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covsirphy-2.9.1
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مشاهده بیشتر
تبلیغات ما
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تبلیغات ما
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مشاهده بیشترتوضیحات
COVID-19 data analysis with phase-dependent SIR-derived ODE models
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | covsirphy-2.9.1 |
| نام | covsirphy |
| نسخه کتابخانه | 2.9.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Hirokazu Takaya |
| ایمیل نویسنده | lisphilar@outlook.jp |
| آدرس صفحه اصلی | https://github.com/lisphilar/covid19-sir/ |
| آدرس اینترنتی | https://pypi.org/project/covsirphy/ |
| مجوز | Apache-2.0 |
<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.**
نیازمندی
| مقدار | نام |
|---|---|
| >=1.23.3,<2.0.0 | numpy |
| >=3.0.1,<4.0.0 | optuna |
| >=1.5.0,<2.0.0 | pandas |
| >=9.0.0,<10.0.0 | pyarrow |
| >=0.8.10,<0.10.0 | tabulate |
| >=0.12.0,<0.13.0 | seaborn |
| >=1.8.1 | scipy |
| >=1.1.2,<2.0.0 | scikit-learn |
| >=0.0.21,<0.0.25 | japanmap |
| >=2.28.1,<3.0.0 | requests |
| >=1.1.7,<2.0.0 | ruptures |
| >=3.6.0,<4.0.0 | matplotlib |
| >=0.7.7,<0.8.0 | country-converter |
| >=0.3.0,<0.4.0 | wbdata |
| >=0.11.1,<0.12.0 | geopandas |
| >=1.3.4,<2.0.0 | Unidecode |
| >=3.3.2,<4.0.0 | lightgbm |
| >=0.5.0,<0.6.0 | AutoTS |
| >=1.4.0,<2.0.0 | p-tqdm |
| >=1.8.3,<2.0.0 | pca |
| >=0.3.3,<0.4.0 | better-exceptions |
| >=0.6.0,<0.7.0 | loguru |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<3.11 | Python |
نحوه نصب
نصب پکیج whl covsirphy-2.9.1:
pip install covsirphy-2.9.1.whl
نصب پکیج tar.gz covsirphy-2.9.1:
pip install covsirphy-2.9.1.tar.gz