Germany: LK Werra-Meißner-Kreis (Hessen)¶

  • Homepage of project: https://oscovida.github.io
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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 12:51:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Werra-Meißner-Kreis", weeks=5);
2023-03-07T12:52:03.359246 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 250 250 300 300 7-day incidence rate (per 100K people) 290.8 LK Werra-Meißner-Kreis, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 150 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 0.75 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 Werra-Meißner-Kreis cases daily growth factor Germany-LK Werra-Meißner-Kreis cases daily growth factor (rolling mean) Germany-LK Werra-Meißner-Kreis 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 Werra-Meißner-Kreis deaths daily growth factor Germany-LK Werra-Meißner-Kreis deaths daily growth factor (rolling mean) Germany-LK Werra-Meißner-Kreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Werra-Meißner-Kreis doubling time cases (rolling mean) 0.0 49.9 99.7 149.6 daily change Germany-LK Werra-Meißner-Kreis new cases (rolling 7d mean) Germany-LK Werra-Meißner-Kreis new cases 0.000 0.249 0.499 0.748 0.997 daily change Germany-LK Werra-Meißner-Kreis new deaths (rolling 7d mean) Germany-LK Werra-Meißner-Kreis new deaths 0.000 0.311 0.623 0.934
In [4]:
overview(country="Germany", subregion="LK Werra-Meißner-Kreis");
2023-03-07T12:52:20.128479 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) 290.8 LK Werra-Meißner-Kreis, 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 0.0 2.5 5.0 7.5 10.0 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 Werra-Meißner-Kreis cases daily growth factor Germany-LK Werra-Meißner-Kreis cases daily growth factor (rolling mean) Germany-LK Werra-Meißner-Kreis 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 Werra-Meißner-Kreis deaths daily growth factor Germany-LK Werra-Meißner-Kreis deaths daily growth factor (rolling mean) Germany-LK Werra-Meißner-Kreis 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 cases doubling time [days] Germany-LK Werra-Meißner-Kreis doubling time cases (rolling mean) Germany-LK Werra-Meißner-Kreis doubling time deaths (rolling mean) 0.0 199.4 398.9 598.3 daily change Germany-LK Werra-Meißner-Kreis new cases (rolling 7d mean) Germany-LK Werra-Meißner-Kreis new cases 0.00 2.49 4.99 7.48 9.97 daily change Germany-LK Werra-Meißner-Kreis new deaths (rolling 7d mean) Germany-LK Werra-Meißner-Kreis new deaths 0.0 94.8 189.7 284.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Werra-Meißner-Kreis", dates="2020-03-15:");
2023-03-07T12:54:49.400532 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 Werra-Meißner-Kreis LK Werra-Meißner-Kreis 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 Werra-Meißner-Kreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Werra-Meißner-Kreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Werra-Meißner-Kreis")
print(f'Population of country="Germany", subregion="LK Werra-Meißner-Kreis": {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 Werra-Meißner-Kreis": 99714 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 46194 25 256 0
2023-03-03 46169 191 256 0
2023-03-02 45978 7 256 0
2023-03-01 45971 49 256 0
2023-02-28 45922 18 256 0
2023-02-27 45904 49 256 0
2023-02-24 45855 19 256 0
2023-02-23 45836 58 256 0
2023-02-22 45778 48 256 1
2023-02-21 45730 34 255 0
2023-02-20 45696 54 255 0
2023-02-17 45642 19 255 0
2023-02-16 45623 24 255 0
2023-02-15 45599 68 255 0
2023-02-14 45531 38 255 0
2023-02-13 45493 37 255 0
2023-02-10 45456 11 255 0
2023-02-09 45445 22 255 0
2023-02-08 45423 27 255 0
2023-02-07 45396 32 255 0
2023-02-06 45364 18 255 0
2023-02-03 45346 16 255 1
2023-02-02 45330 23 254 0
2023-02-01 45307 27 254 0
2023-01-31 45280 22 254 1
2023-01-30 45258 28 253 0
2023-01-29 45230 1 253 0
2023-01-27 45229 10 253 0
2023-01-26 45219 26 253 0
2023-01-25 45193 22 253 0
2023-01-24 45171 18 253 0
2023-01-23 45153 24 253 0
2023-01-20 45129 13 253 0
2023-01-19 45116 30 253 0
2023-01-18 45086 28 253 0
2023-01-17 45058 18 253 0
2023-01-16 45040 37 253 0
2023-01-15 45003 1 253 0
2023-01-13 45002 18 253 0
2023-01-12 44984 26 253 0
2023-01-11 44958 31 253 0
2023-01-10 44927 26 253 0
2023-01-09 44901 87 253 1
2023-01-06 44814 14 252 0
2023-01-05 44800 27 252 0
2023-01-04 44773 45 252 1
2023-01-03 44728 35 251 0
2023-01-02 44693 74 251 0
2022-12-30 44619 41 251 1
2022-12-29 44578 47 250 0
2022-12-28 44531 42 250 0
2022-12-27 44489 93 250 4
2022-12-23 44396 45 246 0
2022-12-22 44351 89 246 0
2022-12-21 44262 69 246 3
2022-12-20 44193 32 243 1
2022-12-19 44161 78 242 0
2022-12-17 44083 1 242 0
2022-12-16 44082 46 242 1
2022-12-15 44036 51 241 0
2022-12-14 43985 91 241 0
2022-12-13 43894 47 241 0
2022-12-12 43847 83 241 0
2022-12-09 43764 41 241 1
2022-12-08 43723 37 240 1
2022-12-07 43686 89 239 1
2022-12-06 43597 45 238 0
2022-12-05 43552 48 238 1
2022-12-02 43504 49 237 0
2022-12-01 43455 38 237 1
2022-11-30 43417 66 236 0
2022-11-29 43351 56 236 0
2022-11-28 43295 47 236 0
2022-11-25 43248 41 236 1
2022-11-24 43207 35 235 0
2022-11-23 43172 62 235 1
2022-11-22 43110 57 234 1
2022-11-21 43053 51 233 0
2022-11-18 43002 47 233 0
2022-11-17 42955 24 233 0
2022-11-16 42931 36 233 0
2022-11-15 42895 42 233 0
2022-11-14 42853 47 233 0
2022-11-12 42806 1 233 0
2022-11-11 42805 31 233 0
2022-11-10 42774 42 233 0
2022-11-09 42732 41 233 0
2022-11-08 42691 62 233 0
2022-11-07 42629 59 233 0
2022-11-04 42570 33 233 0
2022-11-03 42537 57 233 0
2022-11-02 42480 85 233 0
2022-11-01 42395 55 233 1
2022-10-31 42340 76 232 0
2022-10-28 42264 50 232 1
2022-10-27 42214 76 231 0
2022-10-26 42138 90 231 0
2022-10-25 42048 123 231 0
2022-10-24 41925 136 231 0
2022-10-23 41789 1 231 0
2022-10-21 41788 109 231 0
2022-10-20 41679 142 231 0
2022-10-19 41537 219 231 0
2022-10-18 41318 124 231 0
2022-10-17 41194 209 231 1
2022-10-14 40985 117 230 1
2022-10-13 40868 140 229 0
2022-10-12 40728 207 229 0
2022-10-11 40521 216 229 0
2022-10-10 40305 246 229 0
2022-10-09 40059 1 229 0
2022-10-08 40058 1 229 0
2022-10-07 40057 177 229 0
2022-10-06 39880 321 229 1
2022-10-05 39559 167 228 0
2022-10-04 39392 161 228 1
2022-10-02 39231 1 227 0
2022-09-30 39230 73 227 0
2022-09-29 39157 124 227 0
2022-09-28 39033 199 227 0
2022-09-27 38834 104 227 0
2022-09-26 38730 72 227 0
2022-09-23 38658 78 227 0
2022-09-22 38580 60 227 0
2022-09-21 38520 107 227 0
2022-09-20 38413 94 227 0
2022-09-19 38319 107 227 0
2022-09-16 38212 57 227 0
2022-09-15 38155 49 227 0
2022-09-14 38106 121 227 0
2022-09-13 37985 36 227 0
2022-09-12 37949 47 227 0
2022-09-09 37902 45 227 0
2022-09-08 37857 64 227 0
2022-09-07 37793 75 227 0
2022-09-06 37718 65 227 0
2022-09-05 37653 75 227 0
2022-09-02 37578 70 227 0
2022-09-01 37508 44 227 0
2022-08-31 37464 49 227 0
2022-08-30 37415 77 227 0
2022-08-29 37338 64 227 0
2022-08-26 37274 40 227 0
2022-08-25 37234 47 227 0
2022-08-24 37187 85 227 0
2022-08-23 37102 76 227 0
2022-08-22 37026 127 227 0
2022-08-19 36899 81 227 0
2022-08-18 36818 83 227 0
2022-08-17 36735 81 227 0
2022-08-16 36654 84 227 0
2022-08-15 36570 71 227 0
2022-08-12 36499 38 227 0
2022-08-11 36461 76 227 0
2022-08-10 36385 37 227 0
2022-08-09 36348 98 227 0
2022-08-08 36250 87 227 0
2022-08-05 36163 36 227 0
2022-08-04 36127 54 227 0
2022-08-03 36073 114 227 0
2022-08-02 35959 113 227 0
2022-08-01 35846 96 227 0
2022-07-29 35750 85 227 0
2022-07-28 35665 139 227 0
2022-07-27 35526 139 227 2
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2022-06-23 28208 167 215 2
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2022-06-20 27577 68 212 1
2022-06-17 27509 138 211 0
2022-06-16 27371 1 211 0
2022-06-15 27370 97 211 1
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2022-06-13 27116 126 209 0
2022-06-10 26990 127 209 0
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2022-06-03 26464 81 209 0
2022-06-02 26383 54 209 0
2022-06-01 26329 86 209 0
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2022-05-30 26136 39 209 0
2022-05-27 26097 67 209 0
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2022-05-24 25976 71 209 0
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2022-05-20 25852 62 209 0
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2021-03-23 2555 6 143 0
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2021-01-22 1835 29 116 1
2021-01-21 1806 66 115 7
2021-01-20 1740 38 108 1
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2021-01-17 1680 13 104 0
2021-01-16 1667 23 104 3
2021-01-15 1644 11 101 3
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2021-01-12 1576 18 96 0
2021-01-11 1558 8 96 0
2021-01-10 1550 19 96 0
2021-01-09 1531 29 96 1
2021-01-08 1502 21 95 6
2021-01-07 1481 40 89 3
2021-01-06 1441 51 86 4
2021-01-05 1390 15 82 2
2021-01-04 1375 3 80 0
2021-01-03 1372 6 80 2
2021-01-02 1366 6 78 0
2021-01-01 1360 25 78 2
2020-12-31 1335 15 76 1
2020-12-30 1320 63 75 6
2020-12-29 1257 5 69 0
2020-12-28 1252 18 69 4
2020-12-26 1234 20 65 1
2020-12-24 1214 30 64 2
2020-12-23 1184 70 62 5
2020-12-22 1114 8 57 1
2020-12-21 1106 3 56 0
2020-12-20 1103 11 56 2
2020-12-19 1092 17 54 1
2020-12-18 1075 9 53 1
2020-12-17 1066 26 52 1
2020-12-16 1040 26 51 1
2020-12-15 1014 15 50 1
2020-12-14 999 10 49 0
2020-12-13 989 12 49 1
2020-12-12 977 12 48 1
2020-12-11 965 23 47 0
2020-12-10 942 18 47 2
2020-12-09 924 21 45 1
2020-12-08 903 3 44 0
2020-12-07 900 5 44 0
2020-12-06 895 13 44 0
2020-12-05 882 9 44 0
2020-12-04 873 23 44 2
2020-12-03 850 9 42 0
2020-12-02 841 17 42 1
2020-12-01 824 11 41 3
2020-11-30 813 2 38 0
2020-11-29 811 18 38 2
2020-11-27 793 15 36 1
2020-11-26 778 44 35 10
2020-11-25 734 19 25 0
2020-11-24 715 10 25 0
2020-11-23 705 54 25 2
2020-11-21 651 1 23 0
2020-11-20 650 33 23 2
2020-11-19 617 22 21 1
2020-11-18 595 22 20 1
2020-11-17 573 11 19 0
2020-11-16 562 3 19 0
2020-11-15 559 8 19 0
2020-11-14 551 4 19 0
2020-11-13 547 8 19 0
2020-11-12 539 7 19 0
2020-11-11 532 8 19 0
2020-11-10 524 12 19 0
2020-11-09 512 23 19 0
2020-11-07 489 1 19 0
2020-11-06 488 21 19 0
2020-11-05 467 7 19 0
2020-11-04 460 24 19 1
2020-11-03 436 8 18 0
2020-11-02 428 13 18 0
2020-11-01 415 4 18 0
2020-10-31 411 8 18 0
2020-10-30 403 12 18 0
2020-10-29 391 7 18 0
2020-10-28 384 5 18 0
2020-10-27 379 1 18 0
2020-10-26 378 18 18 0
2020-10-23 360 13 18 0
2020-10-22 347 4 18 0
2020-10-21 343 10 18 0
2020-10-19 333 4 18 0
2020-10-18 329 2 18 0
2020-10-17 327 2 18 0
2020-10-16 325 3 18 0
2020-10-15 322 4 18 0
2020-10-14 318 3 18 0
2020-10-13 315 1 18 0
2020-10-12 314 1 18 0
2020-10-11 313 5 18 0
2020-10-10 308 5 18 0
2020-10-09 303 10 18 0
2020-10-08 293 6 18 0
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2020-10-04 269 3 18 0
2020-10-02 266 6 18 0
2020-10-01 260 2 18 0
2020-09-30 258 1 18 0
2020-09-28 257 1 18 0
2020-09-25 256 1 18 0
2020-09-24 255 1 18 0
2020-09-23 254 1 18 0
2020-09-14 253 2 18 0
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2020-09-04 249 1 18 0
2020-09-03 248 1 18 0
2020-08-28 247 1 18 0
2020-08-26 246 1 18 0
2020-08-25 245 1 18 0
2020-08-20 244 1 18 0
2020-08-19 243 1 18 0
2020-08-18 242 1 18 0
2020-08-13 241 2 18 0
2020-08-11 239 1 18 0
2020-08-10 238 2 18 0
2020-08-05 236 2 18 0
2020-07-08 234 1 18 0
2020-07-04 233 1 18 0
2020-06-27 232 1 18 0
2020-06-25 231 1 18 0
2020-06-24 230 1 18 0
2020-06-23 229 1 18 0
2020-06-21 228 2 18 0
2020-06-18 226 2 18 0
2020-06-16 224 6 18 0
2020-06-14 218 2 18 0
2020-06-13 216 1 18 0
2020-06-12 215 1 18 0
2020-06-02 214 1 18 0
2020-05-28 213 1 18 0
2020-05-27 212 2 18 0
2020-05-26 210 3 18 0
2020-05-20 207 2 18 0
2020-05-19 205 1 18 0
2020-05-17 204 2 18 0
2020-05-15 202 2 18 0
2020-05-12 200 5 18 0
2020-05-09 195 1 18 0
2020-05-08 194 5 18 1
2020-05-07 189 5 17 0
2020-05-06 184 5 17 0
2020-05-05 179 1 17 0
2020-05-04 178 1 17 0
2020-05-03 177 1 17 0
2020-05-02 176 1 17 0
2020-05-01 175 1 17 0
2020-04-30 174 7 17 0
2020-04-29 167 4 17 0
2020-04-28 163 2 17 1
2020-04-27 161 1 16 0
2020-04-25 160 2 16 1
2020-04-24 158 7 15 0
2020-04-23 151 21 15 1
2020-04-22 130 5 14 0
2020-04-21 125 2 14 0
2020-04-19 123 1 14 1
2020-04-18 122 3 13 0
2020-04-17 119 5 13 0
2020-04-16 114 7 13 1
2020-04-15 107 7 12 1
2020-04-14 100 7 11 1
2020-04-13 93 12 10 1
2020-04-12 81 3 9 0
2020-04-11 78 22 9 3
2020-04-09 56 21 6 3
2020-04-08 35 1 3 0
2020-04-07 34 3 3 2
2020-04-06 31 1 1 0
2020-04-05 30 2 1 0
2020-04-04 28 1 1 0
2020-04-03 27 1 1 0
2020-04-02 26 3 1 0
2020-04-01 23 1 1 0
2020-03-31 22 1 1 1
2020-03-29 21 7 0 0
2020-03-26 14 1 0 0
2020-03-25 13 4 0 0
2020-03-24 9 4 0 0
2020-03-22 5 1 0 0
2020-03-21 4 1 0 0
2020-03-20 3 1 0 0
2020-03-19 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: 0:03:11.350031