23 10 2
2020-10-18 386 13 8 0
2020-10-17 373 5 8 0
2020-10-16 368 25 8 1
2020-10-15 343 25 7 1
2020-10-14 318 26 6 0
2020-10-13 292 10 6 0
2020-10-12 282 19 6 0
2020-10-11 263 19 6 0
2020-10-10 244 11 6 0
2020-10-09 233 15 6 0
2020-10-08 218 6 6 0
2020-10-07 212 6 6 0
2020-10-06 206 4 6 0
2020-10-05 202 1 6 0
2020-10-04 201 1 6 0
2020-10-03 200 3 6 0
2020-10-02 197 6 6 0
2020-10-01 191 2 6 0
2020-09-30 189 1 6 0
2020-09-28 188 1 6 0
2020-09-23 187 1 6 0
2020-09-17 186 1 6 0
2020-09-09 185 1 6 0
2020-09-02 184 1 6 0
2020-08-28 183 1 6 0
2020-08-22 182 1 6 1
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2020-08-15 180 1 5 0
2020-08-14 179 1 5 0
2020-08-10 178 2 5 0
2020-08-04 176 1 5 0
2020-07-31 175 1 5 0
2020-07-30 174 1 5 0
2020-07-29 173 1 5 0
2020-07-27 172 1 5 0
2020-07-01 171 1 5 0
2020-06-17 170 1 5 0
2020-06-08 169 2 5 0
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2020-05-29 166 1 5 0
2020-05-18 165 1 5 0
2020-05-13 164 1 5 0
2020-05-11 163 1 5 0
2020-05-07 162 1 5 0
2020-05-06 161 1 5 0
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2020-04-23 152 1 3 0
2020-04-21 151 2 3 0
2020-04-15 149 3 3 0
2020-04-14 146 1 3 0
2020-04-12 145 1 3 0
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2020-04-09 142 8 3 0
2020-04-08 134 11 3 0
2020-04-07 123 4 3 0
2020-04-06 119 4 3 1
2020-04-04 115 7 2 0
2020-04-03 108 6 2 0
2020-04-02 102 5 2 0
2020-04-01 97 9 2 0
2020-03-31 88 3 2 0
2020-03-30 85 4 2 0
2020-03-28 81 8 2 1
2020-03-27 73 4 1 0
2020-03-26 69 7 1 0
2020-03-25 62 8 1 0
2020-03-24 54 5 1 0
2020-03-23 49 3 1 0
2020-03-22 46 7 1 0
2020-03-21 39 3 1 0
2020-03-20 36 7 1 0
2020-03-19 29 7 1 0
2020-03-18 22 3 1 0
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2020-03-13 10 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: 0:03:06.576548