1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
|
%global _empty_manifest_terminate_build 0
Name: python-covsirphy
Version: 2.28.0
Release: 1
Summary: COVID-19 data analysis with phase-dependent SIR-derived ODE models
License: Apache-2.0
URL: https://github.com/lisphilar/covid19-sir/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/49/f6/938c4848fbeb067c4e05b2e0d45a65736999f167e09d503ac638679f5e41/covsirphy-2.28.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-optuna
Requires: python3-pandas
Requires: python3-pyarrow
Requires: python3-tabulate
Requires: python3-seaborn
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-japanmap
Requires: python3-requests
Requires: python3-ruptures
Requires: python3-matplotlib
Requires: python3-country-converter
Requires: python3-wbdata
Requires: python3-geopandas
Requires: python3-Unidecode
Requires: python3-lightgbm
Requires: python3-AutoTS
Requires: python3-p-tqdm
Requires: python3-pca
Requires: python3-better-exceptions
Requires: python3-loguru
%description
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
[](https://badge.fury.io/py/covsirphy)
[](https://pepy.tech/project/covsirphy)
[](https://badge.fury.io/py/covsirphy)
[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
[](https://codecov.io/gh/lisphilar/covid19-sir)
# CovsirPhy introduction
[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
## Inspiration
* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
* Predict the number of cases in each country/province
* Find the relationship of reproductive number and measures taken by each country
<strong>If you have ideas or need new functionalities, please join this project.
Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
## Installation
The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
```Bash
pip install --upgrade covsirphy
```
> **Warning**
> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
## Demo
Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
```Python
import covsirphy as cs
# Data preparation,time-series segmentation, parameter estimation with SIR-F model
snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
# Check actual records
snr.simulate(name=None);
# Show the result of time-series segmentation
snr.to_dynamics(name="Baseline").detect();
# Perform simulation with estimated ODE parameter values
snr.simulate(name="Baseline");
# Predict ODE parameter values (30 days from the last date of actual records)
snr.build_with_template(name="Predicted", template="Baseline");
snr.predict(days=30, name="Predicted");
# Perform simulation with estimated and predicted ODE parameter values
snr.simulate(name="Predicted");
# Add a future phase to the baseline (ODE parameters will not be changed)
snr.append();
# Show created phases and ODE parameter values
snr.summary()
# Compare reproduction number of scenarios (predicted/baseline)
snr.compare_param("Rt");
# Compare simulated number of cases
snr.compare_cases("Confirmed");
# Describe representative values
snr.describe()
```
Output of `snr.simulate(name="Predicted");`
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
## Tutorial
Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
## Release notes
Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
## Developers
CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
## Support
Please support this project as a developer (or a backer).
[](https://opencollective.com/covsirphy)
## License: Apache License 2.0
Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
## Citation
Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
This is the output of `covsirphy.__citation__`.
```Python
import covsirphy as cs
cs.__citation__
```
**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
%package -n python3-covsirphy
Summary: COVID-19 data analysis with phase-dependent SIR-derived ODE models
Provides: python-covsirphy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-covsirphy
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
[](https://badge.fury.io/py/covsirphy)
[](https://pepy.tech/project/covsirphy)
[](https://badge.fury.io/py/covsirphy)
[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
[](https://codecov.io/gh/lisphilar/covid19-sir)
# CovsirPhy introduction
[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
## Inspiration
* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
* Predict the number of cases in each country/province
* Find the relationship of reproductive number and measures taken by each country
<strong>If you have ideas or need new functionalities, please join this project.
Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
## Installation
The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
```Bash
pip install --upgrade covsirphy
```
> **Warning**
> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
## Demo
Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
```Python
import covsirphy as cs
# Data preparation,time-series segmentation, parameter estimation with SIR-F model
snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
# Check actual records
snr.simulate(name=None);
# Show the result of time-series segmentation
snr.to_dynamics(name="Baseline").detect();
# Perform simulation with estimated ODE parameter values
snr.simulate(name="Baseline");
# Predict ODE parameter values (30 days from the last date of actual records)
snr.build_with_template(name="Predicted", template="Baseline");
snr.predict(days=30, name="Predicted");
# Perform simulation with estimated and predicted ODE parameter values
snr.simulate(name="Predicted");
# Add a future phase to the baseline (ODE parameters will not be changed)
snr.append();
# Show created phases and ODE parameter values
snr.summary()
# Compare reproduction number of scenarios (predicted/baseline)
snr.compare_param("Rt");
# Compare simulated number of cases
snr.compare_cases("Confirmed");
# Describe representative values
snr.describe()
```
Output of `snr.simulate(name="Predicted");`
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
## Tutorial
Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
## Release notes
Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
## Developers
CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
## Support
Please support this project as a developer (or a backer).
[](https://opencollective.com/covsirphy)
## License: Apache License 2.0
Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
## Citation
Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
This is the output of `covsirphy.__citation__`.
```Python
import covsirphy as cs
cs.__citation__
```
**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
%package help
Summary: Development documents and examples for covsirphy
Provides: python3-covsirphy-doc
%description help
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/docs/logo/covsirphy_headline.png" width="390" alt="CovsirPhy: COVID-19 analysis with phase-dependent SIRs">
[](https://badge.fury.io/py/covsirphy)
[](https://pepy.tech/project/covsirphy)
[](https://badge.fury.io/py/covsirphy)
[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)
[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)
[](https://codecov.io/gh/lisphilar/covid19-sir)
# CovsirPhy introduction
[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)
| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)
| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)
| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)
| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)
| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)
<strong>CovsirPhy is a Python library for infectious disease (COVID-19: Coronavirus disease 2019, Monkeypox 2022) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyze them easily. Scenario analysis with CovsirPhy enables us to make data-informed decisions. </strong>
## Inspiration
* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models
* Predict the number of cases in each country/province
* Find the relationship of reproductive number and measures taken by each country
<strong>If you have ideas or need new functionalities, please join this project.
Any suggestions with [Github Issues](https://github.com/lisphilar/covid19-sir/issues/new/choose) and [Twitter: @lisphilar](https://twitter.com/lisphilar) are always welcomed. Questions are also great. Please refer to [Guideline of contribution](https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html).</strong>
## Installation
The latest stable version of CovsirPhy is available at [PyPI (The Python Package Index): covsirphy](https://pypi.org/project/covsirphy/) and supports Python 3.8 or newer versions. Details are explained in [Documentation: Installation](https://lisphilar.github.io/covid19-sir/INSTALLATION.html).
```Bash
pip install --upgrade covsirphy
```
> **Warning**
> We cannot use `covsirphy` on Google Colab, which uses Python 3.7. [Binder](https://mybinder.org/) is recommended.
## Demo
Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan.
```Python
import covsirphy as cs
# Data preparation,time-series segmentation, parameter estimation with SIR-F model
snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
# Check actual records
snr.simulate(name=None);
# Show the result of time-series segmentation
snr.to_dynamics(name="Baseline").detect();
# Perform simulation with estimated ODE parameter values
snr.simulate(name="Baseline");
# Predict ODE parameter values (30 days from the last date of actual records)
snr.build_with_template(name="Predicted", template="Baseline");
snr.predict(days=30, name="Predicted");
# Perform simulation with estimated and predicted ODE parameter values
snr.simulate(name="Predicted");
# Add a future phase to the baseline (ODE parameters will not be changed)
snr.append();
# Show created phases and ODE parameter values
snr.summary()
# Compare reproduction number of scenarios (predicted/baseline)
snr.compare_param("Rt");
# Compare simulated number of cases
snr.compare_cases("Confirmed");
# Describe representative values
snr.describe()
```
Output of `snr.simulate(name="Predicted");`
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/master/example/output/demo_jpn/04_predicted.png" width="600">
## Tutorial
Tutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).
* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)
* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)
* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)
* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)
* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)
* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)
## Release notes
Release notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.
We can see the release plan for the next stable version in [milestone page of the GitHub repository](https://github.com/lisphilar/covid19-sir/milestones). If you find a highly urgent matter, please let us know via [issue page](https://github.com/lisphilar/covid19-sir/issues).
## Developers
CovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).
This project started in Kaggle platform. Hirokazu Takaya ([@lisphilar](<https://www.kaggle.com/lisphilar>)) published [Kaggle Notebook: COVID-19 data with SIR model](https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model) on 12Feb2020 and developed it, discussing with Kaggle community. On 07May2020, "covid19-sir" repository was created. On 10May2020, `covsirphy` version 1.0.0 was published in GitHub. First release in PyPI (version 2.3.0) was on 28Jun2020.
## Support
Please support this project as a developer (or a backer).
[](https://opencollective.com/covsirphy)
## License: Apache License 2.0
Please refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.
## Citation
Please cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).
**Hirokazu Takaya and CovsirPhy Development Team (2020-2022), CovsirPhy version [version number]: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models, [https://github.com/lisphilar/covid19-sir](https://github.com/lisphilar/covid19-sir)**
This is the output of `covsirphy.__citation__`.
```Python
import covsirphy as cs
cs.__citation__
```
**We have no original papers the author and contributors wrote, but note that some scientific approaches, including SIR-F model, S-R change point analysis, phase-dependent approach to SIR-derived models, were developed in this project.**
%prep
%autosetup -n covsirphy-2.28.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-covsirphy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.28.0-1
- Package Spec generated
|
