Germany: LK Stendal (Sachsen-Anhalt)¶

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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 15:45:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Stendal", weeks=5);
2023-03-07T15:46:08.582918 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 20 20 40 40 60 60 80 80 7-day incidence rate (per 100K people) 41.0 LK Stendal, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Stendal cases daily growth factor Germany-LK Stendal cases daily growth factor (rolling mean) Germany-LK Stendal 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 Stendal deaths daily growth factor Germany-LK Stendal deaths daily growth factor (rolling mean) Germany-LK Stendal 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-LK Stendal doubling time cases (rolling mean) 0.00 10.97 21.95 daily change Germany-LK Stendal new cases (rolling 7d mean) Germany-LK Stendal new cases 0.000 0.219 0.439 0.658 0.878 daily change Germany-LK Stendal new deaths (rolling 7d mean) Germany-LK Stendal new deaths 0.000 0.317 0.635 0.952
In [4]:
overview(country="Germany", subregion="LK Stendal");
2023-03-07T15:46:23.512166 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) 41.0 LK Stendal, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Stendal cases daily growth factor Germany-LK Stendal cases daily growth factor (rolling mean) Germany-LK Stendal 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 Stendal deaths daily growth factor Germany-LK Stendal deaths daily growth factor (rolling mean) Germany-LK Stendal 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-LK Stendal doubling time cases (rolling mean) Germany-LK Stendal doubling time deaths (rolling mean) 0.0 219.5 439.0 658.5 daily change Germany-LK Stendal new cases (rolling 7d mean) Germany-LK Stendal new cases 0.000 2.195 4.390 6.585 daily change Germany-LK Stendal new deaths (rolling 7d mean) Germany-LK Stendal new deaths 0.0 86.4 172.8 259.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Stendal", dates="2020-03-15:");
2023-03-07T15:49:04.130862 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 Stendal LK Stendal 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 Stendal Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Stendal")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Stendal")
print(f'Population of country="Germany", subregion="LK Stendal": {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 Stendal": 109746 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 45616 12 317 0
2023-03-03 45604 2 317 0
2023-03-02 45602 14 317 0
2023-03-01 45588 2 317 0
2023-02-28 45586 15 317 0
2023-02-27 45571 12 317 0
2023-02-24 45559 3 317 0
2023-02-23 45556 12 317 0
2023-02-22 45544 11 317 1
2023-02-21 45533 14 316 0
2023-02-20 45519 17 316 0
2023-02-17 45502 11 316 0
2023-02-16 45491 10 316 0
2023-02-15 45481 14 316 0
2023-02-14 45467 7 316 0
2023-02-13 45460 2 316 0
2023-02-10 45458 5 316 0
2023-02-09 45453 4 316 0
2023-02-08 45449 2 316 0
2023-02-07 45447 9 316 0
2023-02-06 45438 13 316 0
2023-02-02 45425 20 316 0
2023-02-01 45405 16 316 0
2023-01-31 45389 28 316 0
2023-01-30 45361 25 316 0
2023-01-27 45336 5 316 0
2023-01-26 45331 14 316 0
2023-01-25 45317 5 316 2
2023-01-24 45312 26 314 0
2023-01-23 45286 15 314 0
2023-01-20 45271 9 314 0
2023-01-19 45262 6 314 1
2023-01-18 45256 9 313 0
2023-01-17 45247 7 313 0
2023-01-16 45240 11 313 0
2023-01-13 45229 6 313 0
2023-01-12 45223 18 313 0
2023-01-11 45205 13 313 0
2023-01-10 45192 15 313 0
2023-01-09 45177 22 313 0
2023-01-05 45155 18 313 0
2023-01-04 45137 18 313 0
2023-01-03 45119 49 313 0
2023-01-02 45070 34 313 0
2022-12-30 45036 26 313 0
2022-12-29 45010 33 313 0
2022-12-28 44977 37 313 0
2022-12-27 44940 56 313 1
2022-12-23 44884 22 312 0
2022-12-22 44862 38 312 0
2022-12-21 44824 31 312 0
2022-12-20 44793 58 312 0
2022-12-19 44735 59 312 0
2022-12-16 44676 32 312 0
2022-12-15 44644 39 312 0
2022-12-14 44605 51 312 0
2022-12-13 44554 90 312 0
2022-12-12 44464 53 312 0
2022-12-09 44411 46 312 1
2022-12-08 44365 40 311 0
2022-12-07 44325 53 311 0
2022-12-06 44272 93 311 0
2022-12-05 44179 73 311 1
2022-12-02 44106 63 310 0
2022-12-01 44043 27 310 0
2022-11-30 44016 70 310 0
2022-11-29 43946 102 310 0
2022-11-28 43844 57 310 0
2022-11-25 43787 22 310 0
2022-11-24 43765 67 310 0
2022-11-23 43698 53 310 0
2022-11-22 43645 59 310 0
2022-11-21 43586 53 310 0
2022-11-18 43533 28 310 0
2022-11-17 43505 33 310 0
2022-11-16 43472 21 310 0
2022-11-15 43451 62 310 0
2022-11-14 43389 50 310 0
2022-11-11 43339 21 310 0
2022-11-10 43318 54 310 0
2022-11-09 43264 47 310 0
2022-11-08 43217 86 310 0
2022-11-07 43131 55 310 0
2022-11-05 43076 1 310 0
2022-11-04 43075 50 310 0
2022-11-03 43025 59 310 0
2022-11-02 42966 115 310 0
2022-11-01 42851 101 310 0
2022-10-28 42750 59 310 1
2022-10-27 42691 75 309 1
2022-10-26 42616 80 308 0
2022-10-25 42536 167 308 0
2022-10-24 42369 115 308 0
2022-10-21 42254 78 308 0
2022-10-20 42176 96 308 0
2022-10-19 42080 138 308 0
2022-10-18 41942 274 308 0
2022-10-17 41668 135 308 0
2022-10-14 41533 106 308 0
2022-10-13 41427 125 308 1
2022-10-12 41302 139 307 0
2022-10-11 41163 263 307 0
2022-10-10 40900 150 307 0
2022-10-07 40750 117 307 0
2022-10-06 40633 282 307 1
2022-10-05 40351 145 306 0
2022-10-04 40206 127 306 0
2022-09-30 40079 97 306 0
2022-09-29 39982 81 306 0
2022-09-28 39901 132 306 0
2022-09-27 39769 125 306 0
2022-09-26 39644 76 306 0
2022-09-23 39568 44 306 0
2022-09-22 39524 71 306 0
2022-09-21 39453 77 306 0
2022-09-20 39376 113 306 0
2022-09-19 39263 43 306 0
2022-09-16 39220 52 306 0
2022-09-15 39168 52 306 0
2022-09-14 39116 60 306 0
2022-09-13 39056 80 306 0
2022-09-12 38976 50 306 0
2022-09-09 38926 37 306 0
2022-09-08 38889 19 306 0
2022-09-07 38870 31 306 0
2022-09-06 38839 64 306 0
2022-09-05 38775 37 306 0
2022-09-02 38738 18 306 0
2022-09-01 38720 29 306 0
2022-08-31 38691 27 306 0
2022-08-30 38664 60 306 0
2022-08-29 38604 45 306 1
2022-08-26 38559 20 305 0
2022-08-25 38539 20 305 0
2022-08-24 38519 41 305 0
2022-08-23 38478 49 305 0
2022-08-22 38429 43 305 0
2022-08-19 38386 33 305 0
2022-08-18 38353 41 305 1
2022-08-17 38312 47 304 0
2022-08-16 38265 66 304 0
2022-08-15 38199 48 304 0
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2022-08-09 37999 90 304 1
2022-08-08 37909 74 303 0
2022-08-05 37835 44 303 0
2022-08-04 37791 34 303 0
2022-08-03 37757 59 303 0
2022-08-02 37698 86 303 0
2022-08-01 37612 78 303 1
2022-07-30 37534 1 302 0
2022-07-29 37533 55 302 1
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2022-07-27 37398 102 301 1
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2021-05-12 4581 32 217 1
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2021-05-10 4510 12 215 1
2021-05-09 4498 4 214 0
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2021-04-30 4291 30 206 0
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2021-04-28 4233 47 206 1
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2021-04-26 4114 12 204 0
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2021-04-24 4089 11 203 1
2021-04-23 4078 50 202 2
2021-04-22 4028 36 200 1
2021-04-21 3992 59 199 0
2021-04-20 3933 54 199 4
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2021-04-13 3684 62 190 0
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2021-04-02 3270 70 183 2
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2021-03-20 2819 16 179 0
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2021-03-15 2697 25 177 0
2021-03-13 2672 7 177 0
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2021-03-06 2582 14 172 0
2021-03-05 2568 12 172 0
2021-03-04 2556 28 172 0
2021-03-03 2528 28 172 2
2021-03-02 2500 8 170 0
2021-03-01 2492 3 170 1
2021-02-28 2489 6 169 0
2021-02-27 2483 10 169 0
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2021-02-24 2440 24 169 0
2021-02-23 2416 8 169 0
2021-02-21 2408 5 169 0
2021-02-20 2403 10 169 1
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2021-02-18 2385 9 168 0
2021-02-17 2376 6 168 0
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2021-02-12 2350 1 167 0
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2021-02-10 2342 12 167 1
2021-02-09 2330 4 166 1
2021-02-08 2326 3 165 0
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2021-02-06 2321 6 165 0
2021-02-05 2315 10 165 1
2021-02-04 2305 11 164 1
2021-02-03 2294 11 163 0
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2021-02-01 2276 2 163 0
2021-01-31 2274 10 163 1
2021-01-30 2264 9 162 0
2021-01-29 2255 12 162 0
2021-01-28 2243 17 162 0
2021-01-27 2226 16 162 0
2021-01-26 2210 7 162 0
2021-01-25 2203 2 162 0
2021-01-24 2201 15 162 0
2021-01-23 2186 16 162 0
2021-01-22 2170 28 162 2
2021-01-21 2142 24 160 2
2021-01-20 2118 15 158 0
2021-01-19 2103 25 158 0
2021-01-18 2078 8 158 0
2021-01-17 2070 8 158 0
2021-01-16 2062 16 158 1
2021-01-15 2046 21 157 5
2021-01-14 2025 32 152 2
2021-01-13 1993 12 150 1
2021-01-12 1981 52 149 4
2021-01-11 1929 9 145 2
2021-01-10 1920 60 143 7
2021-01-09 1860 4 136 1
2021-01-08 1856 42 135 4
2021-01-07 1814 51 131 1
2021-01-06 1763 35 130 4
2021-01-05 1728 21 126 4
2021-01-04 1707 17 122 3
2021-01-03 1690 47 119 6
2021-01-02 1643 28 113 0
2021-01-01 1615 51 113 0
2020-12-31 1564 35 113 1
2020-12-30 1529 66 112 3
2020-12-29 1463 32 109 2
2020-12-28 1431 9 107 4
2020-12-27 1422 22 103 6
2020-12-26 1400 38 97 2
2020-12-25 1362 28 95 6
2020-12-24 1334 34 89 1
2020-12-23 1300 76 88 5
2020-12-22 1224 71 83 2
2020-12-21 1153 22 81 6
2020-12-20 1131 53 75 5
2020-12-19 1078 18 70 2
2020-12-18 1060 63 68 7
2020-12-17 997 52 61 6
2020-12-16 945 65 55 5
2020-12-15 880 37 50 0
2020-12-14 843 28 50 3
2020-12-13 815 22 47 2
2020-12-12 793 30 45 3
2020-12-11 763 33 42 4
2020-12-10 730 26 38 0
2020-12-09 704 59 38 3
2020-12-08 645 39 35 1
2020-12-07 606 14 34 6
2020-12-06 592 26 28 5
2020-12-05 566 12 23 1
2020-12-04 554 36 22 4
2020-12-03 518 25 18 3
2020-12-02 493 24 15 0
2020-12-01 469 15 15 0
2020-11-30 454 6 15 0
2020-11-29 448 14 15 1
2020-11-28 434 19 14 3
2020-11-27 415 4 11 0
2020-11-26 411 12 11 0
2020-11-25 399 18 11 0
2020-11-24 381 16 11 1
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2020-11-22 364 3 10 0
2020-11-21 361 16 10 1
2020-11-20 345 9 9 0
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2020-11-18 331 16 9 0
2020-11-17 315 6 9 0
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2020-11-14 305 2 9 0
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2020-11-10 284 5 9 0
2020-11-08 279 5 9 0
2020-11-07 274 7 9 0
2020-11-06 267 9 9 0
2020-11-05 258 18 9 0
2020-11-04 240 1 9 0
2020-11-03 239 2 9 0
2020-11-02 237 11 9 0
2020-11-01 226 19 9 0
2020-10-31 207 7 9 0
2020-10-30 200 2 9 0
2020-10-29 198 12 9 1
2020-10-28 186 20 8 0
2020-10-27 166 4 8 0
2020-10-26 162 2 8 0
2020-10-24 160 3 8 0
2020-10-23 157 9 8 0
2020-10-21 148 3 8 2
2020-10-18 145 1 6 0
2020-10-17 144 1 6 0
2020-10-16 143 1 6 0
2020-10-15 142 2 6 0
2020-10-14 140 8 6 0
2020-10-13 132 2 6 0
2020-10-09 130 1 6 0
2020-10-07 129 1 6 0
2020-10-04 128 3 6 0
2020-10-01 125 1 6 0
2020-09-30 124 2 6 0
2020-09-22 122 2 6 0
2020-09-15 120 1 6 0
2020-09-10 119 1 6 0
2020-09-09 118 3 6 0
2020-09-06 115 1 6 0
2020-09-05 114 1 6 0
2020-08-18 113 1 6 0
2020-08-15 112 2 6 0
2020-08-08 110 1 6 0
2020-07-16 109 1 6 0
2020-07-12 108 1 6 0
2020-05-20 107 1 6 0
2020-05-06 106 1 6 0
2020-05-03 105 1 6 0
2020-05-01 104 1 6 0
2020-04-30 103 2 6 0
2020-04-29 101 2 6 0
2020-04-28 99 2 6 0
2020-04-25 97 3 6 0
2020-04-24 94 2 6 0
2020-04-23 92 2 6 0
2020-04-22 90 2 6 0
2020-04-21 88 3 6 0
2020-04-18 85 3 6 1
2020-04-17 82 2 5 0
2020-04-16 80 1 5 0
2020-04-15 79 4 5 0
2020-04-12 75 1 5 0
2020-04-11 74 3 5 0
2020-04-10 71 1 5 0
2020-04-09 70 3 5 2
2020-04-08 67 1 3 0
2020-04-06 66 1 3 0
2020-04-05 65 1 3 0
2020-04-04 64 1 3 0
2020-04-03 63 1 3 0
2020-04-02 62 3 3 0
2020-04-01 59 4 3 0
2020-03-31 55 3 3 0
2020-03-29 52 1 3 0
2020-03-28 51 11 3 0
2020-03-27 40 15 3 1
2020-03-26 25 3 2 1
2020-03-25 22 4 1 1
2020-03-24 18 6 0 0
2020-03-23 12 2 0 0
2020-03-20 10 3 0 0
2020-03-19 7 2 0 0
2020-03-17 5 4 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: 0:03:21.236368