Metadata-Version: 1.1
Name: covsirphy
Version: 2.13.1
Summary: Package for COVID-19 analysis with phase-dependent SIR-derived ODE models
Home-page: https://github.com/lisphilar/covid19-sir
Author: Lisphilar
Author-email: lisphilar@outlook.jp
License: Apache License 2.0
Description: | |PyPI version| |Downloads| |PyPI - Python Version| |Build Status|
        | |GitHub license| |Maintainability| |Test Coverage|
        
        CovsirPhy introduction
        ======================
        
        `Documentation <https://lisphilar.github.io/covid19-sir/index.html>`__
        \|
        `Installation <https://lisphilar.github.io/covid19-sir/INSTALLATION.html>`__
        \| `Quickest
        usage <https://lisphilar.github.io/covid19-sir/usage_quickest.html>`__
        \| `API
        reference <https://lisphilar.github.io/covid19-sir/covsirphy.html>`__ \|
        `Qiita (Japanese) <https://qiita.com/tags/covsirphy>`__
        
        CovsirPhy is a Python package for COVID-19 (Coronavirus disease 2019)
        data analysis with phase-dependent SIR-derived ODE models. We can
        download datasets and analyse them easily. Scenario analysis with
        CovsirPhy enables us to make data-informed decisions. Please refer to
        "Method" part of `Kaggle Notebook: COVID-19 data with SIR
        model <https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model>`__
        to understand the methods.
        
        Functionalities
        ---------------
        
        -  Data preparation and data visualization
        -  `Phase setting with S-R Trend
           analysis <https://lisphilar.github.io/covid19-sir/usage_phases.html>`__
        -  Numerical simulation of ODE models
        
           -  Stable: SIR, SIR-D and SIR-F model
           -  Development: SIR-FV and SEWIR-F model
        
        -  Phase-dependent parameter estimation of ODE models
        -  Scenario analysis: Simulate the number of cases with user-defined
           parameter values
        -  (In development): Find the relationship of government response and
           parameter values
        
        Inspiration
        -----------
        
        -  Monitor the spread of COVID-19
        -  Keep track parameter values/reproduction number in each
           country/province
        -  Find the relationship of reproductive number and measures taken by
           each country
        
        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>`__
        are always welcomed. Please read `Guideline of
        contribution <https://lisphilar.github.io/covid19-sir/CONTRIBUTING.html>`__
        in advance.
        
        Installation
        ------------
        
        The latest stable version of CovsirPhy is available at `PyPI (The Python
        Package Index): covsirphy <https://pypi.org/project/covsirphy/>`__ and
        supports Python 3.6 or newer versions.
        
        ::
        
            pip install --upgrade covsirphy
        
        Development versions are in `GitHub repository:
        CovsirPhy <https://github.com/lisphilar/covid19-sir>`__.
        
        ::
        
            pip install --upgrade "git+https://github.com/lisphilar/covid19-sir.git#egg=covsirphy"
        
        Usage
        -----
        
        Quickest tour of CovsirPhy is here. The following codes analyze the
        records in Japan, but we can change the country name when creating
        ``Scenario`` class instance for your own analysis.
        
        .. code:: python
        
            import covsirphy as cs
            # Download and update datasets
            data_loader = cs.DataLoader("input")
            jhu_data = data_loader.jhu()
            population_data = data_loader.population()
            # Check records
            snl = cs.Scenario(jhu_data, population_data, country="Japan")
            snl.records()
            # S-R trend analysis
            snl.trend().summary()
            # Parameter estimation of SIR-F model
            snl.estimate(cs.SIRF)
            # History of reproduction number
            _ = snl.history(target="Rt")
            # History of parameters
            _ = snl.history_rate()
            _ = snl.history(target="rho")
            # Simulation for 30 days
            snl.add(days=30)
            _ = snl.simulate()
        
        Further information:
        
        -  `Quickest
           version <https://lisphilar.github.io/covid19-sir/usage_quickest.html>`__
        -  `Quick
           version <https://lisphilar.github.io/covid19-sir/usage_quick.html>`__
        -  `Details:
           datasets <https://lisphilar.github.io/covid19-sir/usage_dataset.html>`__
        -  `Details:
           phases <https://lisphilar.github.io/covid19-sir/usage_phases.html>`__
        -  `Details: theoretical
           datasets <https://lisphilar.github.io/covid19-sir/usage_theoretical.html>`__
        -  `Details: policy
           measures <https://lisphilar.github.io/covid19-sir/usage_policy.html>`__
        -  Example codes in `"example" directory of this
           repository <https://github.com/lisphilar/covid19-sir/tree/master/example>`__
        -  `Kaggle: COVID-19 data with SIR
           model <https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model>`__
        
        Support
        -------
        
        Please support this project as a developer (or a backer). |Become a
        backer|
        
        License: Apache License 2.0
        ---------------------------
        
        Please refer to
        `LICENSE <https://github.com/lisphilar/covid19-sir/blob/master/LICENSE>`__
        file.
        
        Citation
        --------
        
        We have no original papers the author and contributors wrote, but please
        cite this package as follows.
        
        CovsirPhy Development Team (2020), CovsirPhy, Python package for
        COVID-19 analysis with SIR-derived ODE models,
        https://github.com/lisphilar/covid19-sir
        
        If you want to use SIR-F/SIR-FV/SEWIR-F model, S-R trend analysis,
        phase-dependent approach to SIR-derived models, and other scientific
        method performed with CovsirPhy, please cite the next Kaggle notebook.
        
        Lisphilar (2020), Kaggle notebook, COVID-19 data with SIR model,
        https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model
        
        Related work
        ------------
        
        | Reproduction number evolution in each country:
        | Ilyass Tabiai and Houda Kaddioui (2020), GitHub pages, COVID19 R0
          tracker, https://ilylabs.github.io/projects/COVID-trackers/
        
        .. |PyPI version| image:: https://badge.fury.io/py/covsirphy.svg
           :target: https://badge.fury.io/py/covsirphy
        .. |Downloads| image:: https://pepy.tech/badge/covsirphy
           :target: https://pepy.tech/project/covsirphy
        .. |PyPI - Python Version| image:: https://img.shields.io/pypi/pyversions/covsirphy
           :target: https://badge.fury.io/py/covsirphy
        .. |Build Status| image:: https://semaphoreci.com/api/v1/lisphilar/covid19-sir/branches/master/shields_badge.svg
           :target: https://semaphoreci.com/lisphilar/covid19-sir
        .. |GitHub license| image:: https://img.shields.io/github/license/lisphilar/covid19-sir
           :target: https://github.com/lisphilar/covid19-sir/blob/master/LICENSE
        .. |Maintainability| image:: https://api.codeclimate.com/v1/badges/eb97eaf9804f436062b9/maintainability
           :target: https://codeclimate.com/github/lisphilar/covid19-sir/maintainability
        .. |Test Coverage| image:: https://api.codeclimate.com/v1/badges/eb97eaf9804f436062b9/test_coverage
           :target: https://codeclimate.com/github/lisphilar/covid19-sir/test_coverage
        .. |Become a backer| image:: https://opencollective.com/covsirphy/tiers/backer.svg?avatarHeight=36&width=600
           :target: https://opencollective.com/covsirphy
        
Keywords: covid19
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
