Germany: LK Wolfenbüttel (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 13:30:38 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wolfenbüttel", weeks=5);
2023-03-07T13:30:47.182171 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 68.8 LK Wolfenbüttel, 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 0.0 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Wolfenbüttel cases daily growth factor Germany-LK Wolfenbüttel cases daily growth factor (rolling mean) Germany-LK Wolfenbüttel 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 Wolfenbüttel deaths daily growth factor Germany-LK Wolfenbüttel deaths daily growth factor (rolling mean) Germany-LK Wolfenbüttel estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Wolfenbüttel doubling time cases (rolling mean) 0.00 11.92 23.84 35.77 daily change Germany-LK Wolfenbüttel new cases (rolling 7d mean) Germany-LK Wolfenbüttel new cases 0.000 0.238 0.477 0.715 0.954 daily change Germany-LK Wolfenbüttel new deaths (rolling 7d mean) Germany-LK Wolfenbüttel new deaths 0.000 0.329 0.659 0.988
In [4]:
overview(country="Germany", subregion="LK Wolfenbüttel");
2023-03-07T13:31:04.927768 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 3000 3000 7-day incidence rate (per 100K people) 68.8 LK Wolfenbüttel, 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 0 200 400 600 800 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 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 Wolfenbüttel cases daily growth factor Germany-LK Wolfenbüttel cases daily growth factor (rolling mean) Germany-LK Wolfenbüttel 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 Wolfenbüttel deaths daily growth factor Germany-LK Wolfenbüttel deaths daily growth factor (rolling mean) Germany-LK Wolfenbüttel 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 Wolfenbüttel doubling time cases (rolling mean) Germany-LK Wolfenbüttel doubling time deaths (rolling mean) 0 238 477 715 954 daily change Germany-LK Wolfenbüttel new cases (rolling 7d mean) Germany-LK Wolfenbüttel new cases 0.000 2.384 4.769 daily change Germany-LK Wolfenbüttel new deaths (rolling 7d mean) Germany-LK Wolfenbüttel new deaths 0.0 62.9 125.9 188.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wolfenbüttel", dates="2020-03-15:");
2023-03-07T13:33:32.392027 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 Wolfenbüttel LK Wolfenbüttel 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 Wolfenbüttel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wolfenbüttel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wolfenbüttel")
print(f'Population of country="Germany", subregion="LK Wolfenbüttel": {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 Wolfenbüttel": 119224 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 55849 37 212 0
2023-03-02 55812 1 212 0
2023-03-01 55811 23 212 0
2023-02-28 55788 21 212 1
2023-02-27 55767 41 211 0
2023-02-24 55726 25 211 0
2023-02-23 55701 17 211 0
2023-02-22 55684 35 211 0
2023-02-21 55649 28 211 0
2023-02-20 55621 38 211 0
2023-02-17 55583 27 211 0
2023-02-16 55556 16 211 0
2023-02-15 55540 34 211 0
2023-02-14 55506 29 211 0
2023-02-13 55477 38 211 0
2023-02-10 55439 13 211 0
2023-02-09 55426 15 211 0
2023-02-08 55411 35 211 0
2023-02-07 55376 23 211 0
2023-02-06 55353 31 211 1
2023-02-03 55322 33 210 0
2023-02-02 55289 14 210 0
2023-02-01 55275 46 210 0
2023-01-31 55229 36 210 0
2023-01-30 55193 59 210 0
2023-01-27 55134 35 210 0
2023-01-26 55099 27 210 0
2023-01-25 55072 42 210 1
2023-01-24 55030 33 209 0
2023-01-23 54997 68 209 0
2023-01-20 54929 32 209 0
2023-01-19 54897 28 209 0
2023-01-18 54869 36 209 0
2023-01-17 54833 28 209 0
2023-01-16 54805 39 209 0
2023-01-13 54766 33 209 0
2023-01-12 54733 41 209 1
2023-01-11 54692 39 208 1
2023-01-10 54653 55 207 0
2023-01-09 54598 78 207 0
2023-01-06 54520 48 207 0
2023-01-05 54472 35 207 1
2023-01-04 54437 70 206 0
2023-01-03 54367 69 206 0
2023-01-02 54298 101 206 0
2022-12-30 54197 73 206 0
2022-12-29 54124 85 206 1
2022-12-28 54039 128 205 1
2022-12-27 53911 193 204 1
2022-12-23 53718 89 203 1
2022-12-22 53629 205 202 4
2022-12-21 53424 105 198 0
2022-12-20 53319 204 198 3
2022-12-19 53115 128 195 2
2022-12-16 52987 123 193 0
2022-12-15 52864 87 193 0
2022-12-14 52777 131 193 1
2022-12-13 52646 136 192 0
2022-12-12 52510 156 192 0
2022-12-09 52354 106 192 0
2022-12-08 52248 106 192 1
2022-12-07 52142 117 191 0
2022-12-06 52025 186 191 0
2022-12-05 51839 151 191 1
2022-12-02 51688 81 190 1
2022-12-01 51607 116 189 1
2022-11-30 51491 117 188 0
2022-11-29 51374 117 188 1
2022-11-28 51257 158 187 0
2022-11-25 51099 80 187 0
2022-11-24 51019 68 187 0
2022-11-23 50951 106 187 0
2022-11-22 50845 137 187 1
2022-11-21 50708 99 186 0
2022-11-18 50609 59 186 0
2022-11-17 50550 84 186 0
2022-11-16 50466 80 186 0
2022-11-15 50386 74 186 0
2022-11-14 50312 101 186 0
2022-11-11 50211 63 186 0
2022-11-10 50148 73 186 0
2022-11-09 50075 86 186 1
2022-11-08 49989 118 185 0
2022-11-07 49871 123 185 0
2022-11-04 49748 60 185 0
2022-11-03 49688 79 185 0
2022-11-02 49609 189 185 0
2022-11-01 49420 224 185 1
2022-10-28 49196 82 184 1
2022-10-27 49114 111 183 0
2022-10-26 49003 199 183 4
2022-10-25 48804 175 179 1
2022-10-24 48629 243 178 0
2022-10-21 48386 138 178 1
2022-10-20 48248 139 177 1
2022-10-19 48109 200 176 0
2022-10-18 47909 252 176 1
2022-10-17 47657 288 175 2
2022-10-14 47369 173 173 0
2022-10-13 47196 214 173 2
2022-10-12 46982 251 171 0
2022-10-11 46731 306 171 2
2022-10-10 46425 348 169 1
2022-10-07 46077 243 168 1
2022-10-06 45834 246 167 2
2022-10-05 45588 287 165 0
2022-10-04 45301 308 165 2
2022-09-30 44993 137 163 1
2022-09-29 44856 129 162 0
2022-09-28 44727 153 162 1
2022-09-27 44574 172 161 0
2022-09-26 44402 152 161 0
2022-09-23 44250 94 161 2
2022-09-22 44156 113 159 0
2022-09-21 44043 75 159 0
2022-09-20 43968 116 159 1
2022-09-19 43852 118 158 0
2022-09-16 43734 55 158 0
2022-09-15 43679 70 158 0
2022-09-14 43609 66 158 1
2022-09-13 43543 109 157 0
2022-09-12 43434 111 157 1
2022-09-09 43323 48 156 0
2022-09-08 43275 92 156 0
2022-09-07 43183 52 156 0
2022-09-06 43131 96 156 0
2022-09-05 43035 112 156 0
2022-09-02 42923 48 156 0
2022-09-01 42875 56 156 0
2022-08-31 42819 91 156 0
2022-08-30 42728 59 156 0
2022-08-29 42669 122 156 1
2022-08-26 42547 86 155 0
2022-08-25 42461 46 155 0
2022-08-24 42415 101 155 0
2022-08-23 42314 103 155 1
2022-08-22 42211 127 154 0
2022-08-19 42084 45 154 0
2022-08-18 42039 100 154 2
2022-08-17 41939 107 152 0
2022-08-16 41832 95 152 0
2022-08-15 41737 209 152 1
2022-08-11 41528 44 151 0
2022-08-10 41484 114 151 1
2022-08-09 41370 142 150 0
2022-08-08 41228 129 150 0
2022-08-05 41099 79 150 0
2022-08-04 41020 76 150 1
2022-08-03 40944 119 149 0
2022-08-02 40825 126 149 0
2022-08-01 40699 195 149 0
2022-07-29 40504 86 149 0
2022-07-28 40418 126 149 1
2022-07-27 40292 167 148 1
2022-07-26 40125 236 147 2
2022-07-25 39889 239 145 0
2022-07-22 39650 187 145 0
2022-07-21 39463 193 145 0
2022-07-20 39270 219 145 2
2022-07-19 39051 382 143 2
2022-07-18 38669 343 141 0
2022-07-15 38326 175 141 2
2022-07-14 38151 263 139 0
2022-07-13 37888 263 139 0
2022-07-12 37625 270 139 1
2022-07-11 37355 349 138 1
2022-07-08 37006 163 137 0
2022-07-07 36843 205 137 0
2022-07-06 36638 240 137 0
2022-07-05 36398 259 137 0
2022-07-04 36139 336 137 0
2022-07-01 35803 235 137 1
2022-06-30 35568 252 136 0
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2022-06-24 34362 123 135 0
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2022-06-22 34016 247 134 0
2022-06-21 33769 185 134 0
2022-06-20 33584 376 134 1
2022-06-19 33208 1 133 0
2022-06-17 33207 126 133 1
2022-06-16 33081 161 132 0
2022-06-15 32920 193 132 0
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2022-06-13 32533 213 132 1
2022-06-10 32320 83 131 0
2022-06-09 32237 147 131 0
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2022-06-07 31911 150 131 1
2022-06-03 31761 42 130 0
2022-06-02 31719 60 130 0
2022-06-01 31659 73 130 0
2022-05-31 31586 76 130 0
2022-05-30 31510 87 130 0
2022-05-27 31423 66 130 0
2022-05-25 31357 83 130 0
2022-05-24 31274 65 130 0
2022-05-23 31209 115 130 0
2022-05-20 31094 67 130 0
2022-05-19 31027 78 130 0
2022-05-18 30949 73 130 0
2022-05-17 30876 143 130 0
2022-05-16 30733 176 130 1
2022-05-13 30557 123 129 0
2022-05-12 30434 77 129 0
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2022-05-06 29868 131 129 0
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2022-05-02 29235 226 129 0
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2022-04-28 28890 151 129 0
2022-04-27 28739 173 129 0
2022-04-26 28566 211 129 0
2022-04-25 28355 283 129 1
2022-04-22 28072 199 128 1
2022-04-21 27873 321 127 0
2022-04-20 27552 334 127 0
2022-04-19 27218 545 127 1
2022-04-14 26673 195 126 0
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2022-04-07 25243 243 126 0
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2022-03-29 22813 654 124 0
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2022-03-26 21340 140 124 0
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2021-02-16 1556 1 80 0
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2021-02-12 1548 6 80 0
2021-02-11 1542 20 80 3
2021-02-10 1522 7 77 1
2021-02-08 1515 5 76 1
2021-02-07 1510 22 75 3
2021-02-05 1488 1 72 0
2021-02-04 1487 24 72 5
2021-02-03 1463 7 67 1
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2021-01-27 1379 42 65 7
2021-01-26 1337 3 58 0
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2021-01-20 1288 7 54 0
2021-01-19 1281 12 54 0
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2021-01-09 1180 14 52 0
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2021-01-07 1151 6 52 0
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2021-01-05 1132 4 52 0
2021-01-04 1128 2 52 0
2021-01-03 1126 3 52 0
2021-01-02 1123 4 52 0
2020-12-31 1119 18 52 0
2020-12-30 1101 13 52 0
2020-12-29 1088 12 52 0
2020-12-28 1076 7 52 1
2020-12-27 1069 2 51 0
2020-12-26 1067 7 51 0
2020-12-25 1060 17 51 1
2020-12-24 1043 11 50 0
2020-12-23 1032 16 50 0
2020-12-22 1016 4 50 0
2020-12-21 1012 14 50 1
2020-12-19 998 23 49 0
2020-12-18 975 9 49 0
2020-12-17 966 15 49 0
2020-12-16 951 15 49 0
2020-12-15 936 1 49 0
2020-12-14 935 32 49 0
2020-12-11 903 21 49 1
2020-12-10 882 53 48 7
2020-12-09 829 25 41 2
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2020-12-07 795 28 38 0
2020-12-06 767 2 38 0
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2020-12-03 761 12 38 0
2020-12-02 749 19 38 0
2020-12-01 730 5 38 0
2020-11-30 725 3 38 0
2020-11-29 722 15 38 2
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2020-11-26 695 20 35 1
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2020-11-24 664 12 34 1
2020-11-23 652 16 33 0
2020-11-20 636 7 33 0
2020-11-19 629 18 33 1
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2020-11-16 596 30 32 3
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2020-11-10 491 11 25 0
2020-11-09 480 20 25 1
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2020-10-29 345 9 15 0
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2020-10-24 292 2 14 0
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2020-10-22 286 6 14 0
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2020-10-15 255 7 14 0
2020-10-14 248 1 14 0
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2020-10-01 236 2 14 0
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2020-09-29 233 1 14 0
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2020-09-24 231 1 14 0
2020-09-23 230 3 14 0
2020-09-22 227 2 14 0
2020-09-18 225 2 14 0
2020-09-17 223 2 14 0
2020-09-16 221 1 14 0
2020-09-15 220 1 14 0
2020-09-14 219 1 14 0
2020-08-27 218 1 14 0
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2020-07-13 191 1 14 0
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2020-05-28 185 2 14 0
2020-05-25 183 1 14 0
2020-05-20 182 1 14 0
2020-05-14 181 1 14 0
2020-05-13 180 3 14 0
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2020-04-29 170 2 13 0
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2020-04-27 167 6 13 0
2020-04-24 161 5 13 1
2020-04-22 156 4 12 0
2020-04-21 152 3 12 0
2020-04-19 149 12 12 2
2020-04-18 137 1 10 0
2020-04-17 136 11 10 1
2020-04-16 125 8 9 2
2020-04-15 117 5 7 2
2020-04-14 112 5 5 1
2020-04-11 107 1 4 0
2020-04-10 106 16 4 2
2020-04-09 90 7 2 2
2020-04-08 83 5 0 0
2020-04-07 78 4 0 0
2020-04-06 74 13 0 0
2020-04-05 61 2 0 0
2020-04-03 59 7 0 0
2020-04-02 52 8 0 0
2020-04-01 44 7 0 0
2020-03-31 37 3 0 0
2020-03-30 34 3 0 0
2020-03-27 31 5 0 0
2020-03-26 26 6 0 0
2020-03-25 20 4 0 0
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2020-03-20 4 1 0 0
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2020-03-18 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: 0:03:03.341819