Germany: SK Suhl (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 16:22:44 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Suhl", weeks=5);
2023-03-07T16:22:52.380684 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 10 10 20 20 30 30 40 40 7-day incidence rate (per 100K people) SK Suhl, 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 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-SK Suhl new deaths (rolling 7d mean) Germany-SK Suhl new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-SK Suhl cases daily growth factor Germany-SK Suhl cases daily growth factor (rolling mean) Germany-SK Suhl 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-SK Suhl deaths daily growth factor Germany-SK Suhl deaths daily growth factor (rolling mean) Germany-SK Suhl estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 15000 cases doubling time [days] Germany-SK Suhl doubling time cases (rolling mean) 0.000 1.803 3.605 5.408 daily change Germany-SK Suhl new cases (rolling 7d mean) Germany-SK Suhl new cases 0.000 0.313 0.626 0.940
In [4]:
overview(country="Germany", subregion="SK Suhl");
2023-03-07T16:23:05.781940 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 7-day incidence rate (per 100K people) 47.2 SK Suhl, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on cases) Germany-SK Suhl cases daily growth factor Germany-SK Suhl cases daily growth factor (rolling mean) Germany-SK Suhl 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-SK Suhl deaths daily growth factor Germany-SK Suhl deaths daily growth factor (rolling mean) Germany-SK Suhl 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-SK Suhl doubling time cases (rolling mean) Germany-SK Suhl doubling time deaths (rolling mean) 0.0 72.1 144.2 216.3 daily change Germany-SK Suhl new cases (rolling 7d mean) Germany-SK Suhl new cases 0.00 3.61 7.21 daily change Germany-SK Suhl new deaths (rolling 7d mean) Germany-SK Suhl new deaths 0.0 48.8 97.5 146.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Suhl", dates="2020-03-15:");
2023-03-07T16:24:24.113343 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 Suhl SK Suhl 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Suhl Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Suhl")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Suhl")
print(f'Population of country="Germany", subregion="SK Suhl": {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 Suhl": 36054 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 12884 7 210 0
2023-03-03 12877 2 210 0
2023-03-02 12875 1 210 0
2023-03-01 12874 3 210 0
2023-02-28 12871 4 210 0
2023-02-27 12867 3 210 0
2023-02-24 12864 1 210 0
2023-02-23 12863 1 210 0
2023-02-22 12862 3 210 0
2023-02-21 12859 3 210 0
2023-02-20 12856 4 210 0
2023-02-17 12852 4 210 0
2023-02-16 12848 3 210 0
2023-02-15 12845 1 210 0
2023-02-14 12844 1 210 0
2023-02-13 12843 5 210 0
2023-02-10 12838 1 210 0
2023-02-08 12837 1 210 0
2023-02-06 12836 2 210 0
2023-02-03 12834 1 210 0
2023-02-01 12833 2 210 0
2023-01-26 12831 3 210 0
2023-01-23 12828 3 210 0
2023-01-16 12825 1 210 0
2023-01-13 12824 1 210 0
2023-01-12 12823 3 210 0
2023-01-09 12820 3 210 0
2023-01-06 12817 3 210 0
2023-01-05 12814 6 210 0
2023-01-04 12808 2 210 0
2023-01-03 12806 8 210 0
2023-01-02 12798 8 210 1
2022-12-30 12790 3 209 0
2022-12-29 12787 3 209 0
2022-12-28 12784 4 209 1
2022-12-27 12780 8 208 0
2022-12-23 12772 8 208 0
2022-12-22 12764 10 208 0
2022-12-21 12754 4 208 1
2022-12-20 12750 6 207 0
2022-12-19 12744 12 207 1
2022-12-16 12732 4 206 0
2022-12-15 12728 2 206 0
2022-12-14 12726 9 206 0
2022-12-13 12717 8 206 1
2022-12-12 12709 15 205 0
2022-12-09 12694 7 205 0
2022-12-08 12687 11 205 0
2022-12-07 12676 10 205 0
2022-12-06 12666 11 205 0
2022-12-05 12655 11 205 0
2022-12-02 12644 8 205 1
2022-12-01 12636 4 204 0
2022-11-30 12632 9 204 1
2022-11-29 12623 12 203 0
2022-11-28 12611 6 203 0
2022-11-25 12605 6 203 0
2022-11-24 12599 7 203 0
2022-11-23 12592 12 203 0
2022-11-22 12580 15 203 0
2022-11-21 12565 7 203 0
2022-11-18 12558 1 203 0
2022-11-17 12557 4 203 0
2022-11-16 12553 10 203 0
2022-11-15 12543 11 203 0
2022-11-14 12532 15 203 0
2022-11-11 12517 7 203 2
2022-11-10 12510 10 201 0
2022-11-09 12500 7 201 0
2022-11-08 12493 17 201 0
2022-11-07 12476 9 201 1
2022-11-04 12467 11 200 0
2022-11-03 12456 16 200 1
2022-11-02 12440 17 199 0
2022-11-01 12423 19 199 0
2022-10-28 12404 17 199 0
2022-10-27 12387 10 199 0
2022-10-26 12377 12 199 0
2022-10-25 12365 24 199 0
2022-10-24 12341 43 199 0
2022-10-21 12298 20 199 0
2022-10-20 12278 25 199 0
2022-10-19 12253 32 199 1
2022-10-18 12221 31 198 0
2022-10-17 12190 41 198 1
2022-10-14 12149 35 197 1
2022-10-13 12114 28 196 0
2022-10-12 12086 41 196 0
2022-10-11 12045 62 196 0
2022-10-10 11983 73 196 0
2022-10-07 11910 43 196 0
2022-10-06 11867 57 196 0
2022-10-05 11810 64 196 0
2022-10-04 11746 47 196 2
2022-09-30 11699 25 194 1
2022-09-29 11674 26 193 0
2022-09-28 11648 19 193 0
2022-09-27 11629 27 193 1
2022-09-26 11602 28 192 1
2022-09-23 11574 11 191 0
2022-09-22 11563 21 191 0
2022-09-21 11542 23 191 0
2022-09-19 11519 7 191 0
2022-09-16 11512 6 191 0
2022-09-15 11506 10 191 0
2022-09-14 11496 18 191 0
2022-09-13 11478 11 191 0
2022-09-12 11467 14 191 1
2022-09-09 11453 8 190 0
2022-09-08 11445 5 190 0
2022-09-07 11440 5 190 0
2022-09-06 11435 20 190 1
2022-09-05 11415 8 189 0
2022-09-02 11407 5 189 0
2022-09-01 11402 5 189 0
2022-08-31 11397 8 189 0
2022-08-30 11389 14 189 0
2022-08-29 11375 17 189 1
2022-08-26 11358 10 188 0
2022-08-25 11348 12 188 0
2022-08-24 11336 10 188 0
2022-08-23 11326 14 188 0
2022-08-22 11312 15 188 0
2022-08-19 11297 7 188 0
2022-08-18 11290 9 188 0
2022-08-17 11281 13 188 0
2022-08-16 11268 10 188 0
2022-08-15 11258 11 188 0
2022-08-12 11247 5 188 0
2022-08-11 11242 9 188 1
2022-08-10 11233 10 187 0
2022-08-09 11223 20 187 0
2022-08-08 11203 11 187 0
2022-08-05 11192 10 187 0
2022-08-04 11182 9 187 0
2022-08-03 11173 15 187 0
2022-08-02 11158 28 187 0
2022-08-01 11130 24 187 1
2022-07-29 11106 10 186 0
2022-07-28 11096 17 186 0
2022-07-27 11079 19 186 0
2022-07-26 11060 34 186 0
2022-07-25 11026 41 186 1
2022-07-22 10985 24 185 0
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2022-07-20 10934 23 185 1
2022-07-19 10911 33 184 0
2022-07-18 10878 34 184 0
2022-07-15 10844 11 184 0
2022-07-14 10833 17 184 0
2022-07-13 10816 12 184 0
2022-07-12 10804 35 184 2
2022-07-11 10769 16 182 0
2022-07-08 10753 10 182 0
2022-07-07 10743 8 182 0
2022-07-06 10735 12 182 0
2022-07-05 10723 16 182 0
2022-07-04 10707 15 182 0
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2022-06-30 10677 10 182 0
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2022-04-11 9599 17 179 1
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2022-04-08 9527 28 177 0
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2022-04-06 9418 51 177 1
2022-04-05 9367 63 176 0
2022-04-04 9304 16 176 0
2022-04-02 9288 64 176 0
2022-04-01 9224 51 176 1
2022-03-31 9173 92 175 0
2022-03-30 9081 84 175 0
2022-03-29 8997 153 175 0
2022-03-28 8844 15 175 1
2022-03-26 8829 100 174 0
2022-03-25 8729 83 174 0
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2022-03-07 7072 7 170 0
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2022-03-05 7031 79 170 0
2022-03-04 6952 60 170 0
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2021-03-12 1457 22 91 1
2021-03-11 1435 25 90 1
2021-03-10 1410 10 89 0
2021-03-09 1400 14 89 0
2021-03-08 1386 3 89 0
2021-03-07 1383 19 89 0
2021-03-06 1364 3 89 1
2021-03-05 1361 13 88 0
2021-03-04 1348 7 88 0
2021-03-03 1341 6 88 0
2021-03-02 1335 16 88 0
2021-03-01 1319 9 88 0
2021-02-28 1310 1 88 0
2021-02-27 1309 1 88 0
2021-02-26 1308 11 88 1
2021-02-25 1297 7 87 0
2021-02-24 1290 6 87 0
2021-02-23 1284 10 87 0
2021-02-22 1274 7 87 1
2021-02-21 1267 2 86 0
2021-02-20 1265 2 86 0
2021-02-19 1263 7 86 0
2021-02-18 1256 6 86 0
2021-02-17 1250 3 86 0
2021-02-16 1247 8 86 1
2021-02-15 1239 15 85 0
2021-02-14 1224 1 85 0
2021-02-12 1223 9 85 1
2021-02-11 1214 10 84 0
2021-02-10 1204 5 84 0
2021-02-09 1199 7 84 0
2021-02-08 1192 7 84 0
2021-02-06 1185 2 84 0
2021-02-05 1183 8 84 0
2021-02-04 1175 12 84 2
2021-02-03 1163 17 82 0
2021-02-02 1146 12 82 0
2021-02-01 1134 15 82 0
2021-01-30 1119 5 82 0
2021-01-29 1114 11 82 0
2021-01-28 1103 14 82 1
2021-01-27 1089 17 81 2
2021-01-26 1072 9 79 2
2021-01-25 1063 12 77 2
2021-01-24 1051 2 75 0
2021-01-23 1049 1 75 0
2021-01-22 1048 10 75 0
2021-01-21 1038 9 75 0
2021-01-20 1029 17 75 1
2021-01-19 1012 6 74 0
2021-01-18 1006 8 74 0
2021-01-16 998 3 74 0
2021-01-15 995 25 74 4
2021-01-14 970 23 70 1
2021-01-13 947 10 69 0
2021-01-12 937 15 69 1
2021-01-11 922 6 68 1
2021-01-10 916 3 67 0
2021-01-09 913 2 67 0
2021-01-08 911 42 67 2
2021-01-07 869 36 65 5
2021-01-06 833 25 60 2
2021-01-05 808 49 58 1
2021-01-04 759 7 57 0
2021-01-03 752 8 57 1
2021-01-02 744 4 56 0
2021-01-01 740 5 56 1
2020-12-31 735 14 55 0
2020-12-30 721 25 55 4
2020-12-29 696 27 51 0
2020-12-28 669 23 51 4
2020-12-27 646 4 47 1
2020-12-25 642 7 46 3
2020-12-24 635 6 43 0
2020-12-23 629 41 43 2
2020-12-22 588 42 41 10
2020-12-21 546 23 31 2
2020-12-20 523 5 29 0
2020-12-19 518 13 29 0
2020-12-18 505 41 29 1
2020-12-17 464 13 28 0
2020-12-16 451 29 28 0
2020-12-15 422 24 28 0
2020-12-14 398 19 28 0
2020-12-13 379 6 28 0
2020-12-12 373 7 28 0
2020-12-11 366 32 28 9
2020-12-10 334 16 19 0
2020-12-09 318 53 19 4
2020-12-08 265 20 15 0
2020-12-07 245 9 15 0
2020-12-06 236 2 15 1
2020-12-05 234 6 14 2
2020-12-04 228 32 12 4
2020-12-03 196 13 8 1
2020-12-02 183 8 7 2
2020-12-01 175 6 5 0
2020-11-30 169 11 5 1
2020-11-29 158 2 4 0
2020-11-28 156 1 4 0
2020-11-27 155 6 4 0
2020-11-26 149 6 4 0
2020-11-25 143 7 4 0
2020-11-24 136 10 4 0
2020-11-23 126 6 4 0
2020-11-22 120 1 4 0
2020-11-20 119 1 4 0
2020-11-17 118 8 4 0
2020-11-16 110 3 4 0
2020-11-14 107 1 4 0
2020-11-13 106 5 4 0
2020-11-12 101 2 4 0
2020-11-11 99 2 4 0
2020-11-10 97 4 4 0
2020-11-09 93 5 4 1
2020-11-08 88 1 3 0
2020-11-07 87 3 3 0
2020-11-06 84 3 3 0
2020-11-05 81 7 3 0
2020-11-04 74 3 3 0
2020-11-03 71 1 3 1
2020-11-02 70 1 2 0
2020-10-31 69 2 2 0
2020-10-30 67 1 2 0
2020-10-29 66 5 2 0
2020-10-28 61 6 2 0
2020-10-27 55 3 2 0
2020-10-25 52 1 2 0
2020-10-23 51 3 2 0
2020-10-22 48 5 2 0
2020-10-21 43 1 2 0
2020-10-20 42 1 2 0
2020-10-19 41 1 2 0
2020-10-17 40 1 2 0
2020-10-14 39 2 2 0
2020-10-09 37 1 2 0
2020-10-07 36 2 2 0
2020-10-06 34 1 2 0
2020-09-12 33 2 2 0
2020-09-04 31 1 2 0
2020-08-29 30 1 2 0
2020-08-21 29 1 2 0
2020-08-20 28 2 2 0
2020-08-16 26 1 2 0
2020-08-14 25 1 2 0
2020-08-13 24 1 2 0
2020-08-12 23 1 2 0
2020-08-11 22 1 2 0
2020-07-02 21 1 2 0
2020-06-10 20 1 2 0
2020-05-27 19 1 2 0
2020-05-25 18 1 2 0
2020-05-23 17 1 2 0
2020-05-19 16 1 2 0
2020-05-15 15 1 2 0
2020-05-14 14 1 2 0
2020-04-17 13 1 2 0
2020-04-15 12 1 2 0
2020-04-06 11 1 2 0
2020-04-04 10 1 2 0
2020-04-03 9 1 2 0
2020-04-02 8 1 2 0
2020-03-30 7 1 2 0
2020-03-28 6 1 2 1
2020-03-24 5 1 1 1
2020-03-21 4 2 0 0
2020-03-20 2 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: 0:01:47.749501