Germany: LK Uelzen (Niedersachsen)¶

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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 13:30:41 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Uelzen", weeks=5);
2023-03-07T13:30:52.925742 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 76.4 LK Uelzen, 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 1 2 3 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 Uelzen cases daily growth factor Germany-LK Uelzen cases daily growth factor (rolling mean) Germany-LK Uelzen 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 Uelzen deaths daily growth factor Germany-LK Uelzen deaths daily growth factor (rolling mean) Germany-LK Uelzen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Uelzen doubling time cases (rolling mean) Germany-LK Uelzen doubling time deaths (rolling mean) 0.00 9.29 18.58 27.87 daily change Germany-LK Uelzen new cases (rolling 7d mean) Germany-LK Uelzen new cases 0.000 0.929 1.858 2.787 daily change Germany-LK Uelzen new deaths (rolling 7d mean) Germany-LK Uelzen new deaths 0.0 70.5 140.9 211.4 281.8 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Uelzen");
2023-03-07T13:31:07.135071 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) 76.4 LK Uelzen, 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 2 4 6 8 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 Uelzen cases daily growth factor Germany-LK Uelzen cases daily growth factor (rolling mean) Germany-LK Uelzen 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 Uelzen deaths daily growth factor Germany-LK Uelzen deaths daily growth factor (rolling mean) Germany-LK Uelzen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Uelzen doubling time cases (rolling mean) Germany-LK Uelzen doubling time deaths (rolling mean) 0.0 185.8 371.6 557.4 daily change Germany-LK Uelzen new cases (rolling 7d mean) Germany-LK Uelzen new cases 0.000 1.858 3.716 5.574 7.432 daily change Germany-LK Uelzen new deaths (rolling 7d mean) Germany-LK Uelzen new deaths 0.0 60.9 121.7 182.6 243.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Uelzen", dates="2020-03-15:");
2023-03-07T13:33:34.005289 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 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 Uelzen LK Uelzen 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 Uelzen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Uelzen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Uelzen")
print(f'Population of country="Germany", subregion="LK Uelzen": {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 Uelzen": 92894 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 44295 9 293 0
2023-03-03 44286 13 293 0
2023-03-02 44273 14 293 0
2023-03-01 44259 8 293 1
2023-02-28 44251 27 292 0
2023-02-27 44224 5 292 0
2023-02-24 44219 15 292 0
2023-02-23 44204 14 292 0
2023-02-22 44190 29 292 0
2023-02-21 44161 30 292 0
2023-02-20 44131 21 292 0
2023-02-17 44110 16 292 0
2023-02-16 44094 16 292 1
2023-02-15 44078 27 291 0
2023-02-14 44051 29 291 0
2023-02-13 44022 5 291 0
2023-02-10 44017 17 291 1
2023-02-09 44000 23 290 3
2023-02-08 43977 17 287 3
2023-02-07 43960 23 284 2
2023-02-06 43937 12 282 0
2023-02-03 43925 16 282 0
2023-02-02 43909 12 282 0
2023-02-01 43897 12 282 0
2023-01-31 43885 35 282 1
2023-01-30 43850 26 281 0
2023-01-27 43824 30 281 0
2023-01-26 43794 14 281 0
2023-01-25 43780 23 281 0
2023-01-24 43757 29 281 0
2023-01-23 43728 30 281 0
2023-01-20 43698 16 281 0
2023-01-19 43682 17 281 0
2023-01-18 43665 43 281 0
2023-01-17 43622 19 281 0
2023-01-16 43603 35 281 0
2023-01-13 43568 21 281 1
2023-01-12 43547 34 280 0
2023-01-11 43513 30 280 0
2023-01-10 43483 41 280 1
2023-01-09 43442 34 279 2
2023-01-06 43408 54 277 0
2023-01-05 43354 38 277 0
2023-01-04 43316 63 277 0
2023-01-03 43253 92 277 1
2023-01-02 43161 54 276 2
2022-12-30 43107 69 274 1
2022-12-29 43038 123 273 1
2022-12-28 42915 165 272 2
2022-12-27 42750 123 270 2
2022-12-23 42627 138 268 2
2022-12-22 42489 142 266 0
2022-12-21 42347 169 266 1
2022-12-20 42178 166 265 1
2022-12-19 42012 149 264 2
2022-12-16 41863 114 262 2
2022-12-15 41749 112 260 1
2022-12-14 41637 163 259 0
2022-12-13 41474 219 259 2
2022-12-12 41255 148 257 4
2022-12-09 41107 127 253 1
2022-12-08 40980 105 252 1
2022-12-07 40875 299 251 3
2022-12-06 40576 127 248 0
2022-12-05 40449 91 248 1
2022-12-02 40358 108 247 3
2022-12-01 40250 125 244 1
2022-11-30 40125 149 243 2
2022-11-29 39976 217 241 0
2022-11-28 39759 105 241 2
2022-11-25 39654 75 239 0
2022-11-24 39579 113 239 0
2022-11-23 39466 92 239 2
2022-11-22 39374 123 237 0
2022-11-21 39251 80 237 1
2022-11-18 39171 52 236 2
2022-11-17 39119 53 234 0
2022-11-16 39066 74 234 0
2022-11-15 38992 104 234 2
2022-11-14 38888 71 232 0
2022-11-11 38817 57 232 0
2022-11-10 38760 57 232 0
2022-11-09 38703 78 232 0
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2022-11-03 38357 72 232 1
2022-11-02 38285 147 231 0
2022-11-01 38138 89 231 1
2022-10-28 38049 78 230 1
2022-10-27 37971 84 229 0
2022-10-26 37887 95 229 1
2022-10-25 37792 158 228 0
2022-10-24 37634 117 228 1
2022-10-21 37517 110 227 0
2022-10-20 37407 107 227 0
2022-10-19 37300 177 227 0
2022-10-18 37123 198 227 1
2022-10-17 36925 151 226 1
2022-10-14 36774 148 225 0
2022-10-13 36626 104 225 1
2022-10-12 36522 167 224 1
2022-10-11 36355 232 223 0
2022-10-10 36123 154 223 1
2022-10-07 35969 152 222 0
2022-10-06 35817 239 222 0
2022-10-05 35578 161 222 1
2022-10-04 35417 193 221 0
2022-09-30 35224 88 221 0
2022-09-29 35136 101 221 0
2022-09-28 35035 113 221 1
2022-09-27 34922 143 220 0
2022-09-26 34779 101 220 0
2022-09-23 34678 89 220 0
2022-09-22 34589 77 220 0
2022-09-21 34512 93 220 0
2022-09-20 34419 119 220 1
2022-09-19 34300 61 219 1
2022-09-16 34239 78 218 0
2022-09-15 34161 65 218 0
2022-09-14 34096 82 218 0
2022-09-13 34014 113 218 0
2022-09-12 33901 59 218 1
2022-09-09 33842 60 217 0
2022-09-08 33782 65 217 0
2022-09-07 33717 61 217 0
2022-09-06 33656 85 217 0
2022-09-05 33571 69 217 0
2022-09-02 33502 51 217 0
2022-09-01 33451 34 217 0
2022-08-31 33417 70 217 1
2022-08-30 33347 76 216 0
2022-08-29 33271 60 216 0
2022-08-26 33211 69 216 2
2022-08-25 33142 59 214 0
2022-08-24 33083 73 214 1
2022-08-23 33010 109 213 0
2022-08-22 32901 56 213 0
2022-08-19 32845 55 213 0
2022-08-18 32790 69 213 0
2022-08-17 32721 96 213 0
2022-08-16 32625 128 213 0
2022-08-15 32497 98 213 0
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2022-08-11 32329 103 213 0
2022-08-10 32226 59 213 0
2022-08-09 32167 98 213 0
2022-08-08 32069 92 213 0
2022-08-05 31977 77 213 0
2022-08-04 31900 56 213 0
2022-08-03 31844 103 213 0
2022-08-02 31741 126 213 0
2022-08-01 31615 95 213 0
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2022-05-03 22036 226 192 0
2022-05-02 21810 173 192 1
2022-04-29 21637 124 191 0
2022-04-28 21513 254 191 1
2022-04-27 21259 360 190 0
2022-04-26 20899 247 190 1
2022-04-25 20652 295 189 1
2022-04-22 20357 318 188 1
2022-04-21 20039 270 187 0
2022-04-20 19769 359 187 0
2022-04-19 19410 442 187 5
2022-04-14 18968 190 182 2
2022-04-13 18778 329 180 1
2022-04-12 18449 626 179 3
2022-04-11 17823 350 176 0
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2022-04-07 17279 458 175 1
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2022-04-05 16332 719 171 5
2022-04-04 15613 377 166 1
2022-04-01 15236 256 165 2
2022-03-31 14980 444 163 0
2022-03-30 14536 450 163 1
2022-03-29 14086 383 162 0
2022-03-28 13703 368 162 2
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2021-04-02 1734 20 111 1
2021-04-01 1714 8 110 0
2021-03-31 1706 16 110 0
2021-03-30 1690 30 110 1
2021-03-29 1660 1 109 1
2021-03-27 1659 14 108 0
2021-03-26 1645 31 108 0
2021-03-25 1614 5 108 0
2021-03-24 1609 10 108 0
2021-03-23 1599 16 108 0
2021-03-21 1583 4 108 0
2021-03-20 1579 4 108 0
2021-03-19 1575 5 108 0
2021-03-18 1570 4 108 0
2021-03-17 1566 10 108 0
2021-03-16 1556 6 108 0
2021-03-15 1550 1 108 0
2021-03-14 1549 2 108 0
2021-03-13 1547 4 108 0
2021-03-12 1543 22 108 0
2021-03-11 1521 3 108 0
2021-03-10 1518 11 108 0
2021-03-09 1507 15 108 0
2021-03-08 1492 2 108 0
2021-03-06 1490 4 108 0
2021-03-05 1486 8 108 0
2021-03-04 1478 1 108 0
2021-03-03 1477 3 108 0
2021-03-02 1474 16 108 0
2021-03-01 1458 6 108 0
2021-02-28 1452 1 108 0
2021-02-27 1451 7 108 0
2021-02-26 1444 8 108 0
2021-02-25 1436 9 108 0
2021-02-24 1427 18 108 0
2021-02-23 1409 25 108 0
2021-02-22 1384 1 108 0
2021-02-21 1383 1 108 0
2021-02-20 1382 11 108 0
2021-02-19 1371 16 108 1
2021-02-18 1355 7 107 0
2021-02-17 1348 26 107 3
2021-02-16 1322 19 104 0
2021-02-14 1303 2 104 0
2021-02-13 1301 7 104 1
2021-02-12 1294 31 103 1
2021-02-11 1263 12 102 4
2021-02-10 1251 29 98 3
2021-02-09 1222 25 95 4
2021-02-08 1197 1 91 0
2021-02-07 1196 3 91 0
2021-02-06 1193 20 91 1
2021-02-05 1173 24 90 1
2021-02-04 1149 18 89 4
2021-02-03 1131 37 85 8
2021-02-02 1094 50 77 4
2021-02-01 1044 8 73 1
2021-01-31 1036 5 72 1
2021-01-30 1031 18 71 1
2021-01-29 1013 53 70 6
2021-01-28 960 37 64 5
2021-01-27 923 45 59 9
2021-01-26 878 58 50 7
2021-01-25 820 11 43 4
2021-01-24 809 4 39 2
2021-01-23 805 21 37 3
2021-01-22 784 26 34 3
2021-01-21 758 27 31 4
2021-01-20 731 33 27 6
2021-01-19 698 25 21 2
2021-01-18 673 2 19 0
2021-01-17 671 3 19 1
2021-01-16 668 10 18 1
2021-01-15 658 32 17 2
2021-01-14 626 10 15 0
2021-01-13 616 19 15 1
2021-01-12 597 15 14 0
2021-01-11 582 5 14 0
2021-01-10 577 2 14 0
2021-01-09 575 8 14 0
2021-01-08 567 11 14 0
2021-01-07 556 10 14 3
2021-01-06 546 7 11 1
2021-01-05 539 13 10 1
2021-01-04 526 1 9 0
2021-01-03 525 2 9 0
2021-01-01 523 8 9 0
2020-12-31 515 6 9 0
2020-12-30 509 13 9 0
2020-12-29 496 9 9 0
2020-12-28 487 2 9 0
2020-12-26 485 1 9 0
2020-12-25 484 8 9 0
2020-12-24 476 11 9 1
2020-12-23 465 8 8 0
2020-12-22 457 9 8 0
2020-12-21 448 3 8 0
2020-12-20 445 1 8 0
2020-12-19 444 3 8 0
2020-12-18 441 9 8 0
2020-12-17 432 2 8 0
2020-12-16 430 2 8 0
2020-12-15 428 5 8 0
2020-12-12 423 8 8 0
2020-12-11 415 7 8 0
2020-12-10 408 4 8 0
2020-12-09 404 6 8 0
2020-12-08 398 1 8 0
2020-12-05 397 7 8 0
2020-12-04 390 3 8 0
2020-12-03 387 3 8 0
2020-12-02 384 4 8 0
2020-12-01 380 11 8 0
2020-11-29 369 1 8 0
2020-11-28 368 2 8 0
2020-11-27 366 9 8 1
2020-11-26 357 1 7 0
2020-11-25 356 7 7 0
2020-11-24 349 3 7 0
2020-11-22 346 2 7 0
2020-11-21 344 8 7 0
2020-11-20 336 17 7 0
2020-11-19 319 15 7 1
2020-11-18 304 56 6 4
2020-11-17 248 12 2 0
2020-11-16 236 3 2 1
2020-11-15 233 1 1 1
2020-11-14 232 4 0 0
2020-11-13 228 17 0 0
2020-11-12 211 6 0 0
2020-11-11 205 3 0 0
2020-11-10 202 16 0 0
2020-11-07 186 5 0 0
2020-11-06 181 22 0 0
2020-11-05 159 1 0 0
2020-11-04 158 12 0 0
2020-11-03 146 2 0 0
2020-11-02 144 1 0 0
2020-10-31 143 4 0 0
2020-10-30 139 5 0 0
2020-10-29 134 11 0 0
2020-10-28 123 2 0 0
2020-10-27 121 5 0 0
2020-10-23 116 4 0 0
2020-10-20 112 4 0 0
2020-10-16 108 1 0 0
2020-10-14 107 2 0 0
2020-10-10 105 1 0 0
2020-10-09 104 4 0 0
2020-10-07 100 1 0 0
2020-10-05 99 1 0 0
2020-10-01 98 1 0 0
2020-09-30 97 1 0 0
2020-09-22 96 1 0 0
2020-09-18 95 1 0 0
2020-09-16 94 1 0 0
2020-09-14 93 3 0 0
2020-09-10 90 1 0 0
2020-09-02 89 1 0 0
2020-09-01 88 3 0 0
2020-08-31 85 1 0 0
2020-08-28 84 1 0 0
2020-08-27 83 2 0 0
2020-08-24 81 2 0 0
2020-08-20 79 1 0 0
2020-08-19 78 1 0 0
2020-08-18 77 4 0 0
2020-08-15 73 1 0 0
2020-07-31 72 5 0 0
2020-07-30 67 2 0 0
2020-07-29 65 1 0 0
2020-07-28 64 1 0 0
2020-07-26 63 3 0 0
2020-07-22 60 1 0 0
2020-07-21 59 2 0 0
2020-07-19 57 11 0 0
2020-07-18 46 1 0 0
2020-07-17 45 1 0 0
2020-06-08 44 1 0 0
2020-05-10 43 1 0 0
2020-04-24 42 1 0 0
2020-04-22 41 1 0 0
2020-04-17 40 1 0 0
2020-04-16 39 5 0 0
2020-04-12 34 1 0 0
2020-04-09 33 2 0 0
2020-04-08 31 2 0 0
2020-04-04 29 1 0 0
2020-04-03 28 2 0 0
2020-04-01 26 1 0 0
2020-03-31 25 4 0 0
2020-03-30 21 3 0 0
2020-03-27 18 4 0 0
2020-03-25 14 3 0 0
2020-03-23 11 1 0 0
2020-03-22 10 3 0 0
2020-03-20 7 2 0 0
2020-03-19 5 2 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: 0:03:00.222018