<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/main/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>
<img src="https://raw.githubusercontent.com/lisphilar/covid19-sir/main/docs/dfd/covsirphy_dfd.png" width="800" alt="Data flow diagram: data science for infectious diseases with CovsirPhy Python library">
## 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/markdown/INSTALLATION.html).
```Bash
pip install --upgrade covsirphy
```
## 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/main/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. The first release in PyPI (version 2.3.0) was on 28Jun2020. Many APIs were reviewed via 2.x series and version 3.0.0 was released on 12May2023.
## 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 (2020-2024), 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.**
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"description": "\n<img src=\"https://raw.githubusercontent.com/lisphilar/covid19-sir/main/docs/logo/covsirphy_headline.png\" width=\"390\" alt=\"CovsirPhy: COVID-19 analysis with phase-dependent SIRs\">\n\n[](https://badge.fury.io/py/covsirphy)\n[](https://pepy.tech/project/covsirphy)\n[](https://badge.fury.io/py/covsirphy)\n[](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE)\n[](https://github.com/lisphilar/covid19-sir/actions/workflows/test.yml)\n[](https://codecov.io/gh/lisphilar/covid19-sir)\n\n# CovsirPhy introduction\n\n[<strong>Documentation</strong>](https://lisphilar.github.io/covid19-sir/index.html)\n| [<strong>Installation</strong>](https://lisphilar.github.io/covid19-sir/markdown/INSTALLATION.html)\n| [<strong>Tutorial</strong>](<https://lisphilar.github.io/covid19-sir/01_data_preparation.html>)\n| [<strong>API reference</strong>](https://lisphilar.github.io/covid19-sir/covsirphy.html)\n| [<strong>GitHub</strong>](https://github.com/lisphilar/covid19-sir)\n| [<strong>Qiita (Japanese)</strong>](https://qiita.com/tags/covsirphy)\n\n<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>\n\n<img src=\"https://raw.githubusercontent.com/lisphilar/covid19-sir/main/docs/dfd/covsirphy_dfd.png\" width=\"800\" alt=\"Data flow diagram: data science for infectious diseases with CovsirPhy Python library\">\n\n## Inspiration\n\n* Monitor the spread of COVID-19/Monkeypox with SIR-derived ODE models\n* Predict the number of cases in each country/province\n* Find the relationship of reproductive number and measures taken by each country\n\n<strong>If you have ideas or need new functionalities, please join this project.\nAny 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>\n\n## Installation\n\nThe 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/markdown/INSTALLATION.html).\n\n```Bash\npip install --upgrade covsirphy\n```\n\n## Demo\n\nQuickest tour of CovsirPhy is here. The following codes analyze the records in Japan.\n\n```Python\nimport covsirphy as cs\n# Data preparation,time-series segmentation, parameter estimation with SIR-F model\nsnr = cs.ODEScenario.auto_build(geo=\"Japan\", model=cs.SIRFModel)\n# Check actual records\nsnr.simulate(name=None);\n# Show the result of time-series segmentation\nsnr.to_dynamics(name=\"Baseline\").detect();\n# Perform simulation with estimated ODE parameter values\nsnr.simulate(name=\"Baseline\");\n# Predict ODE parameter values (30 days from the last date of actual records)\nsnr.build_with_template(name=\"Predicted\", template=\"Baseline\");\nsnr.predict(days=30, name=\"Predicted\");\n# Perform simulation with estimated and predicted ODE parameter values\nsnr.simulate(name=\"Predicted\");\n# Add a future phase to the baseline (ODE parameters will not be changed)\nsnr.append();\n# Show created phases and ODE parameter values\nsnr.summary()\n# Compare reproduction number of scenarios (predicted/baseline)\nsnr.compare_param(\"Rt\");\n# Compare simulated number of cases\nsnr.compare_cases(\"Confirmed\");\n# Describe representative values\nsnr.describe()\n```\n\nOutput of `snr.simulate(name=\"Predicted\");`\n\n<img src=\"https://raw.githubusercontent.com/lisphilar/covid19-sir/main/example/output/demo_jpn/04_predicted.png\" width=\"600\">\n\n## Tutorial\n\nTutorials of functionalities are included in the [CovsirPhy documentation](https://lisphilar.github.io/covid19-sir/index.html).\n\n* [Data preparation](https://lisphilar.github.io/covid19-sir/01_data_preparation.html)\n* [Data Engineering](https://lisphilar.github.io/covid19-sir/02_data_engineering.html)\n* [SIR-derived ODE models](https://lisphilar.github.io/covid19-sir/03_ode.html)\n* [Phase-dependent SIR models](https://lisphilar.github.io/covid19-sir/04_phase_dependent.html)\n* [Scenario analysis](https://lisphilar.github.io/covid19-sir/05_scenario_analysis.html)\n* [ODE parameter prediction](https://lisphilar.github.io/covid19-sir/06_prediction.html)\n\n## Release notes\n\nRelease notes are [here](https://github.com/lisphilar/covid19-sir/releases). Titles & links of issues are listed with acknowledgement.\n\nWe 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).\n\n## Developers\n\nCovsirPhy library is developed by a community of volunteers. Please see the full list [here](https://github.com/lisphilar/covid19-sir/graphs/contributors).\n\nThis 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. The first release in PyPI (version 2.3.0) was on 28Jun2020. Many APIs were reviewed via 2.x series and version 3.0.0 was released on 12May2023.\n\n## Support\n\nPlease support this project as a developer (or a backer).\n[](https://opencollective.com/covsirphy)\n\n## License: Apache License 2.0\n\nPlease refer to [LICENSE](https://github.com/lisphilar/covid19-sir/blob/master/LICENSE) file.\n\n## Citation\n\nPlease cite this library as follows with version number (`import covsirphy as cs; cs.__version__`).\n\n**Hirokazu Takaya and CovsirPhy Development Team (2020-2024), 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)**\n\nThis is the output of `covsirphy.__citation__`.\n\n```Python\nimport covsirphy as cs\ncs.__citation__\n```\n\n**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.**\n",
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