Germany: SK Frankfurt (Oder) (Brandenburg)¶

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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 12:28:35 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Frankfurt (Oder)", weeks=5);
2023-03-07T12:28:45.940883 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 7-day incidence rate (per 100K people) 72.3 SK Frankfurt (Oder), 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Frankfurt (Oder) new deaths (rolling 7d mean) Germany-SK Frankfurt (Oder) new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Frankfurt (Oder) cases daily growth factor Germany-SK Frankfurt (Oder) cases daily growth factor (rolling mean) Germany-SK Frankfurt (Oder) estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Frankfurt (Oder) deaths daily growth factor Germany-SK Frankfurt (Oder) deaths daily growth factor (rolling mean) Germany-SK Frankfurt (Oder) estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Frankfurt (Oder) doubling time cases (rolling mean) 0.00 5.67 11.34 17.00 22.67 daily change Germany-SK Frankfurt (Oder) new cases (rolling 7d mean) Germany-SK Frankfurt (Oder) new cases 0.000 0.349 0.699
In [4]:
overview(country="Germany", subregion="SK Frankfurt (Oder)");
2023-03-07T12:29:01.539726 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 72.3 SK Frankfurt (Oder), 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5 10 15 20 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Frankfurt (Oder) cases daily growth factor Germany-SK Frankfurt (Oder) cases daily growth factor (rolling mean) Germany-SK Frankfurt (Oder) estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-SK Frankfurt (Oder) deaths daily growth factor Germany-SK Frankfurt (Oder) deaths daily growth factor (rolling mean) Germany-SK Frankfurt (Oder) 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 cases doubling time [days] Germany-SK Frankfurt (Oder) doubling time cases (rolling mean) Germany-SK Frankfurt (Oder) doubling time deaths (rolling mean) 0.0 113.4 226.7 daily change Germany-SK Frankfurt (Oder) new cases (rolling 7d mean) Germany-SK Frankfurt (Oder) new cases 0.00 2.83 5.67 8.50 11.34 daily change Germany-SK Frankfurt (Oder) new deaths (rolling 7d mean) Germany-SK Frankfurt (Oder) new deaths 0.0 57.3 114.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Frankfurt (Oder)", dates="2020-03-15:");
2023-03-07T12:31:14.529591 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: SK Frankfurt (Oder) SK Frankfurt (Oder) 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people SK Frankfurt (Oder) Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Frankfurt (Oder)")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Frankfurt (Oder)")
print(f'Population of country="Germany", subregion="SK Frankfurt (Oder)": {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="SK Frankfurt (Oder)": 56679 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 27618 1 222 0
2023-03-04 27617 3 222 0
2023-03-03 27614 1 222 0
2023-03-02 27613 4 222 0
2023-03-01 27609 5 222 0
2023-02-28 27604 27 222 0
2023-02-27 27577 3 222 0
2023-02-26 27574 1 222 0
2023-02-25 27573 5 222 0
2023-02-24 27568 5 222 0
2023-02-23 27563 9 222 0
2023-02-22 27554 6 222 0
2023-02-21 27548 11 222 0
2023-02-19 27537 6 222 0
2023-02-18 27531 7 222 0
2023-02-17 27524 4 222 0
2023-02-16 27520 15 222 0
2023-02-15 27505 7 222 0
2023-02-14 27498 21 222 0
2023-02-13 27477 3 222 0
2023-02-12 27474 13 222 0
2023-02-10 27461 6 222 0
2023-02-09 27455 11 222 0
2023-02-08 27444 6 222 0
2023-02-07 27438 27 222 0
2023-02-06 27411 1 222 0
2023-02-04 27410 1 222 0
2023-02-03 27409 16 222 0
2023-02-02 27393 12 222 0
2023-02-01 27381 8 222 0
2023-01-31 27373 16 222 0
2023-01-30 27357 8 222 0
2023-01-28 27349 4 222 0
2023-01-27 27345 4 222 0
2023-01-26 27341 20 222 0
2023-01-25 27321 7 222 0
2023-01-24 27314 24 222 0
2023-01-23 27290 2 222 0
2023-01-21 27288 11 222 0
2023-01-20 27277 9 222 0
2023-01-19 27268 13 222 0
2023-01-18 27255 12 222 0
2023-01-17 27243 6 222 0
2023-01-16 27237 14 222 0
2023-01-15 27223 2 222 0
2023-01-14 27221 11 222 0
2023-01-13 27210 5 222 0
2023-01-12 27205 10 222 0
2023-01-11 27195 17 222 0
2023-01-10 27178 19 222 0
2023-01-09 27159 2 222 0
2023-01-08 27157 1 222 1
2023-01-07 27156 7 221 0
2023-01-06 27149 26 221 0
2023-01-05 27123 21 221 0
2023-01-04 27102 15 221 0
2023-01-03 27087 41 221 0
2023-01-02 27046 2 221 0
2023-01-01 27044 7 221 0
2022-12-31 27037 22 221 0
2022-12-30 27015 27 221 0
2022-12-29 26988 22 221 0
2022-12-28 26966 48 221 0
2022-12-27 26918 6 221 0
2022-12-26 26912 3 221 0
2022-12-25 26909 18 221 0
2022-12-23 26891 55 221 0
2022-12-22 26836 41 221 0
2022-12-21 26795 61 221 0
2022-12-20 26734 57 221 0
2022-12-19 26677 53 221 0
2022-12-17 26624 14 221 0
2022-12-16 26610 34 221 0
2022-12-15 26576 50 221 0
2022-12-14 26526 34 221 0
2022-12-13 26492 99 221 0
2022-12-12 26393 6 221 0
2022-12-11 26387 4 221 0
2022-12-10 26383 40 221 1
2022-12-09 26343 49 220 1
2022-12-08 26294 33 219 0
2022-12-07 26261 66 219 1
2022-12-06 26195 83 218 0
2022-12-05 26112 40 218 0
2022-12-03 26072 12 218 0
2022-12-02 26060 48 218 0
2022-12-01 26012 48 218 0
2022-11-30 25964 57 218 0
2022-11-29 25907 73 218 0
2022-11-28 25834 3 218 0
2022-11-27 25831 21 218 0
2022-11-26 25810 20 218 0
2022-11-25 25790 25 218 0
2022-11-24 25765 49 218 0
2022-11-23 25716 33 218 0
2022-11-22 25683 68 218 0
2022-11-21 25615 7 218 0
2022-11-20 25608 6 218 0
2022-11-19 25602 27 218 0
2022-11-18 25575 22 218 0
2022-11-17 25553 17 218 0
2022-11-16 25536 21 218 0
2022-11-15 25515 43 218 0
2022-11-14 25472 2 218 0
2022-11-13 25470 9 218 0
2022-11-12 25461 20 218 0
2022-11-11 25441 19 218 0
2022-11-10 25422 33 218 0
2022-11-09 25389 20 218 0
2022-11-08 25369 57 218 0
2022-11-07 25312 1 218 0
2022-11-06 25311 3 218 0
2022-11-05 25308 9 218 0
2022-11-04 25299 48 218 1
2022-11-03 25251 37 217 0
2022-11-02 25214 74 217 0
2022-11-01 25140 58 217 0
2022-10-29 25082 31 217 0
2022-10-28 25051 52 217 0
2022-10-27 24999 54 217 0
2022-10-26 24945 53 217 0
2022-10-25 24892 133 217 0
2022-10-24 24759 7 217 0
2022-10-23 24752 27 217 0
2022-10-22 24725 1 217 0
2022-10-21 24724 99 217 1
2022-10-20 24625 119 216 1
2022-10-19 24506 112 215 0
2022-10-18 24394 195 215 0
2022-10-17 24199 16 215 1
2022-10-16 24183 5 214 1
2022-10-15 24178 70 213 1
2022-10-14 24108 153 212 1
2022-10-13 23955 80 211 0
2022-10-12 23875 150 211 1
2022-10-11 23725 236 210 1
2022-10-10 23489 60 209 0
2022-10-08 23429 114 209 0
2022-10-07 23315 107 209 0
2022-10-06 23208 196 209 0
2022-10-05 23012 223 209 0
2022-10-04 22789 30 209 2
2022-10-01 22759 86 207 0
2022-09-30 22673 68 207 0
2022-09-29 22605 80 207 0
2022-09-28 22525 197 207 0
2022-09-27 22328 37 207 0
2022-09-26 22291 28 207 1
2022-09-24 22263 39 206 0
2022-09-23 22224 36 206 0
2022-09-22 22188 45 206 1
2022-09-21 22143 54 205 0
2022-09-20 22089 95 205 0
2022-09-19 21994 13 205 0
2022-09-17 21981 30 205 0
2022-09-16 21951 23 205 0
2022-09-15 21928 27 205 0
2022-09-14 21901 63 205 0
2022-09-13 21838 76 205 0
2022-09-12 21762 16 205 0
2022-09-10 21746 36 205 0
2022-09-09 21710 10 205 0
2022-09-08 21700 125 205 0
2022-09-07 21575 42 205 0
2022-09-06 21533 7 205 0
2022-09-05 21526 5 205 0
2022-09-03 21521 31 205 1
2022-09-02 21490 27 204 0
2022-09-01 21463 36 204 0
2022-08-31 21427 46 204 2
2022-08-30 21381 98 202 0
2022-08-27 21283 23 202 0
2022-08-26 21260 28 202 0
2022-08-25 21232 31 202 0
2022-08-24 21201 51 202 0
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2022-08-22 21090 25 202 0
2022-08-20 21065 1 202 0
2022-08-19 21064 30 202 0
2022-08-18 21034 37 202 0
2022-08-17 20997 69 202 0
2022-08-16 20928 81 202 0
2022-08-15 20847 12 202 0
2022-08-13 20835 31 202 0
2022-08-12 20804 35 202 0
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2022-08-10 20715 44 202 0
2022-08-09 20671 75 202 0
2022-08-08 20596 26 202 0
2022-08-06 20570 42 202 0
2022-08-05 20528 32 202 0
2022-08-04 20496 50 202 0
2022-08-03 20446 46 202 0
2022-08-02 20400 94 202 0
2022-08-01 20306 24 202 0
2022-07-30 20282 30 202 0
2022-07-29 20252 49 202 0
2022-07-28 20203 41 202 0
2022-07-27 20162 70 202 2
2022-07-26 20092 133 200 1
2022-07-25 19959 29 199 0
2022-07-23 19930 71 199 0
2022-07-22 19859 72 199 0
2022-07-21 19787 52 199 0
2022-07-20 19735 87 199 0
2022-07-19 19648 152 199 0
2022-07-18 19496 32 199 0
2022-07-16 19464 50 199 0
2022-07-15 19414 59 199 0
2022-07-14 19355 52 199 0
2022-07-13 19303 95 199 0
2022-07-12 19208 95 199 0
2022-07-11 19113 21 199 1
2022-07-09 19092 37 198 0
2022-07-08 19055 53 198 0
2022-07-07 19002 51 198 0
2022-07-06 18951 66 198 0
2022-07-05 18885 86 198 0
2022-07-04 18799 36 198 0
2022-07-02 18763 43 198 0
2022-07-01 18720 56 198 0
2022-06-30 18664 48 198 0
2022-06-29 18616 72 198 0
2022-06-28 18544 110 198 0
2022-06-25 18434 26 198 0
2022-06-24 18408 26 198 0
2022-06-23 18382 34 198 0
2022-06-22 18348 41 198 0
2022-06-21 18307 68 198 0
2022-06-20 18239 8 198 0
2022-06-18 18231 21 198 0
2022-06-17 18210 10 198 0
2022-06-16 18200 30 198 0
2022-06-15 18170 25 198 0
2022-06-14 18145 48 198 0
2022-06-13 18097 8 198 0
2022-06-11 18089 19 198 0
2022-06-10 18070 23 198 0
2022-06-09 18047 23 198 0
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2021-08-14 2303 5 113 0
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2021-07-27 2277 5 113 0
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2021-01-27 1371 44 84 6
2021-01-26 1327 18 78 1
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2021-01-24 1301 3 74 0
2021-01-23 1298 8 74 2
2021-01-22 1290 11 72 1
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2021-01-20 1255 21 68 3
2021-01-19 1234 17 65 1
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2021-01-06 1084 31 51 3
2021-01-05 1053 50 48 5
2021-01-04 1003 2 43 2
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2021-01-02 996 21 40 1
2020-12-31 975 64 39 14
2020-12-30 911 10 25 1
2020-12-29 901 23 24 0
2020-12-26 878 11 24 2
2020-12-25 867 3 22 0
2020-12-24 864 14 22 0
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2020-12-22 821 54 21 3
2020-12-21 767 10 18 1
2020-12-20 757 24 17 2
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2020-12-18 722 26 14 2
2020-12-17 696 39 12 0
2020-12-16 657 19 12 1
2020-12-15 638 23 11 0
2020-12-14 615 8 11 0
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2020-12-05 472 11 9 0
2020-12-04 461 12 9 0
2020-12-03 449 18 9 1
2020-12-02 431 26 8 1
2020-12-01 405 13 7 1
2020-11-30 392 8 6 0
2020-11-29 384 11 6 0
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2020-11-26 350 17 6 1
2020-11-25 333 3 5 0
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2020-11-21 308 10 5 0
2020-11-20 298 10 5 0
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2020-11-15 242 3 5 0
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2020-11-13 230 6 5 0
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2020-11-11 219 6 5 0
2020-11-10 213 7 5 0
2020-11-09 206 4 5 1
2020-11-08 202 7 4 0
2020-11-07 195 1 4 0
2020-11-06 194 3 4 0
2020-11-05 191 10 4 0
2020-11-04 181 8 4 0
2020-11-03 173 7 4 0
2020-11-01 166 9 4 0
2020-10-31 157 1 4 0
2020-10-30 156 7 4 1
2020-10-29 149 12 3 1
2020-10-28 137 5 2 0
2020-10-27 132 2 2 0
2020-10-26 130 4 2 0
2020-10-25 126 4 2 0
2020-10-23 122 3 2 0
2020-10-22 119 1 2 0
2020-10-21 118 2 2 0
2020-10-20 116 5 2 0
2020-10-16 111 4 2 0
2020-10-15 107 8 2 0
2020-10-14 99 4 2 0
2020-10-13 95 7 2 0
2020-10-11 88 1 2 1
2020-10-10 87 3 1 0
2020-10-09 84 1 1 0
2020-10-08 83 3 1 1
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2020-09-28 71 1 0 0
2020-09-24 70 1 0 0
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2020-09-08 64 1 0 0
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2020-09-03 57 2 0 0
2020-09-02 55 1 0 0
2020-09-01 54 1 0 0
2020-08-31 53 1 0 0
2020-08-20 52 1 0 0
2020-08-19 51 5 0 0
2020-08-18 46 2 0 0
2020-08-17 44 8 0 0
2020-08-08 36 1 0 0
2020-08-07 35 1 0 0
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2020-07-04 33 1 0 0
2020-06-26 32 1 0 0
2020-06-18 31 1 0 0
2020-06-03 30 1 0 0
2020-05-07 29 1 0 0
2020-05-01 28 1 0 0
2020-04-28 27 2 0 0
2020-04-25 25 2 0 0
2020-04-17 23 1 0 0
2020-04-07 22 8 0 0
2020-04-05 14 1 0 0
2020-04-04 13 1 0 0
2020-04-03 12 2 0 0
2020-04-02 10 3 0 0
2020-04-01 7 1 0 0
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2020-03-21 5 2 0 0
2020-03-19 3 1 0 0
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.ipynb)
  • 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: 0:02:53.391333