Germany: LK Tirschenreuth (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 11:44:16 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Tirschenreuth", weeks=5);
2023-03-07T11:44:30.340160 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 7-day incidence rate (per 100K people) 118.6 LK Tirschenreuth, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Tirschenreuth cases daily growth factor Germany-LK Tirschenreuth cases daily growth factor (rolling mean) Germany-LK Tirschenreuth 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-LK Tirschenreuth deaths daily growth factor Germany-LK Tirschenreuth deaths daily growth factor (rolling mean) Germany-LK Tirschenreuth estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 cases doubling time [days] Germany-LK Tirschenreuth doubling time cases (rolling mean) 0.00 14.33 28.66 42.99 daily change Germany-LK Tirschenreuth new cases (rolling 7d mean) Germany-LK Tirschenreuth new cases 0.000 0.358 0.716 daily change Germany-LK Tirschenreuth new deaths (rolling 7d mean) Germany-LK Tirschenreuth new deaths 0.00 0.34 0.68
In [4]:
overview(country="Germany", subregion="LK Tirschenreuth");
2023-03-07T11:44:46.107280 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) 118.6 LK Tirschenreuth, 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 15 20 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Tirschenreuth cases daily growth factor Germany-LK Tirschenreuth cases daily growth factor (rolling mean) Germany-LK Tirschenreuth 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-LK Tirschenreuth deaths daily growth factor Germany-LK Tirschenreuth deaths daily growth factor (rolling mean) Germany-LK Tirschenreuth 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 6000 8000 cases doubling time [days] Germany-LK Tirschenreuth doubling time cases (rolling mean) Germany-LK Tirschenreuth doubling time deaths (rolling mean) 0.0 143.3 286.6 429.9 daily change Germany-LK Tirschenreuth new cases (rolling 7d mean) Germany-LK Tirschenreuth new cases 0.00 3.58 7.16 10.75 14.33 daily change Germany-LK Tirschenreuth new deaths (rolling 7d mean) Germany-LK Tirschenreuth new deaths 0.0 79.3 158.6 237.9 317.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Tirschenreuth", dates="2020-03-15:");
2023-03-07T11:46:54.039602 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 Tirschenreuth LK Tirschenreuth 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 LK Tirschenreuth Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Tirschenreuth")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Tirschenreuth")
print(f'Population of country="Germany", subregion="LK Tirschenreuth": {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 Tirschenreuth": 71648 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 40728 10 334 0
2023-03-03 40718 12 334 0
2023-03-02 40706 12 334 0
2023-03-01 40694 20 334 0
2023-02-28 40674 31 334 0
2023-02-27 40643 44 334 0
2023-02-24 40599 33 334 0
2023-02-23 40566 30 334 0
2023-02-22 40536 43 334 1
2023-02-21 40493 31 333 0
2023-02-20 40462 40 333 0
2023-02-17 40422 27 333 0
2023-02-16 40395 30 333 0
2023-02-15 40365 24 333 0
2023-02-14 40341 49 333 0
2023-02-13 40292 45 333 0
2023-02-10 40247 35 333 0
2023-02-09 40212 26 333 0
2023-02-08 40186 26 333 0
2023-02-07 40160 56 333 0
2023-02-06 40104 36 333 0
2023-02-03 40068 16 333 0
2023-02-02 40052 26 333 0
2023-02-01 40026 17 333 1
2023-01-31 40009 32 332 1
2023-01-30 39977 37 331 0
2023-01-27 39940 13 331 0
2023-01-26 39927 28 331 0
2023-01-25 39899 14 331 0
2023-01-24 39885 24 331 0
2023-01-23 39861 15 331 0
2023-01-20 39846 15 331 0
2023-01-19 39831 17 331 0
2023-01-18 39814 15 331 0
2023-01-17 39799 15 331 0
2023-01-16 39784 26 331 0
2023-01-13 39758 13 331 0
2023-01-12 39745 13 331 0
2023-01-11 39732 19 331 1
2023-01-10 39713 20 330 0
2023-01-09 39693 30 330 0
2023-01-05 39663 16 330 0
2023-01-04 39647 17 330 0
2023-01-03 39630 15 330 0
2023-01-02 39615 25 330 0
2022-12-30 39590 12 330 0
2022-12-29 39578 19 330 0
2022-12-28 39559 20 330 0
2022-12-27 39539 28 330 0
2022-12-23 39511 15 330 0
2022-12-22 39496 15 330 0
2022-12-21 39481 13 330 0
2022-12-20 39468 19 330 0
2022-12-19 39449 25 330 0
2022-12-16 39424 17 330 0
2022-12-15 39407 16 330 0
2022-12-14 39391 12 330 0
2022-12-13 39379 23 330 0
2022-12-12 39356 30 330 0
2022-12-09 39326 9 330 0
2022-12-08 39317 13 330 0
2022-12-07 39304 8 330 0
2022-12-06 39296 20 330 1
2022-12-05 39276 22 329 0
2022-12-02 39254 18 329 0
2022-12-01 39236 7 329 0
2022-11-30 39229 12 329 0
2022-11-29 39217 24 329 1
2022-11-28 39193 22 328 0
2022-11-25 39171 8 328 0
2022-11-24 39163 20 328 0
2022-11-23 39143 14 328 0
2022-11-22 39129 9 328 0
2022-11-21 39120 16 328 0
2022-11-18 39104 22 328 0
2022-11-17 39082 10 328 0
2022-11-16 39072 14 328 0
2022-11-15 39058 41 328 0
2022-11-14 39017 26 328 0
2022-11-11 38991 18 328 0
2022-11-10 38973 27 328 0
2022-11-09 38946 24 328 0
2022-11-08 38922 34 328 0
2022-11-07 38888 67 328 0
2022-11-04 38821 34 328 0
2022-11-03 38787 43 328 0
2022-11-02 38744 64 328 0
2022-10-31 38680 61 328 0
2022-10-28 38619 86 328 0
2022-10-27 38533 22 328 0
2022-10-26 38511 35 328 0
2022-10-25 38476 94 328 0
2022-10-24 38382 154 328 1
2022-10-21 38228 83 327 1
2022-10-20 38145 107 326 0
2022-10-19 38038 123 326 1
2022-10-18 37915 197 325 0
2022-10-17 37718 241 325 1
2022-10-14 37477 116 324 0
2022-10-13 37361 144 324 0
2022-10-12 37217 164 324 0
2022-10-11 37053 198 324 1
2022-10-10 36855 501 323 2
2022-10-07 36354 301 321 0
2022-10-06 36053 104 321 0
2022-10-05 35949 25 321 0
2022-10-04 35924 285 321 1
2022-09-30 35639 115 320 0
2022-09-29 35524 115 320 0
2022-09-28 35409 156 320 0
2022-09-27 35253 132 320 0
2022-09-26 35121 156 320 1
2022-09-23 34965 76 319 0
2022-09-22 34889 61 319 0
2022-09-21 34828 63 319 0
2022-09-20 34765 78 319 0
2022-09-19 34687 80 319 0
2022-09-18 34607 1 319 0
2022-09-16 34606 41 319 0
2022-09-15 34565 46 319 0
2022-09-14 34519 47 319 0
2022-09-13 34472 65 319 0
2022-09-12 34407 59 319 0
2022-09-09 34348 32 319 0
2022-09-08 34316 42 319 0
2022-09-07 34274 47 319 0
2022-09-06 34227 52 319 0
2022-09-05 34175 61 319 0
2022-09-04 34114 1 319 0
2022-09-02 34113 41 319 0
2022-09-01 34072 30 319 0
2022-08-31 34042 37 319 0
2022-08-30 34005 43 319 0
2022-08-29 33962 41 319 0
2022-08-26 33921 28 319 0
2022-08-25 33893 29 319 0
2022-08-24 33864 24 319 0
2022-08-23 33840 43 319 0
2022-08-22 33797 44 319 0
2022-08-19 33753 30 319 0
2022-08-18 33723 28 319 0
2022-08-17 33695 57 319 0
2022-08-16 33638 71 319 0
2022-08-12 33567 39 319 0
2022-08-11 33528 41 319 0
2022-08-10 33487 57 319 0
2022-08-09 33430 66 319 0
2022-08-08 33364 104 319 0
2022-08-05 33260 43 319 0
2022-08-04 33217 53 319 0
2022-08-03 33164 81 319 0
2022-08-02 33083 124 319 0
2022-08-01 32959 147 319 0
2022-07-29 32812 109 319 0
2022-07-28 32703 110 319 0
2022-07-27 32593 119 319 0
2022-07-26 32474 170 319 0
2022-07-25 32304 300 319 2
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2022-07-18 31360 286 317 1
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2021-01-03 2665 20 198 1
2021-01-02 2645 4 197 0
2021-01-01 2641 27 197 1
2020-12-31 2614 47 196 4
2020-12-30 2567 69 192 2
2020-12-29 2498 39 190 0
2020-12-28 2459 4 190 0
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2020-12-26 2417 10 189 0
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2020-12-24 2371 21 188 0
2020-12-23 2350 29 188 1
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2020-12-21 2262 9 187 1
2020-12-20 2253 41 186 0
2020-12-19 2212 5 186 0
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2020-12-17 2184 33 186 2
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2020-12-15 2130 34 182 2
2020-12-14 2096 8 180 2
2020-12-13 2088 22 178 0
2020-12-12 2066 14 178 0
2020-12-11 2052 32 178 1
2020-12-10 2020 33 177 2
2020-12-09 1987 17 175 3
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2020-12-06 1944 10 169 1
2020-12-05 1934 17 168 0
2020-12-04 1917 14 168 0
2020-12-03 1903 28 168 1
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2020-12-01 1857 22 166 2
2020-11-30 1835 4 164 1
2020-11-29 1831 13 163 4
2020-11-28 1818 10 159 0
2020-11-27 1808 14 159 2
2020-11-26 1794 11 157 0
2020-11-25 1783 23 157 1
2020-11-24 1760 12 156 0
2020-11-23 1748 15 156 2
2020-11-22 1733 9 154 2
2020-11-21 1724 10 152 0
2020-11-20 1714 12 152 1
2020-11-19 1702 12 151 0
2020-11-18 1690 29 151 1
2020-11-17 1661 16 150 0
2020-11-16 1645 21 150 3
2020-11-15 1624 2 147 0
2020-11-14 1622 10 147 0
2020-11-13 1612 9 147 0
2020-11-12 1603 21 147 1
2020-11-11 1582 12 146 1
2020-11-10 1570 21 145 0
2020-11-09 1549 9 145 0
2020-11-08 1540 26 145 0
2020-11-07 1514 13 145 0
2020-11-06 1501 32 145 0
2020-11-05 1469 20 145 2
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2020-11-02 1411 6 142 0
2020-11-01 1405 7 142 0
2020-10-31 1398 7 142 0
2020-10-30 1391 5 142 0
2020-10-29 1386 11 142 0
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2020-09-26 1203 4 140 0
2020-09-25 1199 3 140 0
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2020-07-05 1137 1 139 0
2020-06-16 1136 1 139 0
2020-06-09 1135 2 139 0
2020-05-26 1133 1 139 0
2020-05-25 1132 1 139 0
2020-05-22 1131 1 139 0
2020-05-20 1130 2 139 0
2020-05-19 1128 5 139 0
2020-05-18 1123 2 139 0
2020-05-15 1121 2 139 1
2020-05-12 1119 3 138 0
2020-05-11 1116 1 138 1
2020-05-08 1115 1 137 1
2020-05-06 1114 1 136 0
2020-05-05 1113 1 136 0
2020-05-04 1112 1 136 0
2020-05-02 1111 4 136 0
2020-04-30 1107 1 136 0
2020-04-29 1106 5 136 0
2020-04-28 1101 5 136 1
2020-04-27 1096 5 135 0
2020-04-25 1091 1 135 0
2020-04-24 1090 8 135 1
2020-04-23 1082 5 134 2
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2020-04-21 1072 4 131 1
2020-04-20 1068 6 130 1
2020-04-19 1062 5 129 1
2020-04-18 1057 14 128 2
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2020-04-16 1032 13 121 3
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2020-04-14 999 16 118 2
2020-04-13 983 30 116 6
2020-04-11 953 11 110 1
2020-04-10 942 15 109 0
2020-04-09 927 27 109 2
2020-04-08 900 25 107 4
2020-04-07 875 59 103 4
2020-04-06 816 64 99 17
2020-04-04 752 33 82 3
2020-04-03 719 54 79 5
2020-04-02 665 64 74 6
2020-04-01 601 103 68 6
2020-03-31 498 31 62 2
2020-03-30 467 54 60 11
2020-03-29 413 18 49 2
2020-03-28 395 55 47 6
2020-03-27 340 88 41 6
2020-03-26 252 13 35 0
2020-03-25 239 30 35 3
2020-03-24 209 30 32 4
2020-03-23 179 31 28 7
2020-03-22 148 11 21 3
2020-03-21 137 66 18 8
2020-03-20 71 9 10 4
2020-03-19 62 16 6 0
2020-03-18 46 4 6 1
2020-03-17 42 12 5 1
2020-03-16 30 12 4 2
2020-03-14 18 8 2 1
2020-03-13 10 4 1 0
2020-03-12 6 3 1 0
2020-03-11 3 2 1 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: 0:03:06.389769