Germany: LK Nordwestmecklenburg (Mecklenburg-Vorpommern)¶

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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 12:59:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Nordwestmecklenburg", weeks=5);
2023-03-07T12:59:29.292208 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 7-day incidence rate (per 100K people) 34.1 LK Nordwestmecklenburg, 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 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 Nordwestmecklenburg cases daily growth factor Germany-LK Nordwestmecklenburg cases daily growth factor (rolling mean) Germany-LK Nordwestmecklenburg 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 Nordwestmecklenburg deaths daily growth factor Germany-LK Nordwestmecklenburg deaths daily growth factor (rolling mean) Germany-LK Nordwestmecklenburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Nordwestmecklenburg doubling time cases (rolling mean) 0.00 31.69 63.38 daily change Germany-LK Nordwestmecklenburg new cases (rolling 7d mean) Germany-LK Nordwestmecklenburg new cases 0.000 0.792 1.584 daily change Germany-LK Nordwestmecklenburg new deaths (rolling 7d mean) Germany-LK Nordwestmecklenburg new deaths 0.000 0.233 0.466 0.699 0.932
In [4]:
overview(country="Germany", subregion="LK Nordwestmecklenburg");
2023-03-07T12:59:46.919053 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) 34.1 LK Nordwestmecklenburg, 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 1 2 3 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 Nordwestmecklenburg cases daily growth factor Germany-LK Nordwestmecklenburg cases daily growth factor (rolling mean) Germany-LK Nordwestmecklenburg 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 Nordwestmecklenburg deaths daily growth factor Germany-LK Nordwestmecklenburg deaths daily growth factor (rolling mean) Germany-LK Nordwestmecklenburg estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Nordwestmecklenburg doubling time cases (rolling mean) Germany-LK Nordwestmecklenburg doubling time deaths (rolling mean) 0 317 634 951 daily change Germany-LK Nordwestmecklenburg new cases (rolling 7d mean) Germany-LK Nordwestmecklenburg new cases 0.000 1.584 3.169 4.753 daily change Germany-LK Nordwestmecklenburg new deaths (rolling 7d mean) Germany-LK Nordwestmecklenburg new deaths 0.0 33.3 66.5 99.8 133.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Nordwestmecklenburg", dates="2020-03-15:");
2023-03-07T13:01:55.014216 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 Nordwestmecklenburg LK Nordwestmecklenburg 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 Nordwestmecklenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Nordwestmecklenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Nordwestmecklenburg")
print(f'Population of country="Germany", subregion="LK Nordwestmecklenburg": {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 Nordwestmecklenburg": 158449 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 76177 5 200 0
2023-03-03 76172 5 200 0
2023-03-02 76167 16 200 0
2023-03-01 76151 10 200 0
2023-02-28 76141 18 200 0
2023-02-27 76123 19 200 0
2023-02-24 76104 11 200 0
2023-02-23 76093 43 200 0
2023-02-22 76050 11 200 0
2023-02-21 76039 38 200 0
2023-02-20 76001 28 200 0
2023-02-17 75973 11 200 0
2023-02-16 75962 17 200 2
2023-02-15 75945 54 198 0
2023-02-14 75891 26 198 0
2023-02-13 75865 45 198 0
2023-02-10 75820 18 198 0
2023-02-09 75802 19 198 0
2023-02-08 75783 24 198 0
2023-02-07 75759 45 198 0
2023-02-06 75714 33 198 0
2023-02-03 75681 34 198 0
2023-02-02 75647 29 198 0
2023-02-01 75618 29 198 1
2023-01-31 75589 90 197 0
2023-01-30 75499 33 197 1
2023-01-27 75466 25 196 0
2023-01-26 75441 41 196 0
2023-01-25 75400 47 196 0
2023-01-24 75353 69 196 0
2023-01-23 75284 43 196 1
2023-01-20 75241 24 195 0
2023-01-19 75217 51 195 0
2023-01-18 75166 34 195 0
2023-01-17 75132 76 195 0
2023-01-16 75056 43 195 0
2023-01-13 75013 20 195 0
2023-01-12 74993 24 195 0
2023-01-11 74969 41 195 1
2023-01-10 74928 63 194 0
2023-01-09 74865 53 194 1
2023-01-06 74812 33 193 0
2023-01-05 74779 67 193 0
2023-01-04 74712 82 193 1
2023-01-03 74630 181 192 0
2023-01-02 74449 83 192 0
2022-12-30 74366 77 192 0
2022-12-29 74289 95 192 0
2022-12-28 74194 154 192 1
2022-12-27 74040 143 191 1
2022-12-23 73897 135 190 0
2022-12-22 73762 113 190 1
2022-12-21 73649 225 189 0
2022-12-20 73424 169 189 1
2022-12-19 73255 132 188 0
2022-12-16 73123 105 188 0
2022-12-15 73018 150 188 1
2022-12-14 72868 152 187 0
2022-12-13 72716 279 187 0
2022-12-12 72437 157 187 1
2022-12-09 72280 146 186 0
2022-12-08 72134 157 186 0
2022-12-07 71977 156 186 2
2022-12-06 71821 374 184 4
2022-12-05 71447 135 180 1
2022-12-02 71312 111 179 1
2022-12-01 71201 116 178 0
2022-11-30 71085 144 178 0
2022-11-29 70941 240 178 1
2022-11-28 70701 138 177 1
2022-11-25 70563 89 176 0
2022-11-24 70474 97 176 0
2022-11-23 70377 153 176 0
2022-11-22 70224 230 176 1
2022-11-21 69994 99 175 1
2022-11-18 69895 103 174 1
2022-11-17 69792 97 173 0
2022-11-16 69695 176 173 0
2022-11-15 69519 109 173 0
2022-11-14 69410 110 173 0
2022-11-12 69300 1 173 0
2022-11-11 69299 83 173 0
2022-11-10 69216 88 173 0
2022-11-09 69128 118 173 0
2022-11-08 69010 257 173 0
2022-11-07 68753 131 173 0
2022-11-04 68622 129 173 0
2022-11-03 68493 191 173 2
2022-11-02 68302 229 171 0
2022-11-01 68073 197 171 0
2022-10-28 67876 125 171 1
2022-10-27 67751 272 170 0
2022-10-26 67479 242 170 0
2022-10-25 67237 245 170 1
2022-10-24 66992 222 169 0
2022-10-21 66770 643 169 1
2022-10-20 66127 282 168 0
2022-10-19 65845 227 168 1
2022-10-18 65618 299 167 1
2022-10-17 65319 147 166 1
2022-10-14 65172 193 165 0
2022-10-13 64979 213 165 0
2022-10-12 64766 235 165 0
2022-10-11 64531 214 165 0
2022-10-10 64317 146 165 2
2022-10-07 64171 245 163 0
2022-10-06 63926 209 163 0
2022-10-05 63717 188 163 0
2022-10-04 63529 148 163 0
2022-09-30 63381 134 163 0
2022-09-29 63247 158 163 0
2022-09-28 63089 171 163 1
2022-09-27 62918 279 162 0
2022-09-26 62639 108 162 0
2022-09-23 62531 68 162 0
2022-09-22 62463 72 162 0
2022-09-21 62391 134 162 0
2022-09-20 62257 192 162 0
2022-09-19 62065 119 162 1
2022-09-16 61946 99 161 0
2022-09-15 61847 73 161 1
2022-09-14 61774 97 160 1
2022-09-13 61677 205 159 1
2022-09-12 61472 102 158 0
2022-09-09 61370 64 158 0
2022-09-08 61306 75 158 0
2022-09-07 61231 71 158 0
2022-09-06 61160 142 158 0
2022-09-05 61018 96 158 0
2022-09-02 60922 66 158 0
2022-09-01 60856 66 158 0
2022-08-31 60790 94 158 0
2022-08-30 60696 153 158 0
2022-08-29 60543 113 158 0
2022-08-26 60430 63 158 0
2022-08-25 60367 97 158 0
2022-08-24 60270 89 158 0
2022-08-23 60181 188 158 1
2022-08-22 59993 115 157 0
2022-08-19 59878 86 157 0
2022-08-18 59792 91 157 0
2022-08-17 59701 98 157 0
2022-08-16 59603 267 157 1
2022-08-15 59336 133 156 0
2022-08-12 59203 114 156 0
2022-08-11 59089 99 156 1
2022-08-10 58990 128 155 0
2022-08-09 58862 231 155 2
2022-08-08 58631 150 153 0
2022-08-05 58481 99 153 0
2022-08-04 58382 100 153 0
2022-08-03 58282 104 153 0
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2022-08-01 57903 182 153 3
2022-07-30 57721 1 150 0
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2021-01-21 1562 16 35 0
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2021-01-18 1515 1 35 0
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2021-01-16 1512 5 35 0
2021-01-15 1507 22 35 5
2021-01-14 1485 13 30 1
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2021-01-12 1446 17 29 0
2021-01-11 1429 6 29 1
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2021-01-09 1421 29 28 0
2021-01-08 1392 30 28 2
2021-01-07 1362 37 26 2
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2021-01-05 1304 45 24 0
2021-01-04 1259 9 24 0
2021-01-03 1250 5 24 0
2021-01-02 1245 1 24 0
2021-01-01 1244 3 24 0
2020-12-31 1241 15 24 0
2020-12-30 1226 25 24 0
2020-12-29 1201 45 24 0
2020-12-28 1156 6 24 1
2020-12-27 1150 1 23 0
2020-12-25 1149 2 23 0
2020-12-24 1147 14 23 1
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2020-12-22 1118 29 22 0
2020-12-21 1089 2 22 0
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2020-12-18 1067 17 21 2
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2020-12-13 963 10 19 0
2020-12-12 953 26 19 0
2020-12-11 927 63 19 1
2020-12-10 864 32 18 5
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2020-12-08 808 38 13 1
2020-12-07 770 5 12 0
2020-12-06 765 4 12 0
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2020-12-03 729 23 12 1
2020-12-02 706 10 11 1
2020-12-01 696 22 10 0
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2020-11-29 671 3 9 1
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2020-11-26 643 32 8 2
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2020-11-21 572 17 5 0
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2020-11-11 387 23 3 1
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2020-10-31 241 1 1 0
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2020-10-23 190 7 1 0
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2020-10-17 165 2 1 0
2020-10-16 163 2 1 0
2020-10-15 161 1 1 0
2020-10-14 160 2 1 0
2020-10-13 158 4 1 0
2020-10-10 154 1 1 0
2020-10-09 153 3 1 0
2020-10-08 150 1 1 0
2020-10-07 149 1 1 0
2020-10-04 148 1 1 0
2020-10-03 147 1 1 0
2020-10-02 146 3 1 0
2020-10-01 143 7 1 0
2020-09-29 136 12 1 0
2020-09-27 124 3 1 0
2020-09-25 121 6 1 0
2020-09-24 115 2 1 0
2020-09-23 113 2 1 0
2020-09-22 111 3 1 0
2020-09-15 108 1 1 0
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2020-08-21 99 1 1 0
2020-08-19 98 1 1 0
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2020-06-24 80 1 1 0
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2020-04-15 64 1 1 0
2020-04-12 63 3 1 0
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2020-04-08 55 1 1 0
2020-04-07 54 3 1 0
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2020-04-02 44 6 1 0
2020-04-01 38 2 1 0
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2020-03-28 28 3 1 1
2020-03-27 25 1 0 0
2020-03-25 24 3 0 0
2020-03-24 21 3 0 0
2020-03-21 18 1 0 0
2020-03-19 17 4 0 0
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2020-03-17 8 5 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: 0:03:33.490818